Hugging Face's logo

Datasets:
ethankf
/
loci-data

Tasks:
Image Feature Extraction
Languages:
English
ArXiv:
Tags:
cross-view-localization
geolocalization
robotics
openstreetmap
satellite-imagery
License:
Dataset card Files
xet
 Community
loci-data / README.md
ethankf's picture
ethankf
Consolidate terms into LICENSE (drop duplicate TERMS_OF_USE.md)
fe8bbe0 verified
preview code
|
Raw
History Blame Contribute Delete
10.2 kB
metadata 
license: other
license_name: loci-dataset-terms
pretty_name: LOCI Dataset
language:
  - en
task_categories:
  - image-feature-extraction
tags:
  - cross-view-localization
  - geolocalization
  - robotics
  - openstreetmap
  - satellite-imagery
extra_gated_prompt: >-
  By requesting access you agree to the LOCI Dataset Terms of Use (the LICENSE
  file in this repository), including its per-component licenses (ODbL 1.0 for
  OpenStreetMap-derived tables, CC BY-SA 4.0 for street-level imagery and
  derived annotations, CC BY 4.0 for checkpoints and evaluation artifacts,
  public-record terms for MassGIS/CT imagery) and its privacy clause: you will
  not use the Dataset in any manner to identify or invade the privacy of any
  person whose personal data may have been incidentally collected.

LOCI Dataset

Dataset release for Leveraging Semantic Maps for City-Scale Cross-View Localization (arXiv:2607.25215). Code, docs, and reproduction guides live in the code repository linked from the paper.

Every file is listed with its sha256 in RELEASE_MANIFEST.sha256.

Overview

Folder What's inside Size
satellite/ Redistributable satellite imagery (public-record sources), per-city tars + pinned patch manifests 9.2 GB
panoramas/ Street-level panoramas for the 8 trajectory environments (self-collected + Mapillary) 22 GB
osm_landmarks/ OSM landmark tables, the exact per-environment versions used in the paper 254 MB
annotations/ VLM-extracted panorama landmark annotations + embeddings, 11 environments 4.0 GB
correspondence/ VLM-labeled correspondence dataset (Chicago/Seattle) + shared text-value embeddings 0.9 GB
checkpoints/ Trained model weights (DINOv3 backbone stripped — see reassembly below) 0.5 GB
final_results/ The paper's evaluation outputs 2.0 GB
evaluation_paths/ The exact evaluation path files (pano-id sequences) 115 MB
verification/ Similarity-matrix fingerprints + patch manifests for imagery that is not redistributed 437 MB
osm_baseline/ Baked vector map tiles behind the WAG+OSM baseline 58 MB
human_eval_labels/ Raw human labels behind the paper's Table IV 3 MB

satellite/

File Contents Size
framingham.tar 40,401 patches, 640×640 (MassGIS 2025 aerial, 15 cm), VIGOR grid 4.3 GB
middletown.tar 39,601 patches, 640×640 (CT 2023 orthoimagery, 7.6 cm) 4.9 GB
*.patch_manifest.json Per-patch pixel sha256 + pinned source/grid 10 MB ea.

Only public-record imagery is redistributed here. Satellite imagery for the other environments (Esri World Imagery, VIGOR) ships as pinned patch manifests under verification/ instead — see below.

panoramas/

File Contents Size
boston_snowy.tar 1,674 GPS-stamped panoramas, 2048×1024 (as evaluated in the paper), collected during an active snowstorm 0.6 GB
boston_night.tar 1,378 panoramas, 7680×3840, night collect of the same route 5.9 GB
framingham.tar 478 panoramas, 4096×2048 (Mapillary) 1.3 GB
middletown.tar 263 panoramas, 5640×2820 (Mapillary, rain) 0.8 GB
san_francisco_mapillary.tar 300 panoramas, 5760×2880 (Mapillary) 1.0 GB
fort_myers.tar 1,073 panoramas, 12288×6144 (Mapillary, post-Hurricane-Ian) 6.8 GB
noordoostpolder.tar 1,916 panoramas, 4096×2048 (Mapillary) 1.9 GB
veluwe.tar 3,654 panoramas, 4096×2048 (Mapillary) 4.6 GB

Each tar contains <city>/panorama/*.jpg (filenames embed {pano_id},{lat},{lon}) plus pano_id_mapping.csv and the collection pipeline's provenance files (extraction_log.csv, pipeline_metadata.json) where applicable. Panoramas for the VIGOR train/eval cities (chicago, seattle, new_york) are not redistributed — obtain the VIGOR dataset separately.

osm_landmarks/

<city>/<version>.feather — one dated Geofabrik-derived OSM landmark table per environment (ODbL), the exact versions used in the paper. boston.feather appears under both Boston environments (shared table).

annotations/

<city>.tar (11 environments) — VLM-extracted panorama landmark annotations: Gemini batch outputs (sentences/results/**/predictions.jsonl, image payloads replaced with sha256: markers) plus embeddings/embeddings.pkl per environment. Records are keyed by panorama stem ({id},{lat},{lon},) and correspond 1:1 with the released panoramas.

