Release v0.3.0: Kenya Microsoft/Google building rectangles

BUILD_INFO.json

@@ -1,98 +1,149 @@
 {
-  "release": "v0.2.0",
+  "release": "v0.3.0",
   "available_datasets": [
     "doclaynet",
     "mot20",
-    "coco_sama"
+    "coco_sama",
+    "kenya_buildings"
   ],
   "pending_datasets": {},
-  "environment": {
-    "python": "3.12.7 | packaged by Anaconda, Inc. | (main, Oct  4 2024, 13:27:36) [GCC 11.2.0]",
-    "platform": "Linux-5.4.0-216-generic-x86_64-with-glibc2.31",
-    "machine": "x86_64",
-    "packages": {
-      "numpy": "1.26.4",
-      "pyarrow": "16.1.0",
-      "requests": "2.33.1",
-      "matplotlib": "3.9.2",
-      "huggingface_hub": "1.11.0"
-    }
-  },
-  "sources_lock_sha256": "4d153e0c2e6abee9d20ecd59710a3b37b9672e068fb995e831259244a897cb19",
   "builder_repository": "https://github.com/DANNHIROAKI/VOSMA-Dataset-Build",
-  "builder_version_tag": "v0.2.0",
-  "doclaynet": {
-    "selection": "All 4,999 official test pages",
-    "relation_semantics": "Original page GT versus fixed model predictions",
-    "model": {
-      "model_id": "Aryn/deformable-detr-DocLayNet",
-      "revision": "d5503a90ae08dd43565de6984a5dd7924cad2400",
-      "url": "https://huggingface.co/Aryn/deformable-detr-DocLayNet/tree/d5503a90ae08dd43565de6984a5dd7924cad2400",
-      "license": "Apache-2.0",
-      "files": {
-        "README.md": {
-          "sha256": "8e33e46f2a9998656103c130aab591d60c0a89a4cfe027bcd2d8dcaa26057c8e",
-          "size": 4551
+  "builder_version_tag": "v0.3.0",
+  "preserved_original_datasets": {
+    "names": [
+      "doclaynet",
+      "mot20",
+      "coco_sama"
+    ],
+    "huggingface_revision": "14b11a0b5eb6c19b3c686def1d1db67a98aba6dd",
+    "files_changed": false,
+    "build_info": {
+      "release": "v0.2.0",
+      "available_datasets": [
+        "doclaynet",
+        "mot20",
+        "coco_sama"
+      ],
+      "pending_datasets": {},
+      "environment": {
+        "python": "3.12.7 | packaged by Anaconda, Inc. | (main, Oct  4 2024, 13:27:36) [GCC 11.2.0]",
+        "platform": "Linux-5.4.0-216-generic-x86_64-with-glibc2.31",
+        "machine": "x86_64",
+        "packages": {
+          "numpy": "1.26.4",
+          "pyarrow": "16.1.0",
+          "requests": "2.33.1",
+          "matplotlib": "3.9.2",
+          "huggingface_hub": "1.11.0"
+        }
+      },
+      "sources_lock_sha256": "4d153e0c2e6abee9d20ecd59710a3b37b9672e068fb995e831259244a897cb19",
+      "builder_repository": "https://github.com/DANNHIROAKI/VOSMA-Dataset-Build",
+      "builder_version_tag": "v0.2.0",
+      "doclaynet": {
+        "selection": "All 4,999 official test pages",
+        "relation_semantics": "Original page GT versus fixed model predictions",
+        "model": {
+          "model_id": "Aryn/deformable-detr-DocLayNet",
+          "revision": "d5503a90ae08dd43565de6984a5dd7924cad2400",
+          "url": "https://huggingface.co/Aryn/deformable-detr-DocLayNet/tree/d5503a90ae08dd43565de6984a5dd7924cad2400",
+          "license": "Apache-2.0",
+          "files": {
+            "README.md": {
+              "sha256": "8e33e46f2a9998656103c130aab591d60c0a89a4cfe027bcd2d8dcaa26057c8e",
+              "size": 4551
+            },
+            "config.json": {
+              "sha256": "01d2bd3356abd64b84b837294782b28a4052f1a36b19e3a9c7d84f75ee15d5e6",
+              "size": 1822
+            },
+            "preprocessor_config.json": {
+              "sha256": "6eb5442e705395d5b9699782b18668733f81ba9f8f81729be33ca3526fd43641",
+              "size": 300
+            },
+            "model.safetensors": {
+              "sha256": "e3861d34685d3b36e5f38370597daf98fb2f98a852cfa5c125035057ded06809",
+              "size": 164653696
+            }
+          },
+          "training_data": "DocLayNet; exact training split list is not published",
+          "training_overlap_status": "unknown"
         },
-        "config.json": {
-          "sha256": "01d2bd3356abd64b84b837294782b28a4052f1a36b19e3a9c7d84f75ee15d5e6",
-          "size": 1822
+        "inference_environment": {
+          "python": "3.12.7",
+          "torch": "2.5.1+cpu",
+          "torchvision": "0.20.1+cpu",
+          "transformers": "4.46.3",
+          "timm": "0.9.12",
+          "safetensors": "0.4.5",
+          "Pillow": "12.2.0",
+          "numpy": "1.26.4",
+          "tokenizers": "0.20.3",
+          "huggingface-hub": "0.36.2"
         },
-        "preprocessor_config.json": {
-          "sha256": "6eb5442e705395d5b9699782b18668733f81ba9f8f81729be33ca3526fd43641",
-          "size": 300
+        "numerical_protocol": {
+          "device": "cpu",
+          "dtype": "float32",
+          "batch_size": 1,
+          "threads_per_worker": 4,
+          "interop_threads": 1,
+          "seed": 20260915,
+          "deterministic_algorithms": true,
+          "configuration_overrides": {
+            "use_pretrained_backbone": false,
+            "disable_custom_kernels": true,
+            "use_timm_backbone": true
+          }
         },
-        "model.safetensors": {
-          "sha256": "e3861d34685d3b36e5f38370597daf98fb2f98a852cfa5c125035057ded06809",
-          "size": 164653696
-        }
+        "initial_workers": 8,
+        "resumed_workers": 16,
+        "training_overlap_status": "unknown",
+        "inference_cost_is_dataset_construction": true,
+        "algorithm_preprocessing_remains_timed": true
       },
-      "training_data": "DocLayNet; exact training split list is not published",
-      "training_overlap_status": "unknown"
-    },
-    "inference_environment": {
-      "python": "3.12.7",
-      "torch": "2.5.1+cpu",
-      "torchvision": "0.20.1+cpu",
-      "transformers": "4.46.3",
-      "timm": "0.9.12",
-      "safetensors": "0.4.5",
-      "Pillow": "12.2.0",
+      "model_lock_sha256": "037c5e746f35a80f8e4b342432b02bb7a5d01ede6db58615f91fc1f3f22f1aa9",
+      "validation": {
+        "unit_tests_passed": 26,
+        "record_invariants_checked": "every published rectangle",
+        "doclaynet_all_png_bytes_and_prediction_provenance": true,
+        "doclaynet_independent_full_source_record_audit": true,
+        "doclaynet_independent_raw_tensor_postprocess_pilot": true,
+        "doclaynet_pilot_production_exact_repeat": true,
+        "overlap_counter_randomized_bruteforce_cases": 120,
+        "coco_independent_full_audit": true,
+        "mot20_and_coco_sama_files_unchanged_from_v010": true
+      },
+      "builder_commit": "4c3c3e9d8c074cd43884a8a650eeac0d9102a131"
+    }
+  },
+  "kenya_buildings": {
+    "r1_count": 12798990,
+    "r2_count": 25560434,
+    "total_records": 38359424,
+    "groups": 1,
+    "source_lock_sha256": "fd20a649bb73f3ad21c0fd34e88bd00081207c7cdd0a48973439b6c079892709",
+    "build_protocol_sha256": "d626f297e5df999747cbedbb162eb034488afcb4afca857680d8f457a1996d77",
+    "dependencies": {
+      "geos": "3.13.1",
       "numpy": "1.26.4",
-      "tokenizers": "0.20.3",
-      "huggingface-hub": "0.36.2"
+      "proj": "9.5.1",
+      "pyarrow": "16.1.0",
+      "pyproj": "3.7.2",
+      "python": "3.12.7",
+      "shapely": "2.1.2"
     },
-    "numerical_protocol": {
-      "device": "cpu",
-      "dtype": "float32",
-      "batch_size": 1,
-      "threads_per_worker": 4,
-      "interop_threads": 1,
-      "seed": 20260915,
-      "deterministic_algorithms": true,
-      "configuration_overrides": {
-        "use_pretrained_backbone": false,
-        "disable_custom_kernels": true,
-        "use_timm_backbone": true
-      }
+    "projection": "+proj=laea +lat_0=0 +lon_0=37.5 +datum=WGS84 +x_0=0 +y_0=0 +units=m +no_defs",
+    "coordinate_offset": {
+      "dx": 399224818,
+      "dy": 516729472
     },
-    "initial_workers": 8,
-    "resumed_workers": 16,
-    "training_overlap_status": "unknown",
-    "inference_cost_is_dataset_construction": true,
-    "algorithm_preprocessing_remains_timed": true
-  },
-  "model_lock_sha256": "037c5e746f35a80f8e4b342432b02bb7a5d01ede6db58615f91fc1f3f22f1aa9",
-  "validation": {
-    "unit_tests_passed": 26,
-    "record_invariants_checked": "every published rectangle",
-    "doclaynet_all_png_bytes_and_prediction_provenance": true,
-    "doclaynet_independent_full_source_record_audit": true,
-    "doclaynet_independent_raw_tensor_postprocess_pilot": true,
-    "doclaynet_pilot_production_exact_repeat": true,
-    "overlap_counter_randomized_bruteforce_cases": 120,
-    "coco_independent_full_audit": true,
-    "mot20_and_coco_sama_files_unchanged_from_v010": true
+    "builder_version_tag": "v0.3.0",
+    "construction_workers": 6,
+    "full_record_validation_passed": true,
+    "national_pairs_enumerated": false,
+    "specification_sha256": "bbfb4f576294f974c5c8a141cb2c1a5c3a9a14050844baee38136df54528b62c",
+    "builder_commit": "0bb876a283137cb8740d34e6cc11553616de5ecd",
+    "builder_url": "https://github.com/DANNHIROAKI/VOSMA-Dataset-Build/tree/0bb876a283137cb8740d34e6cc11553616de5ecd"
   },
-  "builder_commit": "4c3c3e9d8c074cd43884a8a650eeac0d9102a131"
+  "builder_commit": "0bb876a283137cb8740d34e6cc11553616de5ecd"
 }

