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+ "source_product": "COPERNICUS/S5P/OFFL/L3_NO2 tropospheric_NO2_column_number_density", + "temporal_statistic": "per-pixel maximum over the declared episode", + "answer_statistic": "maximum finite pixel of that episode-peak AOI raster", + "support_statistic": "95th percentile of finite AOI pixels", + "units": "umol_m2", + "rounding_decimals": 6 + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000024/questions/B.json b/data/source_assets/multi_hazard/air_pollution/000024/questions/B.json new file mode 100644 index 0000000000000000000000000000000000000000..1a39f83e47e58ab35dd2bf55a320fd7e1b0b3da4 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000024/questions/B.json @@ -0,0 +1,84 @@ +{ + "available_handles": [ + "s5p_no2__peak" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "TaskComplete" + ], + "case_id": "air_pollution__000024__B", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "class": "string", + "dominant_fraction": "float" + }, + "evidence": { + "dominant_class": "string", + "by_class": { + "q1_low": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "float" + }, + "q2_mid_low": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "float" + }, + "q3_mid_high": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "null" + }, + "q4_high": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "null" + } + }, + "analysis_scope": "string", + "mask_pixel_count": "null", + "classification_contract": "string" + } + }, + "family": "B", + "question_text": "What is the training-relative S5P NO2-column quartile class at the peak for Santiago pollution episode AOI 24?", + "sample_id": "air_pollution__000024", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "classification_contract": { + "version": "air_s5p_train_relative_quartiles_v2", + "source_handle": "s5p_no2__peak", + "fit_split": "train", + "fit_aoi_count": 18, + "fit_valid_pixel_count": 294912, + "intervals": { + "q1_low": [ + -1e+30, + 88.19622039794922 + ], + "q2_mid_low": [ + 88.19622039794922, + 121.34806823730469 + ], + "q3_mid_high": [ + 121.34806823730469, + 179.93792724609375 + ], + "q4_high": [ + 179.93792724609375, + 1e+30 + ] + }, + "interval_rule": "lower-inclusive, upper-exclusive", + "analysis_scope": "all finite AOI pixels", + "dominance": "maximum pixel count; ties use declared class order", + "interpretation": "benchmark-relative S5P NO2-column quartiles, not health or surface-concentration classes" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000024/questions/C.json b/data/source_assets/multi_hazard/air_pollution/000024/questions/C.json new file mode 100644 index 0000000000000000000000000000000000000000..b3fb679e9dba1e31640a0123f0d24035d4537edb --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000024/questions/C.json @@ -0,0 +1,104 @@ +{ + "available_handles": [ + "s5p_no2__peak", + "cams_no2__peak", + "modis_aod__peak" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "CompareSources", + "TaskComplete" + ], + "case_id": "air_pollution__000024__C", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "agree": "bool", + "metric": "string", + "agreement_fraction": "float", + "required_agreement_fraction": "float", + "source_intensity_states": { + "s5p_no2": "string", + "cams_no2": "string", + "modis_aod": "string" + }, + "source_train_z_scores": { + "s5p_no2": "float", + "cams_no2": "float", + "modis_aod": "float" + }, + "verdict": "string" + }, + "evidence": { + "agree": "bool", + "metric": "string", + "agreement_fraction": "float", + "required_agreement_fraction": "float", + "source_intensity_states": { + "s5p_no2": "string", + "cams_no2": "string", + "modis_aod": "string" + }, + "source_train_z_scores": { + "s5p_no2": "float", + "cams_no2": "float", + "modis_aod": "float" + }, + "verdict": "string", + "source_spatial_means": { + "s5p_no2": "float", + "cams_no2": "float", + "modis_aod": "float" + }, + "source_units": { + "s5p_no2": "string", + "cams_no2": "string", + "modis_aod": "string" + }, + "state_counts": { + "below_training_typical": "integer" + }, + "state_z_boundaries": "string", + "normalization_fit_split": "string", + "normalization_train_sample_ids": "string" + } + }, + "family": "C", + "question_text": "Do Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Santiago pollution episode AOI 24?", + "sample_id": "air_pollution__000024", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 5, + "tool_budget_min": 5, + "agreement_contract": { + "version": "air_pollution_train_relative_intensity_v2", + "source_handles": [ + "s5p_no2__peak", + "cams_no2__peak", + "modis_aod__peak" + ], + "source_products": { + "s5p_no2__peak": "Sentinel-5P tropospheric NO2 column episode peak", + "cams_no2__peak": "CAMS total-column NO2 episode peak", + "modis_aod__peak": "MODIS aerosol optical depth episode peak" + }, + "per_source_summary": "arithmetic mean of all finite AOI pixels", + "normalization": "(AOI mean - training AOI-mean mean) / training AOI-mean population std", + "fit_split": "train", + "fit_aoi_count": 18, + "state_boundaries_z": [ + -0.5, + 0.5 + ], + "states": [ + "below_training_typical", + "training_typical", + "above_training_typical" + ], + "agreement_fraction": "largest state count divided by three sources", + "required_agreement_fraction": 1.0, + "interpretation": "agreement of within-product relative episode intensity, not equality of physical units or pollutants" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000024/questions/D.json b/data/source_assets/multi_hazard/air_pollution/000024/questions/D.json new file mode 100644 index 0000000000000000000000000000000000000000..66f83b794167d4ffa860a262058a28ede6f519c7 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000024/questions/D.json @@ -0,0 +1,72 @@ +{ + "available_handles": [ + "high_no2_column" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "EstimateExposure", + "TaskComplete" + ], + "case_id": "air_pollution__000024__D", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "population_in_footprint": "float" + }, + "evidence": { + "population_within_footprint": "float", + "population_source": "string", + "footprint_area_ha": "float", + "critical_infrastructure": { + "hospitals": { + "in_footprint": "integer", + "in_buffer": "integer", + "nearest": { + "name": "null", + "distance_m": "null" + } + }, + "schools": { + "in_footprint": "integer", + "in_buffer": "integer", + 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people-per-pixel values in positive footprint cells", + "requested_assets": [ + "hospitals", + "schools" + ], + "asset_geometry": "one authoritative GeoJSON representative point per cached OSM feature", + "context_buffer_m": 5000, + "distance_method": "local azimuthal-equidistant distance to positive cell centers", + "zero_footprint_rule": "zero population/counts/area and null nearest values" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000024/rasters/cams_no2__peak.json b/data/source_assets/multi_hazard/air_pollution/000024/rasters/cams_no2__peak.json new file mode 100644 index 0000000000000000000000000000000000000000..f103713235fdabfa555570e4537a611825ada944 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000024/rasters/cams_no2__peak.json @@ -0,0 +1,104 @@ +{ + "band_or_field": "total_column_nitrogen_dioxide_surface", + "citation_url": "https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT", + "constant": false, + "crs": 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AOI 25?", + "sample_id": "air_pollution__000025", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "classification_contract": { + "version": "air_s5p_train_relative_quartiles_v2", + "source_handle": "s5p_no2__peak", + "fit_split": "train", + "fit_aoi_count": 18, + "fit_valid_pixel_count": 294912, + "intervals": { + "q1_low": [ + -1e+30, + 88.19622039794922 + ], + "q2_mid_low": [ + 88.19622039794922, + 121.34806823730469 + ], + "q3_mid_high": [ + 121.34806823730469, + 179.93792724609375 + ], + "q4_high": [ + 179.93792724609375, + 1e+30 + ] + }, + "interval_rule": "lower-inclusive, upper-exclusive", + "analysis_scope": "all finite AOI pixels", + "dominance": "maximum pixel count; ties use declared class order", + "interpretation": "benchmark-relative S5P NO2-column quartiles, not health or surface-concentration classes" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000025/questions/C.json b/data/source_assets/multi_hazard/air_pollution/000025/questions/C.json new file mode 100644 index 0000000000000000000000000000000000000000..3281c1041752ffb65e972f07f82a253277f0339e --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000025/questions/C.json @@ -0,0 +1,106 @@ +{ + "available_handles": [ + "s5p_no2__peak", + "cams_no2__peak", + "modis_aod__peak" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "CompareSources", + "TaskComplete" + ], + "case_id": "air_pollution__000025__C", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "agree": "bool", + "metric": "string", + "agreement_fraction": "float", + "required_agreement_fraction": "float", + "source_intensity_states": { + "s5p_no2": "string", + "cams_no2": "string", + "modis_aod": "string" + }, + "source_train_z_scores": { + "s5p_no2": "float", + "cams_no2": "float", + "modis_aod": 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for Delhi pollution episode AOI 25?", + "sample_id": "air_pollution__000025", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 5, + "tool_budget_min": 5, + "agreement_contract": { + "version": "air_pollution_train_relative_intensity_v2", + "source_handles": [ + "s5p_no2__peak", + "cams_no2__peak", + "modis_aod__peak" + ], + "source_products": { + "s5p_no2__peak": "Sentinel-5P tropospheric NO2 column episode peak", + "cams_no2__peak": "CAMS total-column NO2 episode peak", + "modis_aod__peak": "MODIS aerosol optical depth episode peak" + }, + "per_source_summary": "arithmetic mean of all finite AOI pixels", + "normalization": "(AOI mean - training AOI-mean mean) / training AOI-mean population std", + "fit_split": "train", + "fit_aoi_count": 18, + "state_boundaries_z": [ + -0.5, + 0.5 + ], + "states": [ + "below_training_typical", + "training_typical", + "above_training_typical" + ], + "agreement_fraction": 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"peak_contract": { + "version": "air_s5p_spatiotemporal_peak_v2", + "source_handle": "s5p_no2__peak", + "source_product": "COPERNICUS/S5P/OFFL/L3_NO2 tropospheric_NO2_column_number_density", + "temporal_statistic": "per-pixel maximum over the declared episode", + "answer_statistic": "maximum finite pixel of that episode-peak AOI raster", + "support_statistic": "95th percentile of finite AOI pixels", + "units": "umol_m2", + "rounding_decimals": 6 + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000026/questions/B.json b/data/source_assets/multi_hazard/air_pollution/000026/questions/B.json new file mode 100644 index 