Initial version

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.db filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,131 @@
 ---
 license: odbl
+pretty_name: OSMGraphCLIP-MS training dataset
+tags:
+  - openstreetmap
+  - geospatial
+  - graph
+  - location-encoding
+  - remote-sensing
+  - contrastive-learning
+  - clip
+size_categories:
+  - 100K<n<1M
 ---
+
+# OSMGraphCLIP-MS
+
+Training dataset for the **MS** (multiscale) variant of [OSMGraphCLIP](https://github.com/d-michail/osmgraphclip): *"OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs"* ([arXiv:2606.08046](https://arxiv.org/abs/2606.08046)).
+
+It contains ~200k globally-diverse `(lat, lon)` locations, and for each location:
+
+- a **heterogeneous OSM graph** (points, lines, polygons — roads, buildings, land use, POIs — with SBERT node features), and
+- **multiscale concentric-ring "band" features** summarizing OSM content in rings around the location at multiple radii.
+
+These pairs (graph + coordinate, with the band features as auxiliary multiscale signal) are what the graph encoder (`OSMHeteroGAT`) and location encoder (`LocationEncoder`) are contrastively aligned on. This dataset was used to train:
+
+| Model | HuggingFace |
+|---|---|
+| OSMGraphCLIP-MS-L40 | [d-michail/OSMGraphCLIP-MS-L40](https://huggingface.co/d-michail/OSMGraphCLIP-MS-L40) |
+| OSMGraphCLIP-MS-L10 | [d-michail/OSMGraphCLIP-MS-L10](https://huggingface.co/d-michail/OSMGraphCLIP-MS-L10) |
+
+Code to build datasets in this format, and to train on them, is in the [osmgraphclip](https://github.com/d-michail/osmgraphclip) repo (`create_dataset.py`, `create_multiscale_dataset.py`, `create_graphs.py`, `train.py`).
+
+## Subsets
+
+The dataset is split into two location sets, matching the two location CSVs in the code repo:
+
+| Subset | Locations | Source | Samples with no OSM data |
+|---|---|---|---|
+| `satclip_ms/` | 100,000 | `data/satclip_locations.csv` — primary location set | 1,076 |
+| `h3_ms/` | 99,260 | `data/h3_locations.csv` — globally-diverse H3-sampled locations | 645 |
+
+Each subset is independent and has the same internal layout.
+
+## Repo layout
+
+```
+{h3_ms,satclip_ms}/
+├── metadata.json           # generation config for this subset (see below)
+├── dataset.db               # SQLite index (see below)
+├── graphs.zip-parts/        # sharded graphs/ folder (~1 GiB zip shards)
+│   ├── graphs-000.zip
+│   ├── graphs-001.zip
+│   └── ...
+└── bands.zip-parts/         # sharded bands/ folder (~1 GiB zip shards)
+    ├── bands-000.zip
+    └── ...
+```
+
+The raw `graphs/` and `bands/` folders are **not** stored directly in the repo (hundreds of thousands of small files each — unfriendly to git/HF). They are shipped as a sequence of zip shards instead. Each shard stores entries as `<subfolder>/<filename>`, so unzipping **every** shard for a given subset/subfolder combo into that subset's root directory reconstructs the original folder exactly, with no overlap between shards.
+
+### Reconstructing `graphs/` and `bands/`
+
+Use `unpack_shards.py` from this repo:
+
+```bash
+python3 unpack_shards.py h3_ms satclip_ms
+```
+
+This walks the given root(s), finds every `*.zip-parts/` directory, and unzips all shards inside it into the parent directory — reconstructing `h3_ms/graphs/`, `h3_ms/bands/`, `satclip_ms/graphs/`, `satclip_ms/bands/`. It's safe to re-run.
+
+## `graphs/` contents
+
+For each location, identified by an integer id `<id>`:
+
+- `osm_<id>_graph.pkl` — a pickled [`torch_geometric.data.HeteroData`](https://pytorch-geometric.readthedocs.io/) object: the heterogeneous OSM graph with node types `polygon` (392-dim features), `line` (390-dim), `point` (386-dim), and all 9 directed edge-type combinations between them (`edge_index` + `edge_attr`). Node features are SBERT (`all-MiniLM-L6-v2`, 384-dim) embeddings of OSM tags augmented with per-type geometric attributes. Built with the GeoLink-derived `osm_to_graph.py` pipeline (`graph_method: geolink`).
+- `osm_<id>_{point,linestring,multilinestring,polygon,multipolygon}.geojson.gz` — the raw gzipped OSM GeoJSON geometries (with tags) that the graph for that location was built from. Not every geometry type is present for every location.
+- `osm_<id>.nodata` — present instead of the geojson files when no OSM data was found in the location's bounding box. The corresponding graph is still written (an empty `HeteroData`, `method = "zero"` in `dataset.db`), so `<id>` always has a valid graph pickle — `.nodata` just flags "empty, not missing/corrupted".
+
+## `bands/` contents
+
+- `osm_<id>_bands.npz` — multiscale concentric-ring band features at radii `[2000, 10000, 20000]` meters (see `band_radii_m` in `metadata.json`), keyed by:
+  - `band_radii` — the 3 radii, in meters
+  - `spatial_features` `(3, 47)` + `spatial_feature_names` — per-band aggregate spatial statistics
+  - `subbin_spatial` `(3, 2, 16)` + `subbin_feature_names` — per-band, per-subbin (inner/outer half of the ring) spatial statistics
+  - `sector_spatial` `(3, 4, 11)` + `sector_feature_names` — per-band, per-sector (quadrant) spatial statistics
+  - `global_embeddings` `(3, 384)`, `subbin_embeddings` `(3, 2, 384)`, `sector_embeddings` `(3, 4, 384)` — SBERT embeddings of OSM tags aggregated at the whole-band / subbin / sector level
+
+These give the graph encoder multiscale context beyond the single bounding box used for the main graph.
+
+## `dataset.db`
+
+A SQLite database indexing every location by id, with three tables (same `id`s line up across tables and against the `osm_<id>_*` filenames):
+
+- **`downloads`**: `lat`, `lon`, `bbox_size`, `geojson_prefix` (the `osm_<id>` prefix), `timestamp` — one row per raw OSM download.
+- **`graphs`**: `lat`, `lon`, `bbox_size`, `graph_pickle` (filename), `method` (`geolink` or `zero`), `timestamp` — one row per built graph.
+- **`band_features`**: `lat`, `lon`, `bands_path` (filename), `band_radii`, `timestamp` — one row per band-feature file.
+
+## `metadata.json`
+
+Generation config shared by every sample in the subset:
+
+```json
+{
+  "bbox_size_m": 1000,
+  "band_radii_m": [2000.0, 10000.0, 20000.0],
+  "location_source": "data/h3_locations.csv",   // or data/satclip_locations.csv
+  "tagw_path": "data/all_tags30_frequency1.json",
+  "embedding_backend": "sbert",
+  "graph_method": "geolink"
+}
+```
+
+## Citation
+
+```bibtex
+@misc{michail2026osmgraphcliplearninggloballocation,
+      title={OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs},
+      author={Dimitrios Michail and Eleni Saka and Ioannis Giannopoulos and Ioannis Papoutsis},
+      year={2026},
+      eprint={2606.08046},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2606.08046},
+}
+```
+
+## License and acknowledgements
+
+Contains data from [OpenStreetMap](https://www.openstreetmap.org/copyright), © OpenStreetMap contributors, available under the [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/).
+