README.md

@@ -34,6 +34,16 @@ configs:
     data_files:
       - split: groups
         path: doclaynet/groups.parquet
+  - config_name: kenya_buildings_records
+    data_files:
+      - split: r1
+        path: kenya_buildings/R1_metadata.parquet
+      - split: r2
+        path: kenya_buildings/R2_metadata.parquet
+  - config_name: kenya_buildings_groups
+    data_files:
+      - split: groups
+        path: kenya_buildings/groups.parquet
 tags:
   - spatial-join
   - weighted-sampling
@@ -43,19 +53,20 @@ tags:
 
 # VOSMA Real Rectangle Datasets
 
-Construction pipeline for three real-data rectangle relations for area-weighted and IoU-weighted spatial join sampling. **Release v0.2.0 contains completed DocLayNet, MOT20, and COCO/Sama-COCO datasets.** Each retained source annotation or exported prediction becomes one rectangle with unit record weight. This release contains integer arrays and provenance metadata; images, segmentation masks, algorithm indexes, and materialized join pairs are not included.
+Construction pipeline for four real-data rectangle relations for area-weighted and IoU-weighted spatial join sampling. **Release v0.3.0 adds the Kenya Microsoft/Google building-rectangle dataset. The completed DocLayNet, MOT20, and COCO/Sama-COCO files from v0.2.0 are preserved unchanged.** Each retained source annotation or exported prediction becomes one rectangle with unit record weight. This release contains integer arrays and provenance metadata; images, segmentation masks, algorithm indexes, and materialized join pairs are not included.
 
 | Dataset directory | R1 | R2 | Retained R1 records | Retained R2 records | Groups |
 |---|---|---|---:|---:|---:|
 | `doclaynet` | Official test-page ground truth | Fixed Aryn model predictions | 66,531 | 50,084 | 4,999 |
 | `mot20` | Valid pedestrian ground truth | Official detections | 1,134,614 | 661,143 | 8,931 |
 | `coco_sama` | COCO 2017 non-crowd instances | Sama-COCO non-crowd instances | 886,282 | 1,068,028 | 386,072 |
+| `kenya_buildings` | Microsoft Kenya footprint rectangles | Google Open Buildings V3 footprint rectangles | 12,798,990 | 25,560,434 | 1 |
 
 Counts refer to this processed release. Empty sides and groups remain represented according to the group universes below. The per-dataset `dataset.json` and `validation.json` provide filtering counts and validation results.
 
 ## Release status
 
-All 4,999 selected official DocLayNet test pages completed fixed-model inference and the independent image/provenance audit. The three datasets passed record-level geometry and group-isolation validation.
+All 4,999 selected official DocLayNet test pages completed fixed-model inference and the independent image/provenance audit. The original three datasets passed record-level geometry and group-isolation validation. Kenya passed checks of every published record and aligned metadata, sampled original-polygon reprojection checks, and independent fixed-window diagnostics. The pair-count table below describes the original three datasets; Kenya national pairs were not enumerated.
 
 | Completed dataset | Positive-area record pairs | Groups with positive mass | Groups with zero mass |
 |---|---:|---:|---:|
@@ -79,9 +90,11 @@ The checkpoint revision is `d5503a90ae08dd43565de6984a5dd7924cad2400` and its mo
 
 **COCO/Sama-COCO.** R1 uses the [COCO 2017 train/validation instance annotations](https://s3.amazonaws.com/images.cocodataset.org/annotations/annotations_trainval2017.zip); R2 uses the official [Sama train](https://sama-documentation-assets.s3.amazonaws.com/sama-coco/sama-coco-train.zip) and [Sama validation](https://sama-documentation-assets.s3.amazonaws.com/sama-coco/sama-coco-val.zip) annotations. Images are paired by source file identity within the original split, with dimension checks. Category names establish the mapping between the 80 classes. A group is `(split, image, category)` from the union of category mentions on either side before filtering. Both sides require `iscrowd=0`. Annotation-free images remain in `images.parquet`; unmentioned image-category combinations are not synthesized as groups. Sama reannotation began with COCO annotations, so this is a comparison of annotation versions, not a claim of independent blind annotation. [Sama's description of its labeling process](https://www.sama.com/sama-coco-dataset).
 
+**Kenya buildings.** R1 is the original Microsoft Kenya national building-footprint release; R2 is Google Open Buildings V3. A frozen full-resolution geoBoundaries Kenya ADM0 defines the common study region. Full source polygons are projected to one fixed WGS84 LAEA coordinate system, selected by projected polygon-centroid coverage, converted to axis-aligned bounds, quantized at 1000 units per metre, and translated by one shared integer offset. The entire country is one group. No source confidence cutoff, geometry repair, deduplication, object matching or tile isolation is added. The millimetre grid is a numerical representation, not a claim about imagery accuracy. See the [construction guide](https://github.com/DANNHIROAKI/VOSMA-Dataset-Build/blob/v0.3.0/KENYA_BUILD.md) and [published dataset documentation](https://huggingface.co/datasets/DannHiroaki/VOSMA-Dataset/blob/v0.3.0/kenya_buildings/README.md).
+
 ## Geometry contract
 