0000000000000000000000000000000000000000..4f4d34666d0c4760693dfc1718159cd0f72502fe --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000026/questions/B.json @@ -0,0 +1,84 @@ +{ + "available_handles": [ + "s5p_no2__peak" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "TaskComplete" + ], + "case_id": "air_pollution__000026__B", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "class": "string", + "dominant_fraction": "float" + }, + "evidence": { + "dominant_class": "string", + "by_class": { + "q1_low": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "null" + }, + "q2_mid_low": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "null" + }, + "q3_mid_high": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "null" + }, + "q4_high": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "float" + } + }, + "analysis_scope": "string", + "mask_pixel_count": "null", + "classification_contract": "string" + } + }, + "family": "B", + "question_text": "What is the training-relative S5P NO2-column quartile class at the peak for Los Angeles pollution episode AOI 26?", + "sample_id": "air_pollution__000026", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "classification_contract": { + "version": "air_s5p_train_relative_quartiles_v2", + "source_handle": "s5p_no2__peak", + "fit_split": "train", + "fit_aoi_count": 18, + "fit_valid_pixel_count": 294912, + "intervals": { + "q1_low": [ + -1e+30, + 88.19622039794922 + ], + "q2_mid_low": [ + 88.19622039794922, + 121.34806823730469 + ], + "q3_mid_high": [ + 121.34806823730469, + 179.93792724609375 + ], + "q4_high": [ + 179.93792724609375, + 1e+30 + ] + }, + "interval_rule": "lower-inclusive, upper-exclusive", + "analysis_scope": "all finite AOI pixels", + "dominance": "maximum pixel count; ties use declared class order", + "interpretation": "benchmark-relative S5P NO2-column quartiles, not health or surface-concentration classes" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000026/questions/C.json b/data/source_assets/multi_hazard/air_pollution/000026/questions/C.json new file mode 100644 index 0000000000000000000000000000000000000000..04dfb24d920c8d25c043493071e9703451faa56a --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000026/questions/C.json @@ -0,0 +1,105 @@ +{ + "available_handles": [ + "s5p_no2__peak", + "cams_no2__peak", + "modis_aod__peak" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "CompareSources", + "TaskComplete" + ], + "case_id": "air_pollution__000026__C", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "agree": "bool", + "metric": "string", + "agreement_fraction": "float", + "required_agreement_fraction": "float", + "source_intensity_states": { + "s5p_no2": "string", + "cams_no2": "string", + "modis_aod": "string" + }, + "source_train_z_scores": { + "s5p_no2": "float", + "cams_no2": "float", + "modis_aod": "float" + }, + "verdict": "string" + }, + "evidence": { + "agree": "bool", + "metric": "string", + "agreement_fraction": "float", + "required_agreement_fraction": "float", + "source_intensity_states": { + "s5p_no2": "string", + "cams_no2": "string", + "modis_aod": "string" + }, + "source_train_z_scores": { + "s5p_no2": "float", + "cams_no2": "float", + "modis_aod": "float" + }, + "verdict": "string", + "source_spatial_means": { + "s5p_no2": "float", + "cams_no2": "float", + "modis_aod": "float" + }, + "source_units": { + "s5p_no2": "string", + "cams_no2": "string", + "modis_aod": "string" + }, + "state_counts": { + "above_training_typical": "integer", + "below_training_typical": "integer" + }, + "state_z_boundaries": "string", + "normalization_fit_split": "string", + "normalization_train_sample_ids": "string" + } + }, + "family": "C", + "question_text": "Do Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Los Angeles pollution episode AOI 26?", + "sample_id": "air_pollution__000026", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 5, + "tool_budget_min": 5, + "agreement_contract": { + "version": "air_pollution_train_relative_intensity_v2", + "source_handles": [ + "s5p_no2__peak", + "cams_no2__peak", + "modis_aod__peak" + ], + "source_products": { + "s5p_no2__peak": "Sentinel-5P tropospheric NO2 column episode peak", + "cams_no2__peak": "CAMS total-column NO2 episode peak", + "modis_aod__peak": "MODIS aerosol optical depth episode peak" + }, + "per_source_summary": "arithmetic mean of all finite AOI pixels", + "normalization": "(AOI mean - training AOI-mean mean) / training AOI-mean population std", + "fit_split": "train", + "fit_aoi_count": 18, + "state_boundaries_z": [ + -0.5, + 0.5 + ], + "states": [ + "below_training_typical", + "training_typical", + "above_training_typical" + ], + "agreement_fraction": "largest state count divided by three sources", + "required_agreement_fraction": 1.0, + "interpretation": "agreement of within-product relative episode intensity, not equality of physical units or pollutants" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000026/questions/D.json b/data/source_assets/multi_hazard/air_pollution/000026/questions/D.json new file mode 100644 index 0000000000000000000000000000000000000000..cef746f8b394f8cb76551233f49970ac495a50bf --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000026/questions/D.json @@ -0,0 +1,72 @@ +{ + "available_handles": [ + "high_no2_column" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "EstimateExposure", + "TaskComplete" + ], + "case_id": "air_pollution__000026__D", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "population_in_footprint": "float" + }, + "evidence": { + "population_within_footprint": "float", + "population_source": "string", + "footprint_area_ha": "float", + "critical_infrastructure": { + "hospitals": { + "in_footprint": "integer", + "in_buffer": "integer", + "nearest": { + "name": "string", + "distance_m": "float" + } + }, + "schools": { + "in_footprint": "integer", + "in_buffer": "integer", + "nearest": { + "name": "string", + "distance_m": "float" + } + } + }, + "footprint_handle": "string", + "distance_method": "string", + "asset_geometry_contract": "string", + "threshold_contract": "string" + } + }, + "family": "D", + "question_text": "What population is located within the training-defined top-quartile S5P NO2-column footprint during Los Angeles pollution episode AOI 26?", + "sample_id": "air_pollution__000026", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 6, + "tool_budget_min": 6, + "exposure_contract": { + "version": "air_s5p_q4_population_exposure_v2", + "footprint_handle": "high_no2_column", + "footprint_derivation": "s5p_no2__peak >= training-pixel q75", + "threshold": 179.93792724609375, + "threshold_fit_split": "train", + "boundary_rule": "q4 lower bound is inclusive", + "population_source": "WorldPop/GP/100m/pop 2020 population-count clip", + "population_sum": "sum non-negative people-per-pixel values in positive footprint cells", + "requested_assets": [ + "hospitals", + "schools" + ], + "asset_geometry": "one authoritative GeoJSON representative point per cached OSM feature", + "context_buffer_m": 5000, + "distance_method": "local azimuthal-equidistant distance to positive cell centers", + "zero_footprint_rule": "zero population/counts/area and null nearest values" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000026/rasters/cams_no2__peak.json b/data/source_assets/multi_hazard/air_pollution/000026/rasters/cams_no2__peak.json new file mode 100644 index 0000000000000000000000000000000000000000..406df9c686b99bb8272344b2cd6664ea9d37da64 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000026/rasters/cams_no2__peak.json @@ -0,0 +1,104 @@ +{ + "band_or_field": "total_column_nitrogen_dioxide_surface", + "citation_url": "https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT", + "constant": false, + "crs": "EPSG:4326", + "derivation": "preserve physical units; no AOD substitution and no invented NO2 conversion", + "fetch_provenance": { + "output": { + "bytes": 48783, + "constant": false, + "export_grid": { + "crs": "EPSG:4326", + "height": 128, + "width": 128 + }, + "maximum": 2.0838626369368285e-05, + "mean": 2.063200736301951e-05, + "minimum": 2.0477906218729913e-05, + "native_resolution_m": 44528, + "path": "work/rebuilt/air_pollution/000026/rasters/cams_no2__peak.tif", + "resampling": "bilinear", + "sha256": "6bf0a98a0df26ab9a115414d3cc3391fead29958791d44bf51f51a737c3b6729", + "units": "kg m-2", + "valid_fraction": 1.0 + }, + "request": { + "aoi_geometry": { + "coordinates": [ + [ + [ + -118.38957790836211, + 33.93698706130854 + ], + [ + -118.22957790836212, + 33.93698706130854 + ], + [ + -118.22957790836212, + 34.09698706130854 + ], + [ + -118.38957790836211, + 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275.10699462890625, + "minimum": 60.180572509765625, + "modality": "no2_column_deviation", + "scale_m": 5500, + "schema_version": "geodisaster_derived_sidecar_v2", + "sha256": "63a0cce373c8535b1bb416f15596111e729ea29e5e64e78bd9c5fab30c593111", + "shape": [ + 128, + 128 + ], + "source": "derived from Sentinel-5P NO2 and train-only mean", + "source_mode": "provider_exact_rebuild", + "units": "umol_m2", + "valid_fraction": 1.0 +} diff --git a/data/source_assets/multi_hazard/air_pollution/000026/rasters/no2_anomaly.tif b/data/source_assets/multi_hazard/air_pollution/000026/rasters/no2_anomaly.tif new file mode 100644 index 0000000000000000000000000000000000000000..bc20d31fbf932dbb498890eb28b5ac461c73c4da Binary files /dev/null and b/data/source_assets/multi_hazard/air_pollution/000026/rasters/no2_anomaly.tif differ diff --git a/data/source_assets/multi_hazard/air_pollution/000026/rasters/s5p_co__peak.json b/data/source_assets/multi_hazard/air_pollution/000026/rasters/s5p_co__peak.json new 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"95th percentile of finite AOI pixels", + "units": "umol_m2", + "rounding_decimals": 6 + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000028/questions/B.json b/data/source_assets/multi_hazard/air_pollution/000028/questions/B.json new file mode 100644 index 0000000000000000000000000000000000000000..88a38d500fbbf54f9f0847bc79b85bd4a3b9c6e0 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000028/questions/B.json @@ -0,0 +1,84 @@ +{ + "available_handles": [ + "s5p_no2__peak" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "TaskComplete" + ], + "case_id": "air_pollution__000028__B", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "class": "string", + "dominant_fraction": "float" + }, + "evidence": { + "dominant_class": "string", + "by_class": { + "q1_low": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "float" + }, + "q2_mid_low": { + "area_ha": "float", + 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depth episode peak" + }, + "per_source_summary": "arithmetic mean of all finite AOI pixels", + "normalization": "(AOI mean - training AOI-mean mean) / training AOI-mean population std", + "fit_split": "train", + "fit_aoi_count": 18, + "state_boundaries_z": [ + -0.5, + 0.5 + ], + "states": [ + "below_training_typical", + "training_typical", + "above_training_typical" + ], + "agreement_fraction": "largest state count divided by three sources", + "required_agreement_fraction": 1.0, + "interpretation": "agreement of within-product relative episode intensity, not equality of physical units or pollutants" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000028/questions/D.json b/data/source_assets/multi_hazard/air_pollution/000028/questions/D.json new file mode 100644 index 0000000000000000000000000000000000000000..006291e019b51a43342db5ef7039d50d9f16a4dc --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000028/questions/D.json @@ -0,0 +1,72 @@ +{ + 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positive cell centers", + "zero_footprint_rule": "zero population/counts/area and null nearest values" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000028/rasters/cams_no2__peak.json b/data/source_assets/multi_hazard/air_pollution/000028/rasters/cams_no2__peak.json new file mode 100644 index 0000000000000000000000000000000000000000..6c4908529ed21ede096bfec95423afe3977cd5fc --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000028/rasters/cams_no2__peak.json @@ -0,0 +1,104 @@ +{ + "band_or_field": "total_column_nitrogen_dioxide_surface", + "citation_url": "https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT", + "constant": false, + "crs": "EPSG:4326", + "derivation": "preserve physical units; no AOD substitution and no invented NO2 conversion", + "fetch_provenance": { + "output": { + "bytes": 50173, + "constant": false, + "export_grid": { + "crs": "EPSG:4326", + "height": 128, + "width": 128 + }, + "maximum": 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b/data/source_assets/multi_hazard/air_pollution/000028/tabular/openaq_stations.csv new file mode 100644 index 0000000000000000000000000000000000000000..68035889a31b8a51a5cdb1dcc930a439f1c73f27 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000028/tabular/openaq_stations.csv @@ -0,0 +1 @@ +station_id,location_id,sensor_id,name,provider,country,no2_mean_ug_m3,no2_max_ug_m3,n_measurements,window_match,datetime_first_utc,datetime_last_utc,latitude,longitude,row,col,distance_m,source diff --git a/data/source_assets/multi_hazard/air_pollution/000028/tabular/openaq_status.json b/data/source_assets/multi_hazard/air_pollution/000028/tabular/openaq_status.json new file mode 100644 index 0000000000000000000000000000000000000000..5062ea411f8f8019c768d605a0f355c898878415 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000028/tabular/openaq_status.json @@ -0,0 +1,12 @@ +{ + "api_key_used": true, + "candidate_locations": 10, + "csv_path": "samples/air_pollution/sample_000028/tabular/openaq_stations.csv", + "source": "OpenAQ v3", + "stations_written": 0, + "status": "completed_no_window_measurements", + "target_window": [ + "2024-11-07", + "2024-11-14" + ] +} diff --git a/data/source_assets/multi_hazard/air_pollution/000028/tabular/worldpop.json b/data/source_assets/multi_hazard/air_pollution/000028/tabular/worldpop.json new file mode 100644 index 0000000000000000000000000000000000000000..fd94722a1c109da9b8ffa7131f0d53e20d6ce21c --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000028/tabular/worldpop.json @@ -0,0 +1,37 @@ +{ + "constant": false, + "download": { + "download_crs": "EPSG:4326", + "download_shape": [ + 128, + 128 + ], + "download_transform": [ + 0.0012499999999999734, + 0.0, + -70.82144620284132, + 0.0, + -0.0012499999999999734, + -33.42021208899437, + 0.0, + 0.0, + 1.0 + ] + }, + "maximum": 238.68923950195312, + "minimum": 1.449155569076538, + "provider": "WorldPop", + "resampling": 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new file mode 100644 index 0000000000000000000000000000000000000000..c513c89e60a12122788bca195237a4cf8c81e5a4 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000029/questions/A.json @@ -0,0 +1,41 @@ +{ + "available_handles": [ + "s5p_no2__peak" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ComputeStats", + "TaskComplete" + ], + "case_id": "air_pollution__000029__A", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "value": "float", + "units": "string" + }, + "evidence": { + "peak_no2": "float", + "p95_no2": "float" + } + }, + "family": "A", + "question_text": "What is the peak Sentinel-5P NO2 column during Delhi pollution episode AOI 29?", + "sample_id": "air_pollution__000029", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "peak_contract": { + "version": "air_s5p_spatiotemporal_peak_v2", + "source_handle": "s5p_no2__peak", + "source_product": "COPERNICUS/S5P/OFFL/L3_NO2 tropospheric_NO2_column_number_density", + "temporal_statistic": "per-pixel maximum over the declared episode", + "answer_statistic": "maximum finite pixel of that episode-peak AOI raster", + "support_statistic": "95th percentile of finite AOI pixels", + "units": "umol_m2", + "rounding_decimals": 6 + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000029/questions/B.json b/data/source_assets/multi_hazard/air_pollution/000029/questions/B.json new file mode 100644 index 0000000000000000000000000000000000000000..c5e8fabc6d30b4d93d6480bcbdfdf2bb0e096818 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000029/questions/B.json @@ -0,0 +1,84 @@ +{ + "available_handles": [ + "s5p_no2__peak" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "TaskComplete" + ], + "case_id": "air_pollution__000029__B", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "class": "string", + "dominant_fraction": "float" + }, + "evidence": { + "dominant_class": "string", + "by_class": { + "q1_low": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "float" + }, + "q2_mid_low": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "float" + }, + "q3_mid_high": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "float" + }, + "q4_high": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "null" + } + }, + "analysis_scope": "string", + "mask_pixel_count": "null", + "classification_contract": "string" + } + }, + "family": "B", + "question_text": "What is the training-relative S5P NO2-column quartile class at the peak for Delhi pollution episode AOI 29?", + "sample_id": "air_pollution__000029", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "classification_contract": { + "version": "air_s5p_train_relative_quartiles_v2", + "source_handle": "s5p_no2__peak", + "fit_split": "train", + "fit_aoi_count": 18, + "fit_valid_pixel_count": 294912, + "intervals": { + "q1_low": [ + -1e+30, + 88.19622039794922 + ], + "q2_mid_low": [ + 88.19622039794922, + 121.34806823730469 + ], + "q3_mid_high": [ + 121.34806823730469, + 179.93792724609375 + ], + "q4_high": [ + 179.93792724609375, + 1e+30 + ] + }, + "interval_rule": "lower-inclusive, upper-exclusive", + "analysis_scope": "all finite AOI pixels", + "dominance": "maximum pixel count; ties use declared class order", + "interpretation": "benchmark-relative S5P NO2-column quartiles, not health or surface-concentration classes" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000029/questions/C.json b/data/source_assets/multi_hazard/air_pollution/000029/questions/C.json new file mode 100644 index 0000000000000000000000000000000000000000..c86ddb8b9bd2eb9d725b82bf11c4151fd3970e9f --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000029/questions/C.json @@ -0,0 +1,106 @@ +{ + "available_handles": [ + "s5p_no2__peak", + "cams_no2__peak", + "modis_aod__peak" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "CompareSources", + "TaskComplete" + ], + "case_id": "air_pollution__000029__C", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "agree": "bool", + "metric": "string", + "agreement_fraction": "float", + "required_agreement_fraction": "float", + "source_intensity_states": { + "s5p_no2": "string", + "cams_no2": "string", + "modis_aod": "string" + }, + "source_train_z_scores": { + "s5p_no2": "float", + "cams_no2": "float", + "modis_aod": "float" + }, + "verdict": "string" + }, + "evidence": { + "agree": "bool", + "metric": "string", + "agreement_fraction": "float", + "required_agreement_fraction": "float", + "source_intensity_states": { + "s5p_no2": "string", + "cams_no2": "string", + "modis_aod": "string" + }, + "source_train_z_scores": { + "s5p_no2": "float", + "cams_no2": "float", + "modis_aod": "float" + }, + "verdict": "string", + "source_spatial_means": { + "s5p_no2": "float", + "cams_no2": "float", + "modis_aod": "float" + }, + "source_units": { + "s5p_no2": "string", + "cams_no2": "string", + "modis_aod": "string" + }, + "state_counts": { + "above_training_typical": "integer", + "below_training_typical": "integer", + "training_typical": "integer" + }, + "state_z_boundaries": "string", + "normalization_fit_split": "string", + "normalization_train_sample_ids": "string" + } + }, + "family": "C", + "question_text": "Do Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Delhi pollution episode AOI 29?", + "sample_id": "air_pollution__000029", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 5, + "tool_budget_min": 5, + "agreement_contract": { + "version": "air_pollution_train_relative_intensity_v2", + "source_handles": [ + "s5p_no2__peak", + "cams_no2__peak", + "modis_aod__peak" + ], + "source_products": { + "s5p_no2__peak": "Sentinel-5P tropospheric NO2 column episode peak", + "cams_no2__peak": "CAMS total-column NO2 episode peak", + "modis_aod__peak": "MODIS aerosol optical depth episode peak" + }, + "per_source_summary": "arithmetic mean of all finite AOI pixels", + "normalization": "(AOI mean - training AOI-mean mean) / training AOI-mean population std", + "fit_split": "train", + "fit_aoi_count": 18, + "state_boundaries_z": [ + -0.5, + 0.5 + ], + "states": [ + "below_training_typical", + "training_typical", + "above_training_typical" + ], + "agreement_fraction": "largest state count divided by three sources", + "required_agreement_fraction": 1.0, + "interpretation": "agreement of within-product relative episode intensity, not equality of physical units or pollutants" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000029/questions/D.json b/data/source_assets/multi_hazard/air_pollution/000029/questions/D.json new file mode 100644 index 0000000000000000000000000000000000000000..5d44a0385c61effd757d27fe4ba881d7135babd1 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000029/questions/D.json @@ -0,0 +1,72 @@ +{ + "available_handles": [ + "high_no2_column" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "EstimateExposure", + "TaskComplete" + ], + "case_id": "air_pollution__000029__D", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "population_in_footprint": "float" + }, + "evidence": { + "population_within_footprint": "float", + "population_source": "string", + "footprint_area_ha": "float", + "critical_infrastructure": { + "hospitals": { + "in_footprint": "integer", + "in_buffer": "integer", + 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inclusive", + "population_source": "WorldPop/GP/100m/pop 