correspondence/

File Contents Size
labels.tar VLM-labeled correspondence pairs (text-only Gemini batch outputs) 221 MB
text_value_embeddings.pkl text-embedding-005 value embeddings (768-d, 206,277 entries) serving classifier training, matrix export, and LOCI-EF training; covers every text value in the released landmark tables and annotations 645 MB

See correspondence/README.md for details.

checkpoints/

Model Contents Size
wag/, wag_plus_osm/ Trained weights per side (best_panorama/, best_satellite/ — the layout --checkpoint best expects), DINOv3 backbone stripped, + weights_manifest.json (per-tensor sha256 incl. the backbone), a VIGOR-free input_output.tar consistency anchor, and train_config.yaml ~72 MB ea.
loci_ef/ Same layout; transformer + tag-bundle encoders + frozen SAFA (backbone stripped) ~370 MB
correspondence_classifier/ best_model.pt (674 KB, complete — no backbone) + config.yaml <1 MB

Reassembly: the released weights omit the frozen DINOv3 backbone (obtained from Meta under the DINOv3 License) and the pickled model.pt. Run bazel run //tools:reassemble_checkpoints -- --data-root <this download> from the code release: it rebuilds each model from train_config.yaml (torch.hub downloads DINOv3), verifies every tensor — including the downloaded backbone — against weights_manifest.json, and writes model.pt + full model_weights.pt in place. Released configs use a literal {DATA_ROOT} placeholder resolved by the tool. After reassembly the released training/eval entry points load these checkpoints unchanged.

final_results/

{wag,wag_plus_osm,loci,loci_ef}.tar (0.53 GB each) — the paper's evaluation outputs: per-path {error, mode_error, prob_mass_by_radius, path, var, distance_traveled_m}.pt for 1,000 paths per environment (5,000 for New York and Seattle) across all 10 evaluation environments plus Seattle (calibration), with per-environment summary_statistics.json and the exact eval configs (args.json, aggregator_config.yaml). Every average_final_error in the shipped summary_statistics.json matches Table V of the paper exactly.

sigma_calibrations/ holds the four Seattle sigma-calibration fits (JSON + args) used by the eval configs.

The environment set includes framingham_mixed_sat — the paper's "Framingham Mixed-Sat" row. That row's satellite imagery (a Google mosaic) can not be redistributed. All other environments' matrices are regenerable from the released checkpoints and imagery. Code is provided to collect the current google mosaic of Framingham, but this may drift over time.

evaluation_paths/

<env>.json — the paper's evaluation path files: 1,000 3-km paths per trajectory environment; 5,000 5-km goal-directed paths for New York and Seattle. Paths are pano-id sequences; the 8 trajectory files reference exactly the released panoramas (the Seattle/New York files reference VIGOR pano ids).

verification/

fingerprints/<env>__<matrix>.fingerprint.npz — numerical fingerprints for every regenerable similarity matrix (10 environments × 4 matrices): quantiles, seeded samples, random-projection sketch, per-row top-k. Check regenerated matrices with the code release's tools/verify_artifacts.py matrix (tolerance/rank-based — GPU float nondeterminism means bit-exact comparison is not expected).

patch_manifests/<env>.patch_manifest.json — per-patch decoded-pixel sha256 + pinned source/grid for the satellite imagery that is not redistributed: Esri-pinned manifests (boston_snowy/boston_night, fort_myers, noordoostpolder, veluwe) drive bit-exact re-download via download_tiles.py --manifest; foreign-source manifests (chicago, new_york, seattle, san_francisco_mapillary) verify a user's own VIGOR copy.

boston_snowy and boston_night share the same satellite imagery (their manifests are identical) — download once and reuse. framingham / middletown canonical patch manifests live with their imagery under satellite/. framingham_mixed_sat has no fingerprints (see final_results/ above).

osm_baseline/

<region>.mbtiles — baked vector tiles (planetiler, OpenMapTiles schema) behind the WAG+OSM baseline: the exact paper-era bakes from the pinned dated Geofabrik dumps, one per environment plus illinois / washington / new_york for the VIGOR train/eval cities. Rasterize into <city>/satellite_osm/ with the code release's render_osm_tiles.py; boston_night reuses boston_snowy's renders (same grid).

human_eval_labels/

annotations/ — per-rater judgments (yes/minor/no) on 500 VLM-extracted panorama annotations per city (Chicago/Seattle) plus third-pass consensus for the 87 disagreements. correspondence/ — 1,000 blind same/different judgments per rater per city on proposed correspondence pairs plus the full 2,000-pair consensus (112 adjudicated).

Table IV is reproducible from these files digit-for-digit: annotation rows (n=500 per city: Chicago 83.6/4.2/12.2 κ 0.70, Seattle 78.2/5.0/16.8 κ 0.75) and correspondence metrics (κ 0.88/0.90; P/R/F1 0.976/0.859/0.914 Chicago, 0.976/0.850/0.909 Seattle).

Licensing

Composite — per-component licenses are listed in LICENSE: ODbL 1.0 (OpenStreetMap-derived tables and the correspondence dataset), CC BY-SA 4.0 (street-level imagery and VLM annotations), CC BY 4.0 (checkpoints and evaluation artifacts), and public-record terms for the MassGIS / CT ECO imagery. Esri World Imagery satellite tiles and VIGOR imagery are not redistributed; pinned patch manifests allow re-download.