-For each source `bbox=[x,y,w,h]`, construct the endpoints exactly from the original decimal values, then compute:
+For the first three image-based datasets, for each source `bbox=[x,y,w,h]`, construct the endpoints exactly from the original decimal values, then compute:
 
 ```text
 Q(z) = nearest integer to 1000*z, with exact ties rounded to even
@@ -92,25 +105,25 @@ Raw annotation decimals are parsed as `Decimal`, and source endpoint arithmetic
 
 Malformed or non-finite boxes, nonpositive source widths/heights, and rectangles that become degenerate after quantization are excluded and counted. Finite negative coordinates and boxes outside the declared image bounds are retained. Coordinates are not clipped or resized during dataset conversion. Representability failures stop the build rather than silently wrapping integers.
 
-After quantization, each group receives one shared integer translation for both R1 and R2. Its minimum retained y-coordinate becomes 1. Groups occupy disjoint x intervals with a one-unit gap, using the union of retained rectangle extents on both sides. The translation and local bounds are stored in `groups.parquet`. Translation preserves the quantized within-group geometry, areas, and IoUs while preventing intersections between different groups. Quantization itself may change the original continuous geometry slightly.
+For these image-based datasets, after quantization each group receives one shared integer translation for both R1 and R2. Its minimum retained y-coordinate becomes 1. Groups occupy disjoint x intervals with a one-unit gap, using the union of retained rectangle extents on both sides. The translation and local bounds are stored in `groups.parquet`. Translation preserves the quantized within-group geometry, areas, and IoUs while preventing intersections between different groups. Quantization itself may change the original continuous geometry slightly.
 
 Every output `weight` is 1. Area sampling uses the quantized intersection area; IoU sampling uses intersection area divided by union area. No object matching, IoU threshold, NMS, deduplication, bbox merging, or annotation-count balancing is performed. Equal numbers of samples per group do not implement global mass-weighted sampling. Algorithm preprocessing remains part of measured algorithm cost. Int64 coordinate storage does not guarantee that area or cumulative-mass intermediates fit int64. Equal rectangles remain distinct records when they originate from distinct source annotation or prediction records.
 
 ## Files and schema
 
-Each of the three completed dataset directories contains the following files.
+The four dataset directories share the following array and metadata schema. Kenya source provenance uses `SOURCE_LOCK.json`, `country_boundary.geojson`, and `sources/`; its validation reports local overlap diagnostics rather than an exhaustive national pair count.
 
 | File | Contents |
 |---|---|
 | `R1.npy`, `R2.npy` | NumPy arrays with shape `(N,7)`, little-endian signed int64, no pickle |
 | `R1_metadata.parquet`, `R2_metadata.parquet` | One row per retained rectangle, aligned with array row order |
-| `groups.parquet` | Group identity, image dimensions, common translation, bounds, contiguous array slices, empty-side state, and validated overlap counts |
+| `groups.parquet` | Group identity, coordinate-frame dimensions, common translation, bounds, contiguous array slices, empty-side state, and available overlap statistics |
 | `dataset.json` | Schema version, relation definitions, counts, filtering statistics, and numerical contract |
 | `validation.json` | Validation results and clearly scoped geometric spot checks |
 | `exclusions.jsonl.gz` | Rejected source records and exclusion reasons |
 | `SHA256SUMS` | Checksums for the processed files |
 
-Dataset-specific pairing reports, category mappings, and `images.parquet` are included where applicable. Each dataset's `sources/*.json` records source URLs, versions, retrieval metadata, archive members, and SHA-256 checksums of the extracted original annotation files. Source fingerprints identify the exact input bytes used for the release.
+Dataset-specific pairing reports, category mappings, and `images.parquet` are included where applicable. For the first three datasets, `sources/*.json` records source URLs, versions, retrieval metadata, archive members, and SHA-256 checksums of the extracted original annotation files. For Kenya, `SOURCE_LOCK.json` pins the complete original building archives and boundary file; `sources/` preserves small upstream notices and tile metadata. Source fingerprints identify the exact input bytes used for the release.
 
 The seven array columns are:
 
@@ -120,7 +133,7 @@ record_id, group_id, x0, y0, x1, y1, weight
 
 `record_id` is a zero-based row number unique within its relation; `(relation, record_id)` is the full generated record identity. It is not the source annotation ID. `group_id` is shared across the two relations. Coordinates are quantized and translated; `weight` is always 1.
 
-The source record position is a 1-based CSV line number for MOT20 and a 0-based annotations-array index for JSON sources. Metadata includes source file, record position, annotation/image/category IDs, the original bbox decimal values, quantized local endpoints, and source-specific attributes. In `groups.parquet`, `r1_start/r1_count` and `r2_start/r2_count` identify contiguous row slices. `state` is one of `both_nonempty`, `r1_only`, `r2_only`, or `both_empty`. A group with two nonempty sides can still have zero positive-area join pairs.
+For the first three datasets, the source record position is a 1-based CSV line number for MOT20 and a 0-based annotations-array index for JSON sources. For Kenya, it is a 1-based original JSONL record/line for Microsoft and a 1-based data-row index excluding the header for Google; physical line numbers are also preserved. Metadata includes source file, record position, annotation/image/category IDs, the original bbox decimal values (projected metre bounds for Kenya), quantized local endpoints, and source-specific attributes. In `groups.parquet`, `r1_start/r1_count` and `r2_start/r2_count` identify contiguous row slices. `state` is one of `both_nonempty`, `r1_only`, `r2_only`, or `both_empty`. A group with two nonempty sides can still have zero positive-area join pairs.
 
 ## Load a frozen release
 
@@ -133,9 +146,9 @@ import numpy as np
 import pyarrow.parquet as pq
 from huggingface_hub import snapshot_download, HfApi
 
-revision = HfApi().dataset_info("DannHiroaki/VOSMA-Dataset", revision="v0.2.0").sha
+revision = HfApi().dataset_info("DannHiroaki/VOSMA-Dataset", revision="v0.3.0").sha
 print("Record this immutable revision with your experiment:", revision)
-dataset = "doclaynet"  # also available: mot20, coco_sama
+dataset = "doclaynet"  # also available: mot20, coco_sama, kenya_buildings
 root = Path(snapshot_download(
     repo_id="DannHiroaki/VOSMA-Dataset",
     repo_type="dataset",
@@ -161,6 +174,8 @@ The arrays can be used directly as benchmark inputs. Keep download, model infere
 
 ## Rebuild from original annotations
 
+The commands below reproduce the first three datasets. For the fourth dataset, follow [KENYA_BUILD.md](https://github.com/DANNHIROAKI/VOSMA-Dataset-Build/blob/v0.3.0/KENYA_BUILD.md), using the committed `kenya-sources.lock.json`.
+
 From the builder repository root, with Python 3.12 and a C++17 compiler available:
 
 ```sh
@@ -194,8 +209,10 @@ Licenses apply to each dataset portion separately; this collection does not repl
 | Aryn source model | [Apache-2.0 model card](https://huggingface.co/Aryn/deformable-detr-DocLayNet/blob/d5503a90ae08dd43565de6984a5dd7924cad2400/README.md) | Model weights are fetched as build inputs; their pinned revision and license remain recorded in provenance. |
 | MOT20-derived data | [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) | Attribution, noncommercial use, and ShareAlike conditions apply. |
 | COCO/Sama-COCO-derived annotation data | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | Preserve attribution and license access, and indicate modifications. |
+| Kenya Microsoft/Google-derived rectangle data | [ODbL 1.0](https://opendatacommons.org/licenses/odbl/1-0/) | Microsoft specifies ODbL; the Google dual-license ODbL option is selected. Source attribution and the complete license accompany the fourth dataset. |
+| Kenya boundary | Public Domain upstream boundary, with geoBoundaries/RCMRD attribution | Fixed source, version, and processing are recorded with the fourth dataset. |
 
-Credit the original DocLayNet, MOT20, COCO, and Sama-COCO creators when using their portions. This derivative changes annotation selection, numerical representation, and group placement as documented above. Source image licenses are separate; source images are not redistributed here. See the [COCO terms](https://cocodataset.org/#termsofuse) and [Sama dataset information](https://www.sama.com/sama-coco-dataset) for the original resources.
+Credit the original DocLayNet, MOT20, COCO, Sama-COCO, Microsoft, Google, and geoBoundaries/RCMRD creators when using their portions. This derivative changes annotation selection, numerical representation, and group placement as documented above. Source image licenses are separate; source images are not redistributed here. See the [COCO terms](https://cocodataset.org/#termsofuse) and [Sama dataset information](https://www.sama.com/sama-coco-dataset) for the original resources.
 