2020 population-count clip", + "population_sum": "sum non-negative people-per-pixel values in positive footprint cells", + "requested_assets": [ + "hospitals", + "schools" + ], + "asset_geometry": "one authoritative GeoJSON representative point per cached OSM feature", + "context_buffer_m": 5000, + "distance_method": "local azimuthal-equidistant distance to positive cell centers", + "zero_footprint_rule": "zero population/counts/area and null nearest values" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000029/rasters/cams_no2__peak.json b/data/source_assets/multi_hazard/air_pollution/000029/rasters/cams_no2__peak.json new file mode 100644 index 0000000000000000000000000000000000000000..6b98603e8ffeeab36337a9be9adea80b1183815f --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000029/rasters/cams_no2__peak.json @@ -0,0 +1,104 @@ +{ + "band_or_field": "total_column_nitrogen_dioxide_surface", + 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NO2 column during Los Angeles pollution episode AOI 30?", + "sample_id": "air_pollution__000030", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "peak_contract": { + "version": "air_s5p_spatiotemporal_peak_v2", + "source_handle": "s5p_no2__peak", + "source_product": "COPERNICUS/S5P/OFFL/L3_NO2 tropospheric_NO2_column_number_density", + "temporal_statistic": "per-pixel maximum over the declared episode", + "answer_statistic": "maximum finite pixel of that episode-peak AOI raster", + "support_statistic": "95th percentile of finite AOI pixels", + "units": "umol_m2", + "rounding_decimals": 6 + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000030/questions/B.json b/data/source_assets/multi_hazard/air_pollution/000030/questions/B.json new file mode 100644 index 0000000000000000000000000000000000000000..31603ae4e83afdc0c397ec4480b6bc2c92f4e316 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000030/questions/B.json @@ -0,0 +1,84 @@ +{ + "available_handles": [ + "s5p_no2__peak" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "TaskComplete" + ], + "case_id": "air_pollution__000030__B", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "class": "string", + "dominant_fraction": "float" + }, + "evidence": { + "dominant_class": "string", + "by_class": { + "q1_low": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "float" + }, + "q2_mid_low": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "float" + }, + "q3_mid_high": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "float" + }, + "q4_high": { + "area_ha": "float", + "pixel_count": "integer", + "mean_value": "float" + } + }, + "analysis_scope": "string", + "mask_pixel_count": "null", + "classification_contract": "string" + } + }, + "family": "B", + "question_text": "What is the training-relative S5P NO2-column quartile class at the peak for Los Angeles pollution episode AOI 30?", + "sample_id": "air_pollution__000030", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "classification_contract": { + "version": "air_s5p_train_relative_quartiles_v2", + "source_handle": "s5p_no2__peak", + "fit_split": "train", + "fit_aoi_count": 18, + "fit_valid_pixel_count": 294912, + "intervals": { + "q1_low": [ + -1e+30, + 88.19622039794922 + ], + "q2_mid_low": [ + 88.19622039794922, + 121.34806823730469 + ], + "q3_mid_high": [ + 121.34806823730469, + 179.93792724609375 + ], + "q4_high": [ + 179.93792724609375, + 1e+30 + ] + }, + "interval_rule": "lower-inclusive, upper-exclusive", + "analysis_scope": "all finite AOI pixels", + "dominance": "maximum pixel count; ties use declared class order", + "interpretation": "benchmark-relative S5P NO2-column quartiles, not health or surface-concentration classes" + } +} diff --git a/data/source_assets/multi_hazard/air_pollution/000030/questions/C.json b/data/source_assets/multi_hazard/air_pollution/000030/questions/C.json new file mode 100644 index 0000000000000000000000000000000000000000..0d0ff8aa5efb3775d6db52d7acc72441fb974645 --- /dev/null +++ b/data/source_assets/multi_hazard/air_pollution/000030/questions/C.json @@ -0,0 +1,106 @@ +{ + "available_handles": [ + "s5p_no2__peak", + "cams_no2__peak", + "modis_aod__peak" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "CompareSources", + "TaskComplete" + ], + "case_id": "air_pollution__000030__C", + "domain": "air_pollution", + "expected_answer_shape": { + "status": "string", + "answer": { + "agree": "bool", + "metric": "string", + "agreement_fraction": "float", + "required_agreement_fraction": "float", + "source_intensity_states": { + "s5p_no2": "string", + "cams_no2": "string", + "modis_aod": "string" + }, + 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Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Los Angeles pollution episode AOI 30?", + "sample_id": "air_pollution__000030", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 5, + "tool_budget_min": 5, + "agreement_contract": { + "version": "air_pollution_train_relative_intensity_v2", + "source_handles": [ + "s5p_no2__peak", + "cams_no2__peak", + "modis_aod__peak" + ], + "source_products": { + "s5p_no2__peak": "Sentinel-5P tropospheric NO2 column episode peak", + "cams_no2__peak": "CAMS total-column NO2 episode peak", + "modis_aod__peak": "MODIS aerosol optical depth episode peak" + }, + "per_source_summary": "arithmetic mean of all finite AOI pixels", + "normalization": "(AOI mean - training AOI-mean mean) / training AOI-mean population std", + "fit_split": "train", + "fit_aoi_count": 18, + "state_boundaries_z": [ + -0.5, + 0.5 + ], + "states": [ + 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0000000000000000000000000000000000000000..f7c64a01ae766af9df4a1b5ed7c75c52eeeabd56 --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000001/questions/A.json @@ -0,0 +1,41 @@ +{ + "available_handles": [ + "hansen_loss__event" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ComputeStats", + "TaskComplete" + ], + "case_id": "deforestation__000001__A", + "domain": "deforestation", + "expected_answer_shape": { + "answer": { + "units": "string", + "value": "float" + }, + "evidence": { + "primary_area_ha": "float", + "primary_handle": "string" + }, + "status": "string" + }, + "family": "A", + "question_text": "Using the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.", + "sample_id": "deforestation__000001", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "measurement_contract": { + "version": "hansen_2022_binary_grid_area_v1", + "source_handle": "hansen_loss__event", + "source_product": "UMD Hansen Global Forest Change 2025 v1.13 loss-year band", + "event_year": 2022, + "mask_values": "finite binary 0=no 2022 loss, 1=loss year 2022", + "nodata_rule": "exclude declared nodata and non-finite pixels", + "area_rule": "WGS84 ellipsoidal cell area for geographic grids; affine determinant for projected grids", + "rounding": "three decimals in hectares" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000001/questions/B.json b/data/source_assets/multi_hazard/deforestation/000001/questions/B.json new file mode 100644 index 0000000000000000000000000000000000000000..63d491d2155dad1852dcc95e695055f20854739b --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000001/questions/B.json @@ -0,0 +1,84 @@ +{ + "available_handles": [ + "hansen_loss__event", + "ndvi_change" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "TaskComplete" + ], + "case_id": "deforestation__000001__B", + "domain": "deforestation", + "expected_answer_shape": { + "answer": { + "class": "string", + "dominant_fraction": "float" + }, + "evidence": { + "analysis_scope": "string", + "by_class": { + "major": { + "area_ha": "float", + "mean_value": "float", + "pixel_count": "int" + }, + "minor": { + "area_ha": "float", + "mean_value": "float", + "pixel_count": "int" + }, + "none": { + "area_ha": "float", + "mean_value": "float", + "pixel_count": "int" + }, + "severe": { + "area_ha": "float", + "mean_value": "null", + "pixel_count": "int" + } + }, + "dominant_class": "string", + "mask_pixel_count": "int" + }, + "status": "string" + }, + "family": "B", + "question_text": "Inside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.", + "sample_id": "deforestation__000001", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "classification_contract": { + "version": "hansen_masked_s2_ndvi_drop_v1", + "value_handle": "ndvi_change", + "value_formula": "Sentinel-2 NDVI pre minus post", + "mask_handle": "hansen_loss__event", + "analysis_population": "finite NDVI-change pixels where the finite Hansen mask equals 1", + "intervals": { + "none": [ + -10.0, + 0.15 + ], + "minor": [ + 0.15, + 0.3 + ], + "major": [ + 0.3, + 0.5 + ], + "severe": [ + 0.5, + 10.0 + ] + }, + "interval_boundary": "lower inclusive, upper exclusive", + "dominance": "largest pixel count", + "tie_break": "first class in none, minor, major, severe order", + "area_rule": "grid-aware area of class pixels" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000001/questions/C.json b/data/source_assets/multi_hazard/deforestation/000001/questions/C.json new file mode 100644 index 0000000000000000000000000000000000000000..544b8587304137c7b4b10a9e2501298736ebc69d --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000001/questions/C.json @@ -0,0 +1,87 @@ +{ + "available_handles": [ + "hansen_loss__event", + "tmf_loss__event", + "radd_alerts__event" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "CompareSources", + "TaskComplete" + ], + "case_id": "deforestation__000001__C", + "domain": "deforestation", + "expected_answer_shape": { + "status": "string", + "answer": { + "agree": "bool", + "metric": "string", + "median_area_ha": "float", + "median_pct_delta": "float", + "verdict": "string", + "agreement_score_0_100": "float" + }, + "evidence": { + "agree": "bool", + "metric": "string", + "median_area_ha": "float", + "median_pct_delta": "float", + "verdict": "string", + "agreement_score_0_100": "float", + "score_contract": "string", + "per_source_area_ha": { + "hansen_loss": "float", + "tmf_loss": "float", + "radd_alerts": "float" + }, + "pairwise_pct_delta": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + }, + "pairwise_iou": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + }, + "pairwise_state_agreement": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + }, + "pairwise_categorical_kappa": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + } + } + }, + "family": "C", + "question_text": "Compare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.", + "sample_id": "deforestation__000001", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 5, + "tool_budget_min": 5, + "agreement_contract": { + "version": "deforestation_area_agreement_v2", + "source_handles": [ + "hansen_loss__event", + "tmf_loss__event", + "radd_alerts__event" + ], + "source_masks": { + "hansen_loss__event": "finite binary 1 where loss year equals 2022", + "tmf_loss__event": "finite binary 1 where Dec2022 is deforested and Dec2021 is