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h3_ms/metadata.json

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+{
+  "bbox_size_m": 1000,
+  "band_radii_m": [
+    2000.0,
+    10000.0,
+    20000.0
+  ],
+  "location_source": "data/h3_locations/h3_locations.csv",
+  "embedding_model": null,
+  "tagw_path": "data/all_tags30_frequency1.json",
+  "embedding_backend": "sbert",
+  "graph_method": "geolink"
+}

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satclip_ms/metadata.json

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+{
+  "bbox_size_m": 1000,
+  "band_radii_m": [
+    2000.0,
+    10000.0,
+    20000.0
+  ],
+  "location_source": "data/satclip_locations.csv",
+  "embedding_model": null,
+  "tagw_path": "data/all_tags30_frequency1.json",
+  "embedding_backend": "sbert",
+  "graph_method": "geolink"
+}

unpack_shards.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+"""Reassemble sharded folders produced by make_shards.py.
+
+Finds every "<name>.zip-parts/" directory under the given root(s) and
+unzips all shard zips inside it into the parent directory, reconstructing
+the original "<name>/" folder. Safe to re-run (existing files are
+overwritten with -o).
+
+Usage:
+    python3 unpack_shards.py [root ...]
+
+With no arguments, searches the current directory.
+"""
+import glob
+import os
+import subprocess
+import sys
+
+
+def find_zip_parts_dirs(root):
+    matches = []
+    for dirpath, dirnames, _ in os.walk(root):
+        for d in list(dirnames):
+            if d.endswith(".zip-parts"):
+                matches.append(os.path.join(dirpath, d))
+                dirnames.remove(d)  # no need to descend into it
+    return sorted(matches)
+
+
+def unpack(zip_parts_dir):
+    dest = os.path.dirname(zip_parts_dir)
+    shards = sorted(glob.glob(os.path.join(zip_parts_dir, "*.zip")))
+    if not shards:
+        print(f"  no shards found in {zip_parts_dir}, skipping")
+        return
+    name = os.path.basename(zip_parts_dir)[: -len(".zip-parts")]
+    print(f"=== {zip_parts_dir} -> {os.path.join(dest, name)}/ ({len(shards)} shard(s)) ===")
+    for shard in shards:
+        print(f"  extracting {os.path.basename(shard)}")
+        subprocess.run(["unzip", "-oq", shard, "-d", dest], check=True)
+    print("  done")
+
+
+def main():
+    roots = sys.argv[1:] or ["."]
+    zip_parts_dirs = []
+    for root in roots:
+        zip_parts_dirs.extend(find_zip_parts_dirs(root))
+
+    if not zip_parts_dirs:
+        print("No *.zip-parts directories found.")
+        return
+
+    for zip_parts_dir in zip_parts_dirs:
+        unpack(zip_parts_dir)
+
+
+if __name__ == "__main__":
+    main()