 ## Cite the sources
 
@@ -204,4 +221,4 @@ Credit the original DocLayNet, MOT20, COCO, and Sama-COCO creators when using th
 - Lin et al. (2014), [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312).
 - Zimmermann et al. (2023), [Benchmarking a Benchmark: How Reliable is MS-COCO?](https://arxiv.org/abs/2311.02709).
 
-Also identify this dataset repository and the exact release commit SHA in experiment reports. Model predictions versus GT, detection versus GT, and annotation-version comparisons have different provenance and should be reported separately.
+Also identify this dataset repository and the exact release commit SHA in experiment reports. Model predictions versus GT, detection versus GT, annotation-version comparisons, and cross-source building detections have different provenance and should be reported separately.

SHA256SUMS

@@ -1,5 +1,5 @@
-81223b64c53b8e367e0272eb03fe98987b71fc5dd1d3e6c593b869d186417408  BUILD_INFO.json
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kenya_buildings/ATTRIBUTION.md

@@ -0,0 +1,27 @@
+# Attribution and license notices
+
+This derived Kenya rectangle database contains information from
+[Microsoft KenyaNigeriaBuildingFootprints](https://github.com/microsoft/KenyaNigeriaBuildingFootprints) and
+[Google Research Open Buildings V3](https://sites.research.google/open-buildings/), made available here under the
+[Open Database License (ODbL) 1.0](https://opendatacommons.org/licenses/odbl/1-0/).
+Microsoft supplies its source under ODbL. Google offers CC BY 4.0 or ODbL 1.0;
+this release explicitly selects the ODbL option. Source copyright and database
+rights notices are retained in the supplied provenance and source links.
+
+The modifications are country inclusion by projected original-polygon centroid,
+validity exclusions, a fixed WGS84 LAEA projection, projected axis-aligned
+bounding boxes, Q1000 endpoint quantization, a common integer translation and
+row-aligned provenance metadata. Original building polygons are not matched,
+repaired, simplified, confidence-filtered or deduplicated.
+
+Administrative boundaries courtesy of [geoBoundaries.org](https://www.geoboundaries.org/);
+source **RCMRD GeoPortal**, Kenya ADM0, representative year 2020, frozen commit
+`9469f09592ced973a3448cf66b6100b741b64c0d`. Its included upstream metadata specifies **Public Domain**.
+Boundary source acknowledgement: Runfola, D. et al. (2020),
+*geoBoundaries: A global database of political administrative boundaries*,
+PLoS ONE 15(4): e0231866, https://doi.org/10.1371/journal.pone.0231866.
+The fixed boundary is an operational study area and does not adjudicate borders.
+
+The unmodified official ODbL 1.0 text in `LICENSE.txt` was downloaded from
+https://opendatacommons.org/licenses/odbl/odbl-10.txt; SHA-256 `607680718977f6f6c9607972afd98f208573f19251315ed1362a8589b51beaf5`.
+The ODbL database license does not change the builder repository's code license.

kenya_buildings/LICENSE.txt

@@ -0,0 +1,540 @@
+## ODC Open Database License (ODbL)
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kenya_buildings/R1.npy

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kenya_buildings/README.md

@@ -0,0 +1,161 @@
+---
+license: odbl
+language:
+- en
+---
+# Kenya cross-source building rectangles
+
+This fourth VOSMA real dataset compares Microsoft Kenya building footprints (R1)
+with Google Open Buildings V3 (R2). Both sides are machine-generated detections;
+neither side is human ground truth. The intended workload samples rectangle
+pairs with positive intersection area, weighted by rectangle area or rectangle
+IoU. Rectangle overlap is not proof of a matching real-world building, nor the
+same quantity as original-polygon overlap.
+
+## Published scale
+
+| Relation | Source | Read records | Retained rectangles | Excluded records |
+| --- | --- | --- | --- | --- |
+| R1 | Microsoft Kenya | 14,748,685 | 12,798,990 | 1,949,695 |
+| R2 | Google Open Buildings V3 | 96,167,742 | 25,560,434 | 70,607,308 |
+
+There is **one national group** (`group_id=0`). Download tiles do not form
+separate scenes. The full national relative geometry is preserved. Actual source
+and output checksums are in [SOURCE_LOCK.json](SOURCE_LOCK.json) and
+[SHA256SUMS](SHA256SUMS); full construction statistics are in
+[dataset.json](dataset.json).
+
+## Frozen sources and geographic scope
+
+- [Microsoft KenyaNigeriaBuildingFootprints](https://github.com/microsoft/KenyaNigeriaBuildingFootprints): the fixed legacy
+  Kenya release, with imagery from 2020–2021. The upstream advertised count is
+  14,748,685 before this benchmark's validation and country inclusion rule.
+- [Google Open Buildings V3](https://sites.research.google/open-buildings/): inference during May 2023; the original
+  polygon CSVs, not point-only records. The upstream release already excludes
+  confidence below 0.65. This build applies no further confidence cutoff and
+  uses unit record weights. Google tiles read: `171, 177, 179, 17b, 17d, 17f, 181, 183, 185, 19b, 19d`.
+- [geoBoundaries Kenya ADM0](https://media.githubusercontent.com/media/wmgeolab/geoBoundaries/9469f09592ced973a3448cf66b6100b741b64c0d/releaseData/gbOpen/KEN/ADM0/geoBoundaries-KEN-ADM0.geojson):
+  full-resolution boundary frozen at commit `9469f09592ced973a3448cf66b6100b741b64c0d`, boundary ID
+  `KEN-ADM0-21065076`, representing 2020, built 2023-12-12. Longitude/latitude
+  bounds (west, south, east, north): `[33.911819458, -4.702270508, 41.906257629, 5.430648327]`.
+
+Google download planning uses a conservative spherical S2 level-4 covering of
+the country's bounding box together with official tile geometry intersections.
+Candidate `187` is absent from the official tile index and bucket, and lies
+outside the actual country polygon; the available selected tiles cover the
+entire frozen boundary. The additional bbox tile `19b` is read conservatively.
+See the source lock and small [source records](sources/SOURCE_REFERENCES.json).
+The supplied border, including its islands, defines this benchmark's study area.
+
+## Construction and representation
+
+1. Validate the complete original Polygon or MultiPolygon; preserve multipart
+   records and holes. Invalid records are traced in `exclusions.jsonl.gz`.
+2. Project every original coordinate and the fixed national boundary from
+   WGS84 with `always_xy=True` into `+proj=laea +lat_0=0 +lon_0=37.5 +datum=WGS84 +x_0=0 +y_0=0 +units=m +no_defs`, explicitly using
+   the WGS84 datum. No simplification or edge densification is added.
+3. Keep a record exactly when the projected country boundary **covers the
+   centroid of the full projected original geometry**. Google CSV centroid
+   columns are not the inclusion predicate. Retain complete cross-border
+   footprints; do not clip them to the country.
+4. Take each projected geometry's axis-aligned bounds, multiply endpoints by
+   1000, and round to the nearest integer with ties to even. One coordinate
+   unit is **one millimetre of quantization, not one millimetre of source accuracy**.
+5. Apply the same integer translation to both complete relations:
+   `dx=399224818`, `dy=516729472`. It preserves all relative positions,
+   rectangle intersection areas and IoUs of the quantized representation.
+
+There is no deduplication, confidence weighting, cross-source matching,
+overlap-based selection, or algorithm-specific preprocessing. No sampler index
+or pair list is included. Original duplicate records remain distinct records.
+Any later size subsets must inherit the full dataset's projection, quantization
+and exact common offset; they must not be repositioned independently.
+
+`R1.npy` and `R2.npy` are little-endian int64 arrays with columns
+`[record_id, group_id, x0, y0, x1, y1, weight]`; IDs start from zero independently
+per side, weights equal one, and rectangles are half-open. Each aligned
+`R*_metadata.parquet` preserves original source file/record identity, projected
+centroid, unshifted quantized endpoints and source properties. Original record
+indexes are one-based. `groups.parquet` contains the single national group.
+
+## Exclusions
+
+| Reason | R1 | R2 |
+| --- | --- | --- |
+| centroid_outside_country | 1,949,559 | 70,607,308 |
+| invalid_geometry | 102 | 0 |
+| invalid_projected_geometry | 34 | 0 |
+
+Every exclusion is attributable to its original source record. No exclusion
+decision uses the other relation or a benchmark result.
+
+## Verification
+
+Independent validation **passed**. It scanned every published record and its
+aligned metadata, checked IDs, unit weights, positive bounds, source identity,
+Q1000 quantization, the common shift, national centroid coverage and accounting.
+Raw-polygon reprojection was independently recomputed for
+44 retained QA records.
+See [validation.json](validation.json) and [qa_overlay.png](qa_overlay.png).
+
+The additional [fixed-region overlay](qa_local_overlay.png) shows both sources'
+original footprints and their final published rectangles together, at their
+true relative positions, in three predeclared **200 m by 200 m windows** centered
+on Nairobi, Kisumu and Turkana. Each side independently contributes at most 12
+records per window, selected by the lowest published record IDs whose projected
+polygon centroids fall inside. Overlap, IoU and visual appearance never affect
+selection. Empty windows remain empty; centers are never relocated.
+All 71 selected original source records were re-read,
+reprojected and checked against their aligned metadata and final NPY rectangles.
+The [local overlay report](qa_local_overlay.json) records fixed centers, source
+identities, array IDs and scan bounds. This is deterministic local visual QA,
+not a random or representative national quality estimate. Only the plot axes
+crop the view; the dataset's full geometries and rectangles remain unchanged.
+
+The following diagnostics use only three predeclared **10 km by 10 km windows**.
+Each side is selected independently by projected polygon centroid; its entire
+rectangle is then used without clipping. Pairs with omitted outside-window
+records are not counted. These are exact positive-pair counts for those local
+subsets, **not a national join count K and not an estimate of national density
+or weight distributions**. They never filter the published data.
+
+| Diagnostic window | R1 records | R2 records | Positive rectangle pairs | R1 without overlap | R2 without overlap |
+| --- | --- | --- | --- | --- | --- |
+| Nairobi | 59,797 | 171,740 | 282,139 | 933 | 27,467 |
+| Kisumu | 66,191 | 117,467 | 162,247 | 1,870 | 24,217 |
+| Turkana | 14,469 | 40,929 | 22,614 | 1,851 | 24,095 |
+
+`validation.json` additionally records local area/IoU statistics. Local minima,
+maxima and means use all local positive pairs; quantiles use all pairs up to
+50,000 or a deterministic uniform-priority sample thereafter. No national pair
+enumeration or pair-list artifact was produced. These diagnostics provide no
+claim about which sampling algorithm will run faster.
+
+## Reproduction
+
+Builder commit: [`0bb876a283137cb8740d34e6cc11553616de5ecd`](https://github.com/DANNHIROAKI/VOSMA-Dataset-Build/tree/0bb876a283137cb8740d34e6cc11553616de5ecd).
+Download this dataset directory as `kenya_buildings` next to the cloned builder,
+then run from the builder checkout:
+
+```sh
+git checkout 0bb876a283137cb8740d34e6cc11553616de5ecd
+python -m pip install -r requirements.txt -r requirements-kenya.txt
+python fetch_kenya_sources.py --source-lock ../kenya_buildings/SOURCE_LOCK.json --raw-root ./work/raw --log-root ./work/download-logs
+python build_kenya.py --source-lock ../kenya_buildings/SOURCE_LOCK.json --raw-root ./work/raw --output-root ./work/release --work-root ./work/checkpoints
+python validate_kenya.py --dataset-dir ./work/release/kenya_buildings
+python qa_kenya_local_overlay.py --dataset-dir ./work/release/kenya_buildings --raw-root ./work/raw
+```
+
+The downloader verifies the frozen files rather than selecting newer upstream
+versions. Construction is resumable; source, builder, geometry-library or
+protocol changes invalidate checkpoints. Dependency versions used for this
+release are recorded in `dataset.json`. File hashes can be checked inside this
+published directory with `sha256sum -c SHA256SUMS`.
+
+## License and attribution
+
+The two-source derived rectangle database is provided under **ODbL 1.0**.
+Microsoft's data uses ODbL; Google's dual-licensed data is used here under its
+ODbL option. The boundary metadata identifies its upstream boundary as Public
+Domain. See [LICENSE.txt](LICENSE.txt) for the unmodified full ODbL text and
+[ATTRIBUTION.md](ATTRIBUTION.md) for source notices and boundary acknowledgement.