not", + "radd_alerts__event": "0=no event-window alert, 1=low confidence, 2=high confidence; values >0 are alert" + }, + "area_rule": "grid-aware WGS84 ellipsoidal or projected affine-determinant cell areas", + "pairwise_delta_formula": "abs(A-B) / ((A+B)/2) * 100; 0 when A=B=0", + "aggregate": "median of three unrounded pairwise percent differences", + "agreement_rule": "median_pct_delta <= 25.0", + "score_rule": "max(0, 100 - median_pct_delta)", + "spatial_diagnostics": "common-valid cells on exactly co-registered grids; diagnostics do not alter the area verdict", + "rounding": "compute from unrounded areas; emit area/percent/score to three decimals" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000001/questions/D.json b/data/source_assets/multi_hazard/deforestation/000001/questions/D.json new file mode 100644 index 0000000000000000000000000000000000000000..cf292815e7b7f03ec1f63c9d7b94b7052c29c025 --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000001/questions/D.json @@ -0,0 +1,59 @@ +{ + "available_handles": [ + "hansen_loss__event" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "EstimateExposure", + "TaskComplete" + ], + "case_id": "deforestation__000001__D", + "domain": "deforestation", + "expected_answer_shape": { + "status": "string", + "answer": { + "nearest_highway_representative_distance_m": "float", + "availability_status": "string" + }, + "evidence": { + "population_within_footprint": "float", + "population_source": "string", + "footprint_area_ha": "float", + "critical_infrastructure": { + "highways": { + "in_footprint": "integer", + "in_buffer": "integer", + "nearest": { + "name": "string", + "distance_m": "float" + } + } + }, + "footprint_handle": "string", + "distance_method": "string", + "asset_geometry_contract": "string" + } + }, + "family": "D", + "question_text": "Using the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.", + "sample_id": "deforestation__000001", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 6, + "tool_budget_min": 6, + "exposure_contract": { + "version": "deforestation_nearest_highway_representative_v2", + "footprint_handle": "hansen_loss__event", + "asset_category": "highways", + "asset_unit": "one representative point per distinct cached OSM feature", + "coordinate_authority": "GeoJSON geometry in EPSG:4326; row/col properties are non-authoritative", + "distance_method": "local_azimuthal_equidistant_cell_center_v1", + "context_buffer_m": 5000, + "context_buffer_boundary": "inclusive", + "no_cached_feature_rule": "null distance/name, zero counts, availability_status=no_cached_highway_representative", + "in_footprint_rule": "transform representative geometry to raster CRS and locate it with the raster affine transform", + "population_rule": "sum non-negative WorldPop people-per-pixel values on the exactly co-registered footprint grid" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000001/rasters/forest_mask__pre.json b/data/source_assets/multi_hazard/deforestation/000001/rasters/forest_mask__pre.json new file mode 100644 index 0000000000000000000000000000000000000000..12980a9e1becbf7a7be219c9fecb5b491eb675e5 --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000001/rasters/forest_mask__pre.json @@ -0,0 +1,23 @@ +{ + "acquisition_dates": [ + "2000" + ], + "clear_fraction_in_aoi": null, + "fetched_at_build_time": "2026-05-17T04:45:18Z", + "geotiff_path": "source_assets/multi_hazard/deforestation/000001/rasters/forest_mask__pre.tif", + "handle": "forest_mask__pre", + "modality": "forest_mask", + "n_bands": 1, + "nan_pct": 0.0, + "raw_nan_pct_before_gapfill": 0.0, + "scale_m": 30, + "scene_coverage": 1.0, + "sha256": "637cfa45f64f9cd8463ba8ce6166d25429507ef1797bab3a1a6618abce175dba", + "shape": [ + 128, + 128 + ], + "source": "UMD/hansen/global_forest_change_2025_v1_13 treecover2000>30", + "source_mode": "gee_live", + "units": "binary" +} diff --git a/data/source_assets/multi_hazard/deforestation/000001/rasters/forest_mask__pre.tif b/data/source_assets/multi_hazard/deforestation/000001/rasters/forest_mask__pre.tif new file mode 100644 index 0000000000000000000000000000000000000000..abb89c52a1f5655c32e02ca167de168fe4880d68 Binary files /dev/null and b/data/source_assets/multi_hazard/deforestation/000001/rasters/forest_mask__pre.tif differ diff --git a/data/source_assets/multi_hazard/deforestation/000001/rasters/hansen_loss__event.json b/data/source_assets/multi_hazard/deforestation/000001/rasters/hansen_loss__event.json new file mode 100644 index 0000000000000000000000000000000000000000..b29443e539b4e8fc26639e72af5584ac06a8e4f3 --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000001/rasters/hansen_loss__event.json @@ -0,0 +1,25 @@ +{ + "acquisition_dates": [ + "2022" + ], + "clear_fraction_in_aoi": null, + "fetched_at_build_time": "2026-05-17T04:45:06Z", + "gap_fill_method": "masked binary pixels filled as 0 from the live product mask", + "gap_fill_value": 0.0, + "geotiff_path": "source_assets/multi_hazard/deforestation/000001/rasters/hansen_loss__event.tif", + "handle": "hansen_loss__event", + "modality": "forest_loss_mask", + "n_bands": 1, + "nan_pct": 0.0, + "raw_nan_pct_before_gapfill": 59.942626953125, + "scale_m": 30, + "scene_coverage": 1.0, + "sha256": "498d9a51a0f5dca553a746a8fe7bdca00a85ff7d76e9bada2d9dd5deb2dceecc", + "shape": [ + 128, + 128 + ], + "source": "UMD/hansen/global_forest_change_2025_v1_13 lossyear=2022", + "source_mode": "gee_live", + "units": "binary" +} diff --git a/data/source_assets/multi_hazard/deforestation/000001/rasters/hansen_loss__event.tif b/data/source_assets/multi_hazard/deforestation/000001/rasters/hansen_loss__event.tif new file mode 100644 index 0000000000000000000000000000000000000000..1a5e3e89de1badd2dd297184dce95ae1ff467617 Binary files /dev/null and b/data/source_assets/multi_hazard/deforestation/000001/rasters/hansen_loss__event.tif differ diff --git a/data/source_assets/multi_hazard/deforestation/000001/rasters/ndvi_change.json b/data/source_assets/multi_hazard/deforestation/000001/rasters/ndvi_change.json new file mode 100644 index 0000000000000000000000000000000000000000..249286642ffd5ad235cdbf9bf3e98b1d6d7dea4a --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000001/rasters/ndvi_change.json @@ -0,0 +1,24 @@ +{ + "constant": false, + "derived_from": [ + "s2_ndvi__pre", + "s2_ndvi__post" + ], + "formula": "s2_ndvi_pre - s2_ndvi_post", + "geotiff_path": "source_assets/multi_hazard/deforestation/000001/rasters/ndvi_change.tif", + "handle": "ndvi_change", + "maximum": 0.6755224466323853, + "minimum": -0.7482339143753052, + "modality": "s2_ndvi_change", + "scale_m": 10, + "schema_version": "geodisaster_derived_sidecar_v2", + "sha256": "4d08c882cb8af2402fe53b0c713389b8589ec1a36eb1ffc95910a4b376f6d113", + "shape": [ + 128, + 128 + ], + "source": "derived from Sentinel-2 NDVI", + "source_mode": "provider_exact_rebuild", + "units": "index", + "valid_fraction": 1.0 +} diff --git a/data/source_assets/multi_hazard/deforestation/000001/rasters/ndvi_change.tif b/data/source_assets/multi_hazard/deforestation/000001/rasters/ndvi_change.tif new file mode 100644 index 0000000000000000000000000000000000000000..3df3e8d6543e50de83628959fc383690d35dde51 Binary files /dev/null and b/data/source_assets/multi_hazard/deforestation/000001/rasters/ndvi_change.tif differ diff --git a/data/source_assets/multi_hazard/deforestation/000001/rasters/radd_alerts__event.json b/data/source_assets/multi_hazard/deforestation/000001/rasters/radd_alerts__event.json new file mode 100644 index 0000000000000000000000000000000000000000..46595f0d5611f5ec2ba28cfea31e6e3d4a6d64cd --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000001/rasters/radd_alerts__event.json @@ -0,0 +1,97 @@ +{ + "band_or_field": "date_conf", + "citation_url": "https://www.wur.nl/en/research/products-services/radd-forest-disturbance-alert", + "constant": false, + "crs": "EPSG:4326", + "derivation": "decode date_conf; 2xxxx=low confidence, 3xxxx=high confidence; preserve confidence as 1/2 and write a binary event-window mask separately", + "fetch_provenance": { + "output": { + "bytes": 1634, + "constant": false, + "export_grid": { + "crs": "EPSG:4326", + "height": 128, + "width": 128 + }, + "maximum": 2.0, + "mean": 0.01654052734375, + "minimum": 0.0, + "native_resolution_m": 10, + "path": 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+ "modality": "radd_alert_confidence", + "n_bands": 1, + "nan_pct": 0.0, + "native_resolution_m": 10, + "nodata": -9999.0, + "product": "RADD Forest Disturbance Alerts v1", + "provider": "Wageningen University & Research / Global Forest Watch", + "scale_m": 10, + "schema_version": "geodisaster_source_sidecar_v2", + "sha256": "adb4218f6dac1af9550456ea52a6c6bc85c03cbf93dd339845a3258c2e6226b6", + "shape": [ + 128, + 128 + ], + "source": "Wageningen University & Research / Global Forest Watch — RADD Forest Disturbance Alerts v1", + "source_mode": "provider_exact_rebuild", + "temporal_rule": "retain low- and high-confidence alerts whose first-alert date is within each AOI start/end window", + "transform": [ + 0.0012499999999999734, + 0.0, + -52.355143740961736, + 0.0, + -0.0012500000000000011, + -3.3341795206881075 + ], + "units": "encoded alert date and confidence class", + "upstream_identifier": "projects/radar-wur/raddalert/v1; GFW date_conf GeoTIFF tiles", + "valid_fraction": 1.0 +} 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false, + "crs": "EPSG:4326", + "derivation": "decode date_conf; 2xxxx=low confidence, 3xxxx=high confidence; preserve confidence as 1/2 and write a binary event-window mask separately", + "fetch_provenance": { + "output": { + "bytes": 1587, + "constant": false, + "export_grid": { + "crs": "EPSG:4326", + "height": 128, + "width": 128 + }, + "maximum": 1.0, + "mean": 0.00830078125, + "minimum": 0.0, + "native_resolution_m": 10, + "path": "work/rebuilt/deforestation/000001/rasters/radd_alerts__event_binary.tif", + "resampling": "max", + "sha256": "b948fc0b53def32b73a92ef24384bbaf63d591275700984dd452ffd9c62fc6a7", + "units": "binary", + "valid_fraction": 1.0 + }, + "request": { + "aoi_geometry": { + "coordinates": [ + [ + [ + -52.355143740961736, + -3.4941795206881077 + ], + [ + -52.19514374096174, + -3.4941795206881077 + ], + [ + -52.19514374096174, + -3.3341795206881075 + ], + [ + -52.355143740961736, + -3.3341795206881075 + ], + [ + -52.355143740961736, + -3.4941795206881077 + ] + ] + ], + "type": "Polygon" + }, + "end_date": "2022-09-30", + "start_date": "2022-06-01" + }, + "upstream": { + "dataset": "wur_radd_alerts", + "pixel_meaning": "date_conf", + "tile_id": "00N_060W", + "version": "v20261011" + } + }, + "geotiff_path": "source_assets/multi_hazard/deforestation/000001/rasters/radd_alerts__event_binary.tif", + "handle": "radd_alerts__event_binary", + "license": "CC BY 4.0", + "maximum": 1.0, + "minimum": 0.0, + "modality": "radd_event_window_mask", + "n_bands": 1, + "nan_pct": 0.0, + "native_resolution_m": 