kenya_buildings/SHA256SUMS

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+607680718977f6f6c9607972afd98f208573f19251315ed1362a8589b51beaf5  LICENSE.txt
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+89248c71d10e86d76a2ca336aa2444aac9901651c9cdb974a83f29fbb72c55fa  country_boundary.geojson
+f66d0966deb36b4109d76254d760979cda7b4dda965828f9bf47e2b3b0c477fe  dataset.json
+3aa034236bfb7c1be1c9eefb6b91df2f13c18d85726fd00fccd141eb3311f9d1  exclusions.jsonl.gz
+9b679c64fc5c676a63a77fdc0db17538c0d706a287ce6fa0fcd314cc9143b2bb  groups.parquet
+14a958c4e4bea910041160855b9264f22231e98efb9438417372375ac090c2e0  qa_local_overlay.json
+9c317f076044a57b15335f1eda69bded8443969e91a36d49cefa3a33fb8f8e62  qa_local_overlay.png
+2734b3079714cf97d861d6dda374ec4d9fb64668da555f186b02fb3324688316  qa_overlay.png
+1273074878f465215b44db0d51b5b1b53addd75b58d5154b008194a8e1514eaa  qa_samples.geojson
+4ae64680bea104f056dee494e7da62b67b0fc135c07980eeefc9ef791cc837ca  sources/SOURCE_REFERENCES.json
+c3388ed74e1296bcc7f0f38033f2bf7f9ac2798c6c003f0df4d1f37d31e16057  sources/google-187-prefix-list.json
+bbf81906cbefb3ab0b598de3b0ee8b8c8e5c09237a345e8b7918abd17421797c  sources/google-extra-tiles.json
+f63227f3da41a40381d16b5bf6a9a1101a3a462650a69ff3049c6d775e6f1e77  sources/google-tiles.geojson
+da966b365c2bc8af603c1bd2e9ad6558634ae9fb1c022fb23856d71457fd4ff9  sources/kenya-boundary-metadata.json
+808faa84f8fb968f35527b22d8830161b24fa6c16899ea6542367805a52d19f0  sources/kenya-boundary-metadata.txt
+f0a263b59a67c6ec75030854eda123f24c57f896fcc16bbf6a78227b641fba90  sources/microsoft-readme.md
+30d7650b2ca1f4f723d476ab21227ef0000290dde6eb82dc163f78368f4748fe  validation.json

kenya_buildings/SOURCE_LOCK.json

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+  "status": "All source bytes acquired and SHA256-verified before geometry construction",
+  "sources": [
+    {
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+      "format": "geojsonl.zip",
+      "path": "microsoft/kenya.geojsonl.zip",
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+      "url": "https://minedbuildings.z5.web.core.windows.net/legacy/africa/kenya.geojsonl.zip",
+      "expected_records": 14748685,
+      "size_bytes": 1046134828,
+      "etag": "\"0x8DCFF4EE129ABDA\"",
+      "last_modified": "Thu, 07 Nov 2024 17:09:02 GMT",
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+      "license": "ODbL-1.0 (selected from official dual license)"
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+      "path": "google/183_buildings.csv.gz",
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+      "path": "google/19b_buildings.csv.gz",
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+      "license": "ODbL-1.0 (selected from official dual license)"
+    }
+  ],
+  "boundary": {
+    "path": "boundary/geoBoundaries-KEN-ADM0.geojson",
+    "url": "https://media.githubusercontent.com/media/wmgeolab/geoBoundaries/9469f09592ced973a3448cf66b6100b741b64c0d/releaseData/gbOpen/KEN/ADM0/geoBoundaries-KEN-ADM0.geojson",
+    "sha256": "89248c71d10e86d76a2ca336aa2444aac9901651c9cdb974a83f29fbb72c55fa",
+    "size_bytes": 1333814,
+    "source_commit": "9469f09592ced973a3448cf66b6100b741b64c0d",
+    "boundary_id": "KEN-ADM0-21065076",
+    "year_represented": 2020,
+    "build_date": "2023-12-12",
+    "source": "RCMRD GeoPortal, geoBoundaries",
+    "license": "Public Domain (upstream boundary); geoBoundaries attribution supplied",
+    "bounds_epsg4326": [
+      33.91181945760195,
+      -4.702270507977516,
+      41.90625762938612,
+      5.430648327058691
+    ],
+    "metadata_url": "https://media.githubusercontent.com/media/wmgeolab/geoBoundaries/9469f09592ced973a3448cf66b6100b741b64c0d/releaseData/gbOpen/KEN/ADM0/geoBoundaries-KEN-ADM0-metaData.json",
+    "metadata_sha256": "da966b365c2bc8af603c1bd2e9ad6558634ae9fb1c022fb23856d71457fd4ff9"
+  },
+  "tile_index": {
+    "url": "https://openbuildings-public-dot-gweb-research.uw.r.appspot.com/public/tiles.geojson",
+    "sha256": "f63227f3da41a40381d16b5bf6a9a1101a3a462650a69ff3049c6d775e6f1e77",
+    "size_bytes": 250366,
+    "total_tiles": 333,
+    "selected_tokens": [
+      "171",
+      "177",
+      "179",
+      "17b",
+      "17d",
+      "17f",
+      "181",
+      "183",
+      "185",
+      "19b",
+      "19d"
+    ],
+    "selection": "Conservative union of (a) true spherical S2 level-4 covering of the entire EPSG:4326 Kenya boundary bounding box (using s2sphere.RegionCoverer with min_level=max_level=4) and (b) all official tile GeoJSON polygons intersecting the full-resolution Kenya boundary. Download all published objects in this union.",
+    "coverage_verification": "The mathematical spherical S2 bounding-box cover is a superset of Kenya. Its 11 published tiles (plus one officially unprovided bbox-only candidate 187) are used. Their official tile polygon union covers the entire Kenya ADM0 geometry (covers=True, boundary-minus-union area=0 square degrees). The original 10 directly intersecting tiles already have 0.16438123814696362-degree minimum outer-edge margin; 19b is additionally downloaded conservatively. Candidate 187 is absent from the official manifest and bucket and is disjoint from Kenya. No claim is made that an absent upstream file proves worldwide absence of detections in that cell.",
+    "record_selection": "Per RealDataset.md chapter 4, project each original building polygon and the boundary into the fixed CRS; retain exactly records whose projected polygon centroid is covered by the projected Kenya boundary. Retain full polygon, including any portions outside the boundary. Do not use Google CSV centroid columns for record selection.",
+    "spherical_s2_bbox_covering_tokens": [
+      "171",
+      "177",
+      "179",
+      "17b",
+      "17d",
+      "17f",
+      "181",
+      "183",
+      "185",
+      "187",
+      "19b",
+      "19d"
+    ],
+    "bbox_covering_is_country_superset": true,
+    "google_total_bytes": 9285829234,
+    "unpublished_candidate_tiles": [
+      {
+        "token": "187",
+        "tile_index_entry": false,
+        "object_head_status": 404,
+        "gcs_object_metadata_status": 404,
+        "gcs_prefix_listing_has_items": false,
+        "observation": "The official v3 bucket does not publish this candidate object. No substituted dataset or empty placeholder is fabricated. This bbox-only candidate is southeast of the fixed Kenya boundary.",
+        "densified_s2_cell_disjoint_from_kenya": true,
+        "densified_s2_cell_distance_degrees": 0.16438119518431188,
+        "densification_note": "1000 normalized chord subdivisions per S2 great-circle edge, used as independent disjointness sanity check, not an exact symbolic geometry proof."
+      }
+    ]
+  },
+  "dataset": "kenya_buildings",
+  "schema_version": "vosma-kenya-sources-v1",
+  "crs": "+proj=laea +lat_0=0 +lon_0=37.5 +datum=WGS84 +x_0=0 +y_0=0 +units=m +no_defs",
+  "acquired_at_utc": "2026-09-21T17:51:04Z"
+}

kenya_buildings/dataset.json

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+{
+  "always_xy": true,
+  "build_protocol_sha256": "d626f297e5df999747cbedbb162eb034488afcb4afca857680d8f457a1996d77",
+  "columns": [
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+    "group_id",
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+    "weight"
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+  "coordinate_offset": {
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+    "dy": 516729472
+  },
+  "country_filter": "Projected full-geometry centroid covered by the same fixed projected national boundary; full footprints retained without clipping",
+  "dataset": "kenya_buildings",
+  "dependencies": {
+    "geos": "3.13.1",
+    "numpy": "1.26.4",
+    "proj": "9.5.1",
+    "pyarrow": "16.1.0",
+    "pyproj": "3.7.2",
+    "python": "3.12.7",
+    "shapely": "2.1.2"
+  },
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+  "group_count": 1,
+  "group_packing": "One national group; one shared integer translation after quantization of all retained R1/R2 bounds",
+  "group_states": {
+    "both_nonempty": 1
+  },
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+  "no_algorithm_index": true,
+  "no_confidence_threshold": true,
+  "no_deduplication": true,
+  "no_geometry_repair": true,
+  "no_pair_matching": true,
+  "projection": "+proj=laea +lat_0=0 +lon_0=37.5 +datum=WGS84 +x_0=0 +y_0=0 +units=m +no_defs",
+  "projection_datum": "WGS84",
+  "projection_wkt": "PROJCRS[\"unknown\",BASEGEOGCRS[\"unknown\",DATUM[\"World Geodetic System 1984\",ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ID[\"EPSG\",6326]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8901]]],CONVERSION[\"unknown\",METHOD[\"Lambert Azimuthal Equal Area\",ID[\"EPSG\",9820]],PARAMETER[\"Latitude of natural origin\",0,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8801]],PARAMETER[\"Longitude of natural origin\",37.5,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8802]],PARAMETER[\"False easting\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8806]],PARAMETER[\"False northing\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8807]]],CS[Cartesian,2],AXIS[\"(E)\",east,ORDER[1],LENGTHUNIT[\"metre\",1,ID[\"EPSG\",9001]]],AXIS[\"(N)\",north,ORDER[2],LENGTHUNIT[\"metre\",1,ID[\"EPSG\",9001]]]]",
+  "quantization": "Projected binary64 endpoints multiplied by 1000; nearest integer, ties to even (numpy.rint)",
+  "quantization_scale": 1000,
+  "r1_count": 12798990,
+  "r2_count": 25560434,
+  "record_order": "Frozen source lock order, then original record order; ids restart from zero per side",
+  "record_weights": "unit",
+  "rectangle_semantics": "half-open",
+  "schema_version": "vosma-real-rectangles-v1",
+  "source_crs": "EPSG:4326",
+  "source_lock_sha256": "fd20a649bb73f3ad21c0fd34e88bd00081207c7cdd0a48973439b6c079892709",
+  "within_group_cartesian_pairs": 327147739161660
+}

kenya_buildings/exclusions.jsonl.gz

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kenya_buildings/groups.parquet