10, + "nodata": -9999.0, + "product": "RADD Forest Disturbance Alerts v1", + "provider": "Wageningen University & Research / Global Forest Watch", + "scale_m": 10, + "schema_version": "geodisaster_source_sidecar_v2", + "sha256": "b948fc0b53def32b73a92ef24384bbaf63d591275700984dd452ffd9c62fc6a7", + "shape": [ + 128, + 128 + ], + "source": "Wageningen University & Research / Global Forest Watch — RADD Forest Disturbance Alerts v1", + "source_mode": 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b/data/source_assets/multi_hazard/deforestation/000001/rasters/tmf_loss__event.json new file mode 100644 index 0000000000000000000000000000000000000000..4ea08f36adbd7656502cc2dfce12468679e7c41a --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000001/rasters/tmf_loss__event.json @@ -0,0 +1,46 @@ +{ + "bands": [ + "Dec2021", + "Dec2022" + ], + "citation_url": "https://forobs.jrc.ec.europa.eu/TMF/data", + "constant": false, + "download": { + "download_crs": "EPSG:4326", + "download_shape": [ + 128, + 128 + ], + "download_transform": [ + 0.0012499999999999734, + 0.0, + -52.355143740961736, + 0.0, + -0.0012500000000000011, + -3.3341795206881075, + 0.0, + 0.0, + 1.0 + ] + }, + "event_year": 2022, + "formula": "Dec2022 == deforested(3) and Dec2021 != deforested(3)", + "geotiff_path": "source_assets/multi_hazard/deforestation/000001/rasters/tmf_loss__event.tif", + "handle": "tmf_loss__event", + "maximum": 1.0, + "minimum": 0.0, + "modality": "annual_forest_loss_mask", + 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0000000000000000000000000000000000000000..495449dba28ae93fa468dca0c3267230ad96e794 --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000002/quality_report.json @@ -0,0 +1,20 @@ +{ + "all_sources_ok": true, + "c05_exact_source_overlay": true, + "c05_overlay_completed_at": "2026-10-14T11:58:47Z", + "completed_at": "2026-05-17T04:45:18Z", + "derived_completed_at": "2026-10-14T12:03:58Z", + "derived_ok": true, + "domain": "deforestation", + "gapfilled_layers": [ + "hansen_loss__event" + ], + "nan_pct_max": 0.0, + "panels_completed_at": "2026-10-14T12:04:01Z", + "panels_ok": true, + "raw_nan_pct_max_before_gapfill": 57.220458984375, + "sample_id": "sample_000002", + "scene_coverage_min": 1.0, + "source_count": 6, + "source_mode": "provider_exact_rebuild" +} diff --git a/data/source_assets/multi_hazard/deforestation/000002/questions/A.json b/data/source_assets/multi_hazard/deforestation/000002/questions/A.json new file mode 100644 index 0000000000000000000000000000000000000000..acf7946e28059a2350a69b91366ae9f86d0fcd0b --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000002/questions/A.json @@ -0,0 +1,41 @@ +{ + "available_handles": [ + "hansen_loss__event" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ComputeStats", + "TaskComplete" + ], + "case_id": "deforestation__000002__A", + "domain": "deforestation", + "expected_answer_shape": { + "answer": { + "units": "string", + "value": "float" + }, + "evidence": { + "primary_area_ha": "float", + "primary_handle": "string" + }, + "status": "string" + }, + "family": "A", + "question_text": "Using the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.", + "sample_id": "deforestation__000002", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "measurement_contract": { + "version": "hansen_2022_binary_grid_area_v1", + "source_handle": "hansen_loss__event", + "source_product": "UMD Hansen Global Forest Change 2025 v1.13 loss-year band", + "event_year": 2022, + "mask_values": "finite binary 0=no 2022 loss, 1=loss year 2022", + "nodata_rule": "exclude declared nodata and non-finite pixels", + "area_rule": "WGS84 ellipsoidal cell area for geographic grids; affine determinant for projected grids", + "rounding": "three decimals in hectares" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000002/questions/B.json b/data/source_assets/multi_hazard/deforestation/000002/questions/B.json new file mode 100644 index 0000000000000000000000000000000000000000..d19cc66cc1fd4d99adbeb867e80edba79f1e2362 --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000002/questions/B.json @@ -0,0 +1,84 @@ +{ + "available_handles": [ + "hansen_loss__event", + "ndvi_change" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "TaskComplete" + ], + "case_id": "deforestation__000002__B", + "domain": "deforestation", + "expected_answer_shape": { + "answer": { + "class": "string", + "dominant_fraction": "float" + }, + "evidence": { + "analysis_scope": "string", + "by_class": { + "major": { + "area_ha": "float", + "mean_value": "float", + "pixel_count": "int" + }, + "minor": { + "area_ha": "float", + "mean_value": "float", + "pixel_count": "int" + }, + "none": { + "area_ha": "float", + "mean_value": "float", + "pixel_count": "int" + }, + "severe": { + "area_ha": "float", + "mean_value": "null", + "pixel_count": "int" + } + }, + "dominant_class": "string", + "mask_pixel_count": "int" + }, + "status": "string" + }, + "family": "B", + "question_text": "Inside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.", + "sample_id": "deforestation__000002", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "classification_contract": { + "version": "hansen_masked_s2_ndvi_drop_v1", + "value_handle": "ndvi_change", + "value_formula": "Sentinel-2 NDVI pre minus post", + "mask_handle": "hansen_loss__event", + "analysis_population": "finite NDVI-change pixels where the finite Hansen mask equals 1", + "intervals": { + "none": [ + -10.0, + 0.15 + ], + "minor": [ + 0.15, + 0.3 + ], + "major": [ + 0.3, + 0.5 + ], + "severe": [ + 0.5, + 10.0 + ] + }, + "interval_boundary": "lower inclusive, upper exclusive", + "dominance": "largest pixel count", + "tie_break": "first class in none, minor, major, severe order", + "area_rule": "grid-aware area of class pixels" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000002/questions/C.json b/data/source_assets/multi_hazard/deforestation/000002/questions/C.json new file mode 100644 index 0000000000000000000000000000000000000000..476560ae5d933bd36d34a53bc9eaaca8658b0d8b --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000002/questions/C.json @@ -0,0 +1,87 @@ +{ + "available_handles": [ + "hansen_loss__event", + "tmf_loss__event", + "radd_alerts__event" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "CompareSources", + "TaskComplete" + ], + "case_id": "deforestation__000002__C", + "domain": "deforestation", + "expected_answer_shape": { + "status": "string", + "answer": { + "agree": "bool", + "metric": "string", + "median_area_ha": "float", + "median_pct_delta": "float", + "verdict": "string", + "agreement_score_0_100": "float" + }, + "evidence": { + "agree": "bool", + "metric": "string", + "median_area_ha": "float", + "median_pct_delta": "float", + "verdict": "string", + "agreement_score_0_100": "float", + "score_contract": "string", + "per_source_area_ha": { + "hansen_loss": "float", + "tmf_loss": "float", + "radd_alerts": "float" + }, + "pairwise_pct_delta": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + }, + "pairwise_iou": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + }, + "pairwise_state_agreement": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + }, + "pairwise_categorical_kappa": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + } + } + }, + "family": "C", + "question_text": "Compare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.", + "sample_id": "deforestation__000002", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 5, + "tool_budget_min": 5, + "agreement_contract": { + "version": "deforestation_area_agreement_v2", + "source_handles": [ + "hansen_loss__event", + "tmf_loss__event", + "radd_alerts__event" + ], + "source_masks": { + "hansen_loss__event": "finite binary 1 where loss year equals 2022", + "tmf_loss__event": "finite binary 1 where Dec2022 is deforested and Dec2021 is not", + "radd_alerts__event": "0=no event-window alert, 1=low confidence, 2=high confidence; values >0 are alert" + }, + "area_rule": "grid-aware WGS84 ellipsoidal or projected affine-determinant cell areas", + "pairwise_delta_formula": "abs(A-B) / ((A+B)/2) * 100; 0 when A=B=0", + "aggregate": "median of three unrounded pairwise percent differences", + "agreement_rule": "median_pct_delta <= 25.0", + "score_rule": "max(0, 100 - median_pct_delta)", + "spatial_diagnostics": "common-valid cells on exactly co-registered grids; diagnostics do not alter the area verdict", + "rounding": "compute from unrounded areas; emit area/percent/score to three decimals" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000002/questions/D.json b/data/source_assets/multi_hazard/deforestation/000002/questions/D.json new file mode 100644 index 0000000000000000000000000000000000000000..1d0820c51f5f282820e71304416b6e6251ba84bc --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000002/questions/D.json @@ -0,0 +1,59 @@ +{ + "available_handles": [ + "hansen_loss__event" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "EstimateExposure", + "TaskComplete" + ], + "case_id": "deforestation__000002__D", + "domain": "deforestation", + "expected_answer_shape": { + "status": "string", + "answer": { + "nearest_highway_representative_distance_m": "float", + "availability_status": "string" + }, + "evidence": { + "population_within_footprint": "float", + "population_source": "string", + "footprint_area_ha": "float", + "critical_infrastructure": { + "highways": { + "in_footprint": "integer", + "in_buffer": "integer", + "nearest": { + "name": "string", + "distance_m": "float" + } + } + }, + "footprint_handle": "string", + "distance_method": "string", + "asset_geometry_contract": "string" + } + }, + "family": "D", + "question_text": "Using the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.", + "sample_id": "deforestation__000002", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 6, + "tool_budget_min": 6, + "exposure_contract": { + "version": "deforestation_nearest_highway_representative_v2", + "footprint_handle": "hansen_loss__event", + "asset_category": "highways", + "asset_unit": "one representative point per distinct cached OSM feature", + "coordinate_authority": "GeoJSON geometry in EPSG:4326; row/col properties are non-authoritative", + "distance_method": "local_azimuthal_equidistant_cell_center_v1", + "context_buffer_m": 5000, + "context_buffer_boundary": "inclusive", + "no_cached_feature_rule": "null distance/name, zero counts, availability_status=no_cached_highway_representative", + "in_footprint_rule": "transform representative geometry to raster CRS and locate it with the raster affine transform", + "population_rule": "sum non-negative WorldPop people-per-pixel values on the exactly co-registered footprint grid" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000002/rasters/forest_mask__pre.json b/data/source_assets/multi_hazard/deforestation/000002/rasters/forest_mask__pre.json new file mode 100644 index 0000000000000000000000000000000000000000..7d42ed94904d0ea831a635c16ff4804c9167b882 --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000002/rasters/forest_mask__pre.json @@ -0,0 +1,23 @@ +{ + "acquisition_dates": [ + "2000" + ], + "clear_fraction_in_aoi": null, + "fetched_at_build_time": "2026-05-17T04:45:18Z", + "geotiff_path": "source_assets/multi_hazard/deforestation/000002/rasters/forest_mask__pre.tif", + "handle": "forest_mask__pre", + "modality": "forest_mask", + "n_bands": 1, + "nan_pct": 0.0, + "raw_nan_pct_before_gapfill": 0.0, + "scale_m": 30, + "scene_coverage": 1.0, + "sha256": "5a811feba45a3953e9005c3a2787654b07352a086c0a66bfc173758ff37462e3", + "shape": [ + 128, + 128 + ], + "source": "UMD/hansen/global_forest_change_2025_v1_13 treecover2000>30", + "source_mode": "gee_live", + "units": "binary" +} diff --git a/data/source_assets/multi_hazard/deforestation/000002/rasters/forest_mask__pre.tif b/data/source_assets/multi_hazard/deforestation/000002/rasters/forest_mask__pre.tif new file mode 100644 index 0000000000000000000000000000000000000000..dc5b236a9e16c47d36e641cb8b4db3a998a9c53f Binary files /dev/null and b/data/source_assets/multi_hazard/deforestation/000002/rasters/forest_mask__pre.tif differ diff --git a/data/source_assets/multi_hazard/deforestation/000002/rasters/hansen_loss__event.json b/data/source_assets/multi_hazard/deforestation/000002/rasters/hansen_loss__event.json new file mode 100644 index 0000000000000000000000000000000000000000..cd0f1d8c5b51adc4e53c9bd67224256d45a7e870 --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000002/rasters/hansen_loss__event.json @@ -0,0 +1,25 @@ +{ + "acquisition_dates": [ + "2022" + ], + "clear_fraction_in_aoi": null, + "fetched_at_build_time": "2026-05-17T04:45:03Z", + "gap_fill_method": "masked binary pixels filled as 0 from the live product mask", + "gap_fill_value": 0.0, + "geotiff_path": "source_assets/multi_hazard/deforestation/000002/rasters/hansen_loss__event.tif", + "handle": "hansen_loss__event", + "modality": "forest_loss_mask", + "n_bands": 1, + "nan_pct": 0.0, + "raw_nan_pct_before_gapfill": 57.220458984375, + "scale_m": 30, + "scene_coverage": 1.0, + "sha256": "8816e07480f510f98f39b5fd6baeee0703be3ad9e50da24a7f5bd346f01c0a22", + "shape": [ + 128, + 128 + ], + "source": "UMD/hansen/global_forest_change_2025_v1_13 lossyear=2022", + "source_mode": "gee_live", + "units": "binary" +} diff --git a/data/source_assets/multi_hazard/deforestation/000002/rasters/hansen_loss__event.tif b/data/source_assets/multi_hazard/deforestation/000002/rasters/hansen_loss__event.tif new file mode 100644 index 0000000000000000000000000000000000000000..eb2e198e16718089aabe2ec4b4065a4a1ddf997d Binary files /dev/null and b/data/source_assets/multi_hazard/deforestation/000002/rasters/hansen_loss__event.tif differ diff --git a/data/source_assets/multi_hazard/deforestation/000002/rasters/ndvi_change.json b/data/source_assets/multi_hazard/deforestation/000002/rasters/ndvi_change.json new file mode 100644 index 0000000000000000000000000000000000000000..0b59d9110eea3ac46ef45bbb7021f38ee535f4d6 --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000002/rasters/ndvi_change.json @@ -0,0 +1,24 @@ +{ + "constant": false, + "derived_from": [ + "s2_ndvi__pre", + "s2_ndvi__post" + ], + "formula": "s2_ndvi_pre - s2_ndvi_post", + "geotiff_path": 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"derived_completed_at": "2026-10-14T12:03:58Z", + "derived_ok": true, + "domain": "deforestation", + "gapfilled_layers": [ + "hansen_loss__event" + ], + "nan_pct_max": 0.0, + "panels_completed_at": "2026-10-14T12:04:01Z", + "panels_ok": true, + "raw_nan_pct_max_before_gapfill": 44.82421875, + "sample_id": "sample_000003", + "scene_coverage_min": 1.0, + "source_count": 6, + "source_mode": "provider_exact_rebuild" +} diff --git a/data/source_assets/multi_hazard/deforestation/000003/questions/A.json b/data/source_assets/multi_hazard/deforestation/000003/questions/A.json new file mode 100644 index 0000000000000000000000000000000000000000..5c5eab833f15af13ec5f471b6ee660d95213236a --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000003/questions/A.json @@ -0,0 +1,41 @@ +{ + "available_handles": [ + "hansen_loss__event" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ComputeStats", + "TaskComplete" + ], + "case_id": "deforestation__000003__A", + "domain": "deforestation", + "expected_answer_shape": { + "answer": { + "units": "string", + "value": "float" + }, + "evidence": { + "primary_area_ha": "float", + "primary_handle": "string" + }, + "status": "string" + }, + "family": "A", + "question_text": "Using the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.", + "sample_id": "deforestation__000003", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "measurement_contract": { + "version": "hansen_2022_binary_grid_area_v1", + "source_handle": "hansen_loss__event", + "source_product": "UMD Hansen Global Forest Change 2025 v1.13 loss-year band", + "event_year": 2022, + "mask_values": "finite binary 0=no 2022 loss, 1=loss year 2022", + "nodata_rule": "exclude declared nodata and non-finite pixels", + "area_rule": "WGS84 ellipsoidal cell area for geographic grids; affine determinant for projected grids", + "rounding": "three decimals in hectares" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000003/questions/B.json b/data/source_assets/multi_hazard/deforestation/000003/questions/B.json new file mode 100644 index 0000000000000000000000000000000000000000..a04f9b631461f57ec09061e3c7069044f37f950f --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000003/questions/B.json @@ -0,0 +1,84 @@ +{ + "available_handles": [ + "hansen_loss__event", + "ndvi_change" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "TaskComplete" + ], + "case_id": "deforestation__000003__B", + "domain": "deforestation", + "expected_answer_shape": { + "answer": { + "class": "string", + "dominant_fraction": "float" + }, + "evidence": { + "analysis_scope": "string", + "by_class": { + "major": { + "area_ha": "float", + "mean_value": "float", + "pixel_count": "int" + }, + "minor": { + "area_ha": "float", + "mean_value": "float", + "pixel_count": "int" + }, + "none": { + "area_ha": "float", + "mean_value": "float", + "pixel_count": "int" + }, + "severe": { + "area_ha": "float", + "mean_value": "float", + "pixel_count": "int" + } + }, + "dominant_class": "string", + "mask_pixel_count": "int" + }, + "status": "string" + }, + "family": "B", + "question_text": "Inside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.", + "sample_id": "deforestation__000003", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 4, + "tool_budget_min": 4, + "classification_contract": { + "version": "hansen_masked_s2_ndvi_drop_v1", + "value_handle": "ndvi_change", + "value_formula": "Sentinel-2 NDVI pre minus post", + "mask_handle": "hansen_loss__event", + "analysis_population": "finite NDVI-change pixels where the finite Hansen mask equals 1", + "intervals": { + "none": [ + -10.0, + 0.15 + ], + "minor": [ + 0.15, + 0.3 + ], + "major": [ + 0.3, + 0.5 + ], + "severe": [ + 0.5, + 10.0 + ] + }, + "interval_boundary": "lower inclusive, upper exclusive", + "dominance": "largest pixel count", + "tie_break": "first class in none, minor, major, severe order", + "area_rule": "grid-aware area of class pixels" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000003/questions/C.json b/data/source_assets/multi_hazard/deforestation/000003/questions/C.json new file mode 100644 index 0000000000000000000000000000000000000000..4132c0d408ea55ff051bc730856fec7dc7a8c58e --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000003/questions/C.json @@ -0,0 +1,87 @@ +{ + "available_handles": [ + "hansen_loss__event", + "tmf_loss__event", + "radd_alerts__event" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "CompareSources", + "TaskComplete" + ], + "case_id": "deforestation__000003__C", + "domain": "deforestation", + "expected_answer_shape": { + "status": "string", + "answer": { + "agree": "bool", + "metric": "string", + "median_area_ha": "float", + "median_pct_delta": "float", + "verdict": "string", + "agreement_score_0_100": "float" + }, + "evidence": { + "agree": "bool", + "metric": "string", + "median_area_ha": "float", + "median_pct_delta": "float", + "verdict": "string", + "agreement_score_0_100": "float", + "score_contract": "string", + "per_source_area_ha": { + "hansen_loss": "float", + "tmf_loss": "float", + "radd_alerts": "float" + }, + "pairwise_pct_delta": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + }, + "pairwise_iou": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + }, + "pairwise_state_agreement": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + }, + "pairwise_categorical_kappa": { + "hansen_loss__tmf_loss": "float", + "hansen_loss__radd_alerts": "float", + "tmf_loss__radd_alerts": "float" + } + } + }, + "family": "C", + "question_text": "Compare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.", + "sample_id": "deforestation__000003", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 5, + "tool_budget_min": 5, + "agreement_contract": { + "version": "deforestation_area_agreement_v2", + "source_handles": [ + "hansen_loss__event", + "tmf_loss__event", + "radd_alerts__event" + ], + "source_masks": { + "hansen_loss__event": "finite binary 1 where loss year equals 2022", + "tmf_loss__event": "finite binary 1 where Dec2022 is deforested and Dec2021 is not", + "radd_alerts__event": "0=no event-window alert, 1=low confidence, 2=high confidence; values >0 are alert" + }, + "area_rule": "grid-aware WGS84 ellipsoidal or projected affine-determinant cell areas", + "pairwise_delta_formula": "abs(A-B) / ((A+B)/2) * 100; 0 when A=B=0", + "aggregate": "median of three unrounded pairwise percent differences", + "agreement_rule": "median_pct_delta <= 25.0", + "score_rule": "max(0, 100 - median_pct_delta)", + "spatial_diagnostics": "common-valid cells on exactly co-registered grids; diagnostics do not alter the area verdict", + "rounding": "compute from unrounded areas; emit area/percent/score to three decimals" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000003/questions/D.json b/data/source_assets/multi_hazard/deforestation/000003/questions/D.json new file mode 100644 index 0000000000000000000000000000000000000000..0ac7d8f784c543a1d37c1c65a01b8c2d514ea076 --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000003/questions/D.json @@ -0,0 +1,59 @@ +{ + "available_handles": [ + "hansen_loss__event" + ], + "available_tools": [ + "ReadData", + "PrepareScene", + "ScoreHazard", + "EstimateExposure", + "TaskComplete" + ], + "case_id": "deforestation__000003__D", + "domain": "deforestation", + "expected_answer_shape": { + "status": "string", + "answer": { + "nearest_highway_representative_distance_m": "float", + "availability_status": "string" + }, + "evidence": { + "population_within_footprint": "float", + "population_source": "string", + "footprint_area_ha": "float", + "critical_infrastructure": { + "highways": { + "in_footprint": "integer", + "in_buffer": "integer", + "nearest": { + "name": "string", + "distance_m": "float" + } + } + }, + "footprint_handle": "string", + "distance_method": "string", + "asset_geometry_contract": "string" + } + }, + "family": "D", + "question_text": "Using the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.", + "sample_id": "deforestation__000003", + "schema_version": "geodisaster_multi_hazard_question_v2", + "source_mode": "provider_exact_rebuild", + "tool_budget_max": 6, + "tool_budget_min": 6, + "exposure_contract": { + "version": "deforestation_nearest_highway_representative_v2", + "footprint_handle": "hansen_loss__event", + "asset_category": "highways", + "asset_unit": "one representative point per distinct cached OSM feature", + "coordinate_authority": "GeoJSON geometry in EPSG:4326; row/col properties are non-authoritative", + "distance_method": "local_azimuthal_equidistant_cell_center_v1", + "context_buffer_m": 5000, + "context_buffer_boundary": "inclusive", + "no_cached_feature_rule": "null distance/name, zero counts, availability_status=no_cached_highway_representative", + "in_footprint_rule": "transform representative geometry to raster CRS and locate it with the raster affine transform", + "population_rule": "sum non-negative WorldPop people-per-pixel values on the exactly co-registered footprint grid" + } +} diff --git a/data/source_assets/multi_hazard/deforestation/000003/rasters/forest_mask__pre.json b/data/source_assets/multi_hazard/deforestation/000003/rasters/forest_mask__pre.json new file mode 100644 index 0000000000000000000000000000000000000000..a7d455a5c4d713c9fd3c9c3f7a919cd006b66878 --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000003/rasters/forest_mask__pre.json @@ -0,0 +1,23 @@ +{ + "acquisition_dates": [ + "2000" + ], + "clear_fraction_in_aoi": null, + "fetched_at_build_time": "2026-05-17T04:45:20Z", + "geotiff_path": "source_assets/multi_hazard/deforestation/000003/rasters/forest_mask__pre.tif", + "handle": "forest_mask__pre", + "modality": "forest_mask", + "n_bands": 1, + "nan_pct": 0.0, + "raw_nan_pct_before_gapfill": 0.0, + "scale_m": 30, + "scene_coverage": 1.0, + "sha256": "59dba2175df87c2a773257e486ae5b1cf57667d5b0fe3a78485e13ca28ebd087", + "shape": [ + 128, + 128 + ], + "source": "UMD/hansen/global_forest_change_2025_v1_13 treecover2000>30", + "source_mode": "gee_live", + "units": "binary" +} diff --git a/data/source_assets/multi_hazard/deforestation/000003/rasters/forest_mask__pre.tif b/data/source_assets/multi_hazard/deforestation/000003/rasters/forest_mask__pre.tif new file mode 100644 index 0000000000000000000000000000000000000000..814788dd61dbd2131d8134c26f0e9cbe02288b3d Binary files /dev/null and b/data/source_assets/multi_hazard/deforestation/000003/rasters/forest_mask__pre.tif differ diff --git a/data/source_assets/multi_hazard/deforestation/000003/rasters/hansen_loss__event.json b/data/source_assets/multi_hazard/deforestation/000003/rasters/hansen_loss__event.json new file mode 100644 index 0000000000000000000000000000000000000000..713dce3ce2f308d389e8021d7a64463b696e85ba --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000003/rasters/hansen_loss__event.json @@ -0,0 +1,25 @@ +{ + "acquisition_dates": [ + "2022" + ], + "clear_fraction_in_aoi": null, + "fetched_at_build_time": "2026-05-17T04:45:06Z", + "gap_fill_method": "masked binary pixels filled as 0 from the live product mask", + "gap_fill_value": 0.0, + "geotiff_path": "source_assets/multi_hazard/deforestation/000003/rasters/hansen_loss__event.tif", + "handle": "hansen_loss__event", + "modality": "forest_loss_mask", + "n_bands": 1, + "nan_pct": 0.0, + "raw_nan_pct_before_gapfill": 44.82421875, + "scale_m": 30, + "scene_coverage": 1.0, + "sha256": "39bb60395362ff36dda60631d883dbf15c2b3638b349ff3b1b2498b7aad5cdc9", + "shape": [ + 128, + 128 + ], + "source": "UMD/hansen/global_forest_change_2025_v1_13 lossyear=2022", + "source_mode": "gee_live", + "units": "binary" +} diff --git a/data/source_assets/multi_hazard/deforestation/000003/rasters/hansen_loss__event.tif b/data/source_assets/multi_hazard/deforestation/000003/rasters/hansen_loss__event.tif new file mode 100644 index 0000000000000000000000000000000000000000..c834021820d5b00672ee009631925d6ceb54d54a Binary files /dev/null and b/data/source_assets/multi_hazard/deforestation/000003/rasters/hansen_loss__event.tif differ diff --git a/data/source_assets/multi_hazard/deforestation/000003/rasters/ndvi_change.json b/data/source_assets/multi_hazard/deforestation/000003/rasters/ndvi_change.json new file mode 100644 index 0000000000000000000000000000000000000000..1aa3d15295da7058dad7219040fbe9bc7ad98adb --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000003/rasters/ndvi_change.json @@ -0,0 +1,24 @@ +{ + "constant": false, + "derived_from": [ + "s2_ndvi__pre", + "s2_ndvi__post" + ], + "formula": "s2_ndvi_pre - s2_ndvi_post", + "geotiff_path": "source_assets/multi_hazard/deforestation/000003/rasters/ndvi_change.tif", + "handle": "ndvi_change", + "maximum": 0.6655064225196838, + "minimum": -0.6674482226371765, + "modality": "s2_ndvi_change", + "scale_m": 10, + "schema_version": "geodisaster_derived_sidecar_v2", + "sha256": "ee1133513e52cb174279ecf3236fa008b7fed31f3e946aaae78d6250b014721b", + "shape": [ + 128, + 128 + ], + "source": "derived from Sentinel-2 NDVI", + "source_mode": "provider_exact_rebuild", + "units": "index", + "valid_fraction": 1.0 +} diff --git a/data/source_assets/multi_hazard/deforestation/000003/rasters/ndvi_change.tif b/data/source_assets/multi_hazard/deforestation/000003/rasters/ndvi_change.tif new file mode 100644 index 0000000000000000000000000000000000000000..4d4bbddf8ee7a3226a60ee987393e26ede105d9c Binary files /dev/null and b/data/source_assets/multi_hazard/deforestation/000003/rasters/ndvi_change.tif differ diff --git a/data/source_assets/multi_hazard/deforestation/000003/rasters/radd_alerts__event.json b/data/source_assets/multi_hazard/deforestation/000003/rasters/radd_alerts__event.json new file mode 100644 index 0000000000000000000000000000000000000000..9397c0626e7bab6dc2e1a0bc2340d6569ec9c47f --- /dev/null +++ b/data/source_assets/multi_hazard/deforestation/000003/rasters/radd_alerts__event.json @@ -0,0 +1,97 @@ +{ + "band_or_field": "date_conf", + "citation_url": "https://www.wur.nl/en/research/products-services/radd-forest-disturbance-alert", + "constant": true, + "crs": "EPSG:4326", + "derivation": "decode date_conf; 2xxxx=low confidence, 3xxxx=high confidence; preserve confidence as 1/2 and write a binary event-window mask separately", + "fetch_provenance": { + "output": { + "bytes": 1344, + "constant": true, + "export_grid": { + "crs": "EPSG:4326", + "height": 128, + "width": 128 + }, + "maximum": 0.0, + "mean": 0.0, + "minimum": 0.0, + "native_resolution_m": 10, + "path": "work/rebuilt/deforestation/000003/rasters/radd_alerts__event.tif", + "resampling": "max", + "sha256": "7bc27b8865f61c7cd3f450c63f627584587819cf1172ef00e7a86d7b024a0c30", + "units": "confidence_class_1_low_2_high", + "valid_fraction": 1.0 + }, + "request": { + "aoi_geometry": { + "coordinates": [ + [ + [ + -69.29272088386337, + -12.640843191311232 + ], + [ + -69.13272088386337, + -12.640843191311232 + ], + [ + -69.13272088386337, + -12.480843191311232 + ], + [ + -69.29272088386337, + -12.480843191311232 + ], + [ + -69.29272088386337, + -12.640843191311232 + ] + ] + ], + "type": "Polygon" + }, + "end_date": "2022-09-30", + "start_date": "2022-06-01" + }, + "upstream": { + "dataset": "wur_radd_alerts", + "pixel_meaning": "date_conf", + "tile_id": "10S_070W", + "version": "v20261011" + } + }, + "geotiff_path": "source_assets/multi_hazard/deforestation/000003/rasters/radd_alerts__event.tif", + "handle": "radd_alerts__event", + "license": "CC BY 4.0", + "maximum": 0.0, + "minimum": 0.0, + "modality": "radd_alert_confidence", + "n_bands": 1, + "nan_pct": 0.0, + "native_resolution_m": 10, + "nodata": -9999.0, + "product": "RADD Forest Disturbance Alerts v1", + "provider": "Wageningen University & Research / Global Forest Watch", + "scale_m": 10, + "schema_version": "geodisaster_source_sidecar_v2", + "sha256": "7bc27b8865f61c7cd3f450c63f627584587819cf1172ef00e7a86d7b024a0c30", + "shape": [ + 128, + 128 + ], + "source": "Wageningen University & Research / Global Forest Watch — RADD Forest Disturbance Alerts v1", + "source_mode": "provider_exact_rebuild", + "temporal_rule": "retain low- and high-confidence alerts whose first-alert date is within each AOI start/end window", + "transform": [ + 0.0012499999999999734, + 0.0, + -69.29272088386337, + 0.0, + -0.0012500000000000011, + -12.480843191311232 + ], + "units": "encoded alert date and confidence class", + "upstream_identifier": "projects/radar-wur/raddalert/v1; GFW date_conf GeoTIFF tiles", + "valid_fraction": 1.0 +} diff 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+ "crs": "EPSG:4326", + "derivation": "decode date_conf; 2xxxx=low confidence, 3xxxx=high confidence; preserve confidence as 1/2 and write a binary event-window mask separately", + "fetch_provenance": { + "output": { + "bytes": 1321, + "constant": true, + "export_grid": { + "crs": "EPSG:4326", + "height": 128, + "width": 128 + }, + "maximum": 0.0, + "mean": 0.0, + "minimum": 0.0, + "native_resolution_m": 10, + "path": "work/rebuilt/deforestation/000003/rasters/radd_alerts__event_binary.tif", + "resampling": "max", + "sha256": "1e03083187178821cce2a5d8a1e06e96eac709bceb9cc683f5bf252dffd83006", + "units": "binary", + "valid_fraction": 1.0 + }, + "request": { + "aoi_geometry": { + "coordinates": [ + [ + [ + -69.29272088386337, + -12.640843191311232 + ], + [ + -69.13272088386337, + -12.640843191311232 + ], + [ + -69.13272088386337, + -12.480843191311232 + ], + [ + -69.29272088386337, + -12.480843191311232 + ], + [ + -69.29272088386337, + -12.640843191311232 + ] + ] + ], + "type": "Polygon" + }, + "end_date": "2022-09-30", + "start_date": "2022-06-01" + }, + "upstream": { + "dataset": "wur_radd_alerts", + "pixel_meaning": "date_conf", + "tile_id": "10S_070W", + "version": "v20261011" + } + }, + "geotiff_path": "source_assets/multi_hazard/deforestation/000003/rasters/radd_alerts__event_binary.tif", + "handle": "radd_alerts__event_binary", + "license": "CC BY 4.0", + "maximum": 0.0, + "minimum": 0.0, + "modality": "radd_event_window_mask", + "n_bands": 1, + "nan_pct": 0.0, + "native_resolution_m": 10, + "nodata": -9999.0, + "product": "RADD Forest Disturbance Alerts v1", + "provider": "Wageningen University & Research / Global Forest Watch", + "scale_m": 10, + "schema_version": "geodisaster_source_sidecar_v2", + "sha256": "1e03083187178821cce2a5d8a1e06e96eac709bceb9cc683f5bf252dffd83006", + "shape": [ + 128, + 128 + ], + "source": "Wageningen University & Research / Global Forest Watch — RADD Forest Disturbance Alerts v1", + "source_mode": "provider_exact_rebuild", + 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