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+size 9105

kenya_buildings/qa_local_overlay.json

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+{
+  "passed": true,
+  "dataset": "kenya_buildings",
+  "window_size_m": 200,
+  "r1_count": 12798990,
+  "r2_count": 25560434,
+  "maximum_records_per_side_per_window": 12,
+  "original_polygons_checked": 71,
+  "selection": "Each fixed half-open projected 200 m square independently selects up to the stated limit from each side by ascending published record_id, requiring the original projected polygon centroid to lie inside. No IoU, intersection, matching or appearance criterion is used.",
+  "scope": "Deterministic local visual QA only; not an estimate of national quality, overlap, density or weight distributions. Empty windows remain empty and are never relocated.",
+  "visual_crop": "Complete original projected geometries and complete Q1000 rectangles are drawn at their true common relative positions; panel axes visually crop to the fixed window. No data geometry is clipped or altered.",
+  "published_coordinates": "Rectangle corners are checked against the final NPY arrays after the dataset's common integer shift; displayed coordinates remove that same shift and subtract the fixed panel center in metres.",
+  "projection": "+proj=laea +lat_0=0 +lon_0=37.5 +datum=WGS84 +x_0=0 +y_0=0 +units=m +no_defs",
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+  "source_lock_sha256": "fd20a649bb73f3ad21c0fd34e88bd00081207c7cdd0a48973439b6c079892709",
+  "raw_source_verification": "Selected records are re-read from the frozen source files and checked against metadata and final arrays. Archive sizes are checked when available. The main build verifies full archive SHA256; this supplementary scan stops after each required maximum record rather than rehashing or reparsing complete archives.",
+  "metadata_rows_scanned": {
+    "r1": 12798990,
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+      "records_scanned": 11127819,
+      "physical_lines_scanned": 11127819,
+      "selected_source_records": 35
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+      "source_file": "google/179_buildings.csv.gz",
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+      "records_scanned": 389180,
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+  "regions": [
+    {
+      "name": "Nairobi",
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kenya_buildings/sources/SOURCE_REFERENCES.json

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+    "SOURCE_LOCK.json is authoritative for complete source URLs, immutable object generations and SHA-256 values.",
+    "No large upstream building archives are duplicated in sources/."
+  ]
+}

kenya_buildings/sources/google-187-prefix-list.json

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+{
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kenya_buildings/sources/google-extra-tiles.json

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kenya_buildings/sources/kenya-boundary-metadata.json

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+{"boundaryID": "KEN-ADM0-21065076", "boundaryISO": "KEN", "boundaryYear": "2020", "boundaryType": "ADM0", "boundarySource": "RCMRD GeoPortal, geoBoundaries", "boundaryCanonical": "Republic of Kenya", "boundaryLicense": "Public Domain", "licenseDetail": "", "licenseSource": "https//rcmrd.africageoportal.com/datasets/africageoportal", "boundarySourceURL": "https//rcmrd.africageoportal.com/datasets/africageoportal", "sourceDataUpdateDate": "Thu Jan 19 07:31:04 2023", "buildDate": "Dec 12, 2023", "admUnitCount": "1", "meanVertices": "30581.0", "minVertices": "30581", "maxVertices": "30581", "meanPerimeterLengthKM": "5536.641335689762", "maxPerimeterLengthKM": "5536.641335689762", "minPerimeterLengthKM": "5536.641335689762", "meanAreaSqKM": "590897.8133124034", "minAreaSqKM": "590897.8133124034", "maxAreaSqKM": "590897.8133124034"}

kenya_buildings/sources/kenya-boundary-metadata.txt

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+Usage of the geoBoundaries Database requires citation.:
+This can be satisfied by either:
+   1. (Preferred) - Citing our academic work: Runfola, D. et al. (2020)
+      geoBoundaries: A global database of political administrative boundaries.
+      PLoS ONE 15(4): e0231866. https://doi.org/10.1371/journal.pone.0231866
+   2. Providing a link to geoboundaries.org - i.e., 'Administrative boundaries courtesy 
+      of geoBoundaries.org'
+Additionally, we recommend citation of the source(s) noted in this metadata file.
+
+
+ISO-3166-1 (Alpha-3): KEN
+Boundary Representative of Year: 2020
+Boundary Type: ADM0
+Boundary Source(s): RCMRD GeoPortal, geoBoundaries
+Canonical Boundary Type Name: Republic of Kenya
+License: Public Domain
+License Notes: 
+License Source: https//rcmrd.africageoportal.com/datasets/africageoportal
+Data Source: https//rcmrd.africageoportal.com/datasets/africageoportal
+Source Data Updated On: Thu Jan 19 07:31:04 2023
+File Built On: Dec 12, 2023
+Number of Administrative Units: 1

kenya_buildings/sources/microsoft-readme.md

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+## Introduction
+
+Bing Maps is releasing open building footprints Nigeria and Kenya. We have detected 50.5 million buildings from Maxar imagery collected between 2020 and 2021. The data is freely available for download and use under applicable license.
+
+![sample footprints](images/footprints-sample.png)
+
+### Regions included 
+
+![building regions](images/country-overview.png)
+
+
+## License
+This data is licensed by Microsoft under the [Open Data Commons Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/).
+
+## FAQ
+### What does the data include?
+50,516,194 building footprint polygon geometries located in Kenya and Nigeria in line-delimited GeoJSON format. You may download the data in GeoJSON format here:
+
+| Location | Building Count | Link | Size (Compressed) |
+| :---: | :---: | :---: | :---: |
+| Nigeria | 35,767,509 | [Nigeria.geojsonl.zip](https://minedbuildings.z5.web.core.windows.net/legacy/africa/nigeria.geojsonl.zip) | 2.3GB |
+| Kenya | 14,748,685 | [Kenya.geojsonl.zip](https://minedbuildings.z5.web.core.windows.net/legacy/africa/kenya.geojsonl.zip) | 984MB |
+
+### What is the GeoJSON format?
+GeoJSON is a format for encoding a variety of geographic data structures. 
+For intensive documentation and tutorials, refer to [GeoJson blog](http://geojson.org/).
+
+### Why is the data being released?
+Microsoft has a continued interest in supporting a thriving OpenStreetMap ecosystem.
+
+### Should we import the data into OpenStreetMap?
+Maybe. Never overwrite the hard work of other contributors or blindly import data into OSM without first checking the local quality. While our metrics show that this data meets or exceeds the quality of hand-drawn building footprints, the data does vary in quality from place to place, between rural and urban, mountains and plains, and so on. Inspect quality locally and discuss an import plan with the community. Always follow the [OSM import community guidelines](https://wiki.openstreetmap.org/wiki/Import/Guidelines).
+
+### Will the data be used or made available in larger OpenStreetMap ecosystem?
+Yes. Currently Microsoft Open Buildings dataset is used in ml-enabler for task creation. You can try it out at [AI assisted Tasking Manager](https://tasks-assisted.hotosm.org/). The data will also be made available in Facebook [RapiD](https://mapwith.ai/rapid#background=Bing&disable_features=boundaries&map=2.00/0.0/0.0).
+
+### How did we create the data?
+The building extraction is done in two stages:
+1.	Semantic Segmentation – Recognizing building pixels on an aerial image using deep neural networks (DNNs)
+2.	Polygonization – Converting building pixel detections into polygons
+
+#### Stage1: Semantic Segmentation
+![segmenation diagram](images/segmentation.jpg)
+
+#### Stage 2: Polygonization
+![polygonization diagram](images/polygonization.jpg)
+
+### Were there any modeling improvements used for this release? 
+There were no changes in the model training process over previous releases. 
+
+### Evaluation set metrics
+The evaluation metrics are computed on the set of 2,500 building labels for Nigeria and 2,000 for Kenya.
+
+Building match metrics on the evaluation set:
+
+| Country | Precision | Recall |
+| :---: | :---: | :---: |
+| Kenya | 97.5% | 69.1% |
+| Nigeria | 95.3% | 69.% |
+
+We track following metrics to measure the quality of matched buildings in the evaluation set:
+1. Intersection over Union – This is a standard metric measuring the overlap quality against the labels
+2. Dominant angle rotation error – This measures the polygon rotation deviation
+
+| Country | IoU | Rotation error [deg] |
+| :---: | :---: |:---: |
+|  Kenya | 65.9% | 3.9 |
+|  Nigeria | 64.8% | 1.9 |
+
+
+### False positive ratio in the corpus
+
+We estimate 1.3-1.9% false positive ratio in 1,000 randomly sampled buildings from the entire output corpus.
+
+
+### What is the vintage of this data?
+Vintage of extracted building footprints depends on vintage of the underlying imagery. Underlying imagery is from Maxar between 2020 and 2021.
+
+### How good is the data?
+Our metrics show that in the vast majority of cases the quality is at least as good as hand digitized buildings in OpenStreetMap. It is not perfect, particularly in dense urban areas but it provides good recall in rural areas.
+
+### What is the coordinate reference system?
+EPSG: 4326
+
+### Will there be more data coming for other geographies?
+Maybe. This is a work in progress. Also, check out our other building releases!
+* [US](https://github.com/microsoft/USBuildingFootprints)
+* [Australia](https://github.com/microsoft/AustraliaBuildingFootprints)
+* [Canada](https://github.com/microsoft/CanadianBuildingFootprints)
+* [Uganda and Tanzania](https://github.com/microsoft/Uganda-Tanzania-Building-Footprints)
+* [South America](https://github.com/microsoft/SouthAmericaBuildingFootprints)
+
+<br>
+
+## Contributing
+
+This project welcomes contributions and suggestions.  Most contributions require you to agree to a
+Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
+the rights to use your contribution. For details, visit https://cla.microsoft.com.
+
+When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
+a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
+provided by the bot. You will only need to do this once across all repos using our CLA.
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
+contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+
+## Legal Notices
+
+Microsoft, Windows, Microsoft Azure and/or other Microsoft products and services referenced in the documentation
+may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries.
+The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks.
+Microsoft's general trademark guidelines can be found [here](http://go.microsoft.com/fwlink/?LinkID=254653).
+
+Privacy information can be found [here](https://privacy.microsoft.com/en-us/).
+
+Microsoft and any contributors reserve all others rights, whether under their respective copyrights, patents,
+or trademarks, whether by implication, estoppel or otherwise.

kenya_buildings/validation.json

@@ -0,0 +1,189 @@
+{
+  "passed": true,
+  "every_record_checked": true,
+  "dataset": "kenya_buildings",
+  "checks": [
+    "NPY schema and sequential IDs",
+    "unit weights and positive rectangles",
+    "one national group",
+    "metadata row alignment",
+    "finite source projected bounds",
+    "Q1000 ties-to-even endpoint quantization",
+    "identical integer shift on both sides",
+    "source-file/source-record and physical-line uniqueness",
+    "retained confidence metadata",
+    "all retained projected centroids covered by the fixed national boundary",
+    "source accounting",
+    "manifest and group bounds",
+    "original polygon projection QA"
+  ],
+  "whole_population": {
+    "r1": {
+      "records": 12798990,
+      "source_files": 1,
+      "records_with_retained_confidence": 0,
+      "source_record_uniqueness": "All source files occur in one block; record indexes and physical lines strictly increase within each source file",
+      "npy_sha256": "668afc2ca4eae70088ce668e458e048580fda2a8d4aaf551c033d5ef4a95940e"
+    },
+    "r2": {
+      "records": 25560434,
+      "source_files": 10,
+      "records_with_retained_confidence": 25560434,
+      "source_record_uniqueness": "All source files occur in one block; record indexes and physical lines strictly increase within each source file",
+      "npy_sha256": "f873331dd6c442bd808ddc09ef2e24cdb2ead7e436153d11aa32811f03980074"
+    }
+  },
+  "coordinate_offset": {
+    "dx": 399224818,
+    "dy": 516729472
+  },
+  "numeric_range": {
+    "max_coordinate": 1116765737,
+    "note": "Rectangle and cumulative-mass arithmetic in each sampler still require its own overflow checks."
+  },
+  "local_diagnostics": {
+    "scope": "Only the predeclared 10 km windows; not an estimate of national overlap count, density, or weight distributions.",
+    "selection": "Select each side independently by projected polygon centroid inside a half-open window; intersect the full un-clipped selected rectangles. Cross-window-boundary matches to omitted records are not counted.",
+    "does_not_filter_published_data": true,
+    "national_pairs_enumerated": false,
+    "pair_lists_saved": false,
+    "regions": [
+      {
+        "name": "Nairobi",
+        "center_wgs84_lon_lat": [
+          36.8219,
+          -1.2921
+        ],
+        "center_projected_m": [
+          -75471.03824561537,
+          -142872.73870625946
+        ],
+        "window_projected_m": [
+          -80471.03824561537,
+          -147872.73870625946,
+          -70471.03824561537,
+          -137872.73870625946
+        ],
+        "positive_pairs": 282139,
+        "quantile_sample_size": 50000,
+        "quantile_method": "uniform independent-priority sample",
+        "sample_seed": 20260922,
+        "intersection_area_m2": {
+          "min": 9.3e-05,
+          "max": 38540.1384,
+          "mean": 145.08487184245814,
+          "p05": 2.3021398,
+          "median": 47.7194225,
+          "p95": 543.6329135999997
+        },
+        "iou": {
+          "min": 1.47593307313814e-07,
+          "max": 0.9691918970160244,
+          "mean": 0.1382090827949209,
+          "p05": 0.002162639624048356,
+          "median": 0.052946231649960596,
+          "p95": 0.5887667553572272
+        },
+        "r1_count": 59797,
+        "r2_count": 171740,
+        "r1_no_overlap": 933,
+        "r2_no_overlap": 27467,
+        "r1_no_overlap_fraction": 0.015602789437597204,
+        "r2_no_overlap_fraction": 0.15993362058926283
+      },
+      {
+        "name": "Kisumu",
+        "center_wgs84_lon_lat": [
+          34.7617,
+          -0.0917
+        ],
+        "center_projected_m": [
+          -304796.861576017,
+          -10142.555795589731
+        ],
+        "window_projected_m": [
+          -309796.861576017,
+          -15142.555795589731,
+          -299796.861576017,
+          -5142.555795589731
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+        "positive_pairs": 162247,
+        "quantile_sample_size": 50000,
+        "quantile_method": "uniform independent-priority sample",
+        "sample_seed": 20260922,
+        "intersection_area_m2": {
+          "min": 0.000333,
+          "max": 27643.137168,
+          "mean": 81.27880855214633,
+          "p05": 1.4137422,
+          "median": 36.645067,
+          "p95": 287.1428511499999
+        },
+        "iou": {
+          "min": 1.1272686281140796e-06,
+          "max": 0.9876282015571037,
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+          "median": 0.13491235737294632,
+          "p95": 0.7657653802313356
+        },
+        "r1_count": 66191,
+        "r2_count": 117467,
+        "r1_no_overlap": 1870,
+        "r2_no_overlap": 24217,
+        "r1_no_overlap_fraction": 0.02825157498753607,
+        "r2_no_overlap_fraction": 0.20616002792273574
+      },
+      {
+        "name": "Turkana",
+        "center_wgs84_lon_lat": [
+          35.6,
+          3.1
+        ],
+        "center_projected_m": [
+          -211267.19905954832,
+          342788.8870027996
+        ],
+        "window_projected_m": [
+          -216267.19905954832,
+          337788.8870027996,
+          -206267.19905954832,
+          347788.8870027996
+        ],
+        "positive_pairs": 22614,
+        "quantile_sample_size": 22614,
+        "quantile_method": "all pairs",
+        "sample_seed": 20260922,
+        "intersection_area_m2": {
+          "min": 0.000836,
+          "max": 2804.009208,
+          "mean": 69.46577302838922,
+          "p05": 1.49396975,
+          "median": 27.7111695,
+          "p95": 274.07146319999947
+        },
+        "iou": {
+          "min": 2.8164767346444694e-06,
+          "max": 0.9494066553554955,
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+          "p05": 0.006471222548308523,
+          "median": 0.16392912531690806,
+          "p95": 0.5956761913882225
+        },
+        "r1_count": 14469,
+        "r2_count": 40929,
+        "r1_no_overlap": 1851,
+        "r2_no_overlap": 24095,
+        "r1_no_overlap_fraction": 0.12792867509848643,
+        "r2_no_overlap_fraction": 0.5887023870605195
+      }
+    ]
+  },
+  "polygon_qa": {
+    "checked_original_polygons": 44,
+    "overlay_polygons": 8,
+    "overlay": "qa_overlay.png",
+    "projection_recomputed": true,
+    "scope": "Deterministic source samples; not a random national quality estimate"
+  }
+}