#!/usr/bin/env python3 """ generate_stac.py ================ Creates STAC 1.0.0 metadata for the MatchGeo-DEM dataset. Outputs: - stac/collection.json — STAC Collection for the whole dataset - stac/items/{city_id}.json — STAC Item per city (merged DEM + tile assets) Requires: rasterio, shapely (optional but recommended) Install: pip install rasterio shapely Usage: python generate_stac.py --data-root /path/to/MatchGeo-DEM-v1/data """ import json import argparse from pathlib import Path from datetime import datetime from collections import OrderedDict try: import rasterio from rasterio.crs import CRS HAS_RASTERIO = True except ImportError: HAS_RASTERIO = False try: from shapely.geometry import box, mapping HAS_SHAPELY = True except ImportError: HAS_SHAPELY = False # ------------------------------------------------------------------ # Static dataset catalog (synchronized with manifest.json) # ------------------------------------------------------------------ CITIES = [ {"id": "ATA_MV", "name": "Mount Athos, Greece", "epsg": 3031, "resolution": 2.0, "method": "satellite_insar", "n_tiles": 5625, "labelled": False, "year_start": 2011, "year_end": 2015, "provider": "Copernicus DEM"}, {"id": "BRA_SP", "name": "São Paulo, Brazil", "epsg": 31983, "resolution": 0.5, "method": "airborne_lidar", "n_tiles": 558, "labelled": True, "year_start": 2020, "year_end": 2020, "provider": "GeoSampa"}, {"id": "CHN_WS", "name": "Wutai Shan, China", "epsg": 32649, "resolution": 1.0, "method": "uav_sfm", "n_tiles": 1076, "labelled": False, "year_start": 2021, "year_end": 2021, "provider": "OpenTopography"}, {"id": "ESP_EH", "name": "El Hierro, Spain", "epsg": 3040, "resolution": 1.0, "method": "airborne_lidar", "n_tiles": 2460, "labelled": False, "year_start": 2022, "year_end": 2025, "provider": "PNOA-LiDAR"}, {"id": "FIN_LM", "name": "Lahti, Finland", "epsg": 3067, "resolution": 2.0, "method": "airborne_lidar_photogrammetry", "n_tiles": 248, "labelled": False, "year_start": 2020, "year_end": 2026, "provider": "National Land Survey of Finland"}, {"id": "GER_BN", "name": "Bonn, Germany", "epsg": 25832, "resolution": 1.0, "method": "airborne_lidar", "n_tiles": 1759, "labelled": True, "year_start": 2016, "year_end": 2018, "provider": "Geobasis NRW"}, {"id": "IDN_SV", "name": "Sinabung Volcano, Indonesia", "epsg": 32647, "resolution": 0.87, "method": "uas_sfm", "n_tiles": 181, "labelled": False, "year_start": 2018, "year_end": 2018, "provider": "OpenTopography"}, {"id": "KAZ_AC", "name": "Almaty City, Kazakhstan", "epsg": 32643, "resolution": 1.0, "method": "satellite_stereophotogrammetry", "n_tiles": 887, "labelled": False, "year_start": 2017, "year_end": 2017, "provider": "OpenTopography"}, {"id": "KSA_WA", "name": "Wadi Al-Akhdar, Saudi Arabia", "epsg": 32637, "resolution": 1.6, "method": "satellite_stereophotogrammetry", "n_tiles": 3880, "labelled": False, "year_start": 2016, "year_end": 2016, "provider": "OpenTopography"}, {"id": "NAM_HF", "name": "Hebron Fault, Namibia", "epsg": 32733, "resolution": 0.53, "method": "satellite_stereophotogrammetry", "n_tiles": 1457, "labelled": False, "year_start": 2017, "year_end": 2017, "provider": "OpenTopography"}, {"id": "NZL_KP", "name": "Kapiti Coast, New Zealand", "epsg": 2193, "resolution": 1.0, "method": "airborne_lidar", "n_tiles": 1776, "labelled": False, "year_start": 2010, "year_end": 2025, "provider": "LINZ"}, {"id": "PHL_TA", "name": "Tarlac, Philippines", "epsg": 32651, "resolution": 1.0, "method": "airborne_lidar", "n_tiles": 286, "labelled": False, "year_start": 2014, "year_end": 2017, "provider": "LiPAD"}, {"id": "USA_GC", "name": "Grand Canyon, United States", "epsg": 6341, "resolution": 10.0, "method": "lidar_ifsar", "n_tiles": 600, "labelled": False, "year_start": 2020, "year_end": 2026, "provider": "USGS 3DEP"}, ] # ------------------------------------------------------------------ # Helpers # ------------------------------------------------------------------ def read_raster_bounds(tif_path): """Return (bbox, crs_wkt, width, height) from a GeoTIFF.""" if not HAS_RASTERIO: return None, None, None, None try: with rasterio.open(tif_path) as src: bounds = src.bounds bbox = [bounds.left, bounds.bottom, bounds.right, bounds.top] return bbox, src.crs.to_wkt(), src.width, src.height except Exception as e: print(f" ⚠️ Could not read {tif_path}: {e}") return None, None, None, None def bbox_to_geometry(bbox): """Convert [minx, miny, maxx, maxy] to GeoJSON Polygon dict.""" if HAS_SHAPELY and bbox: return mapping(box(*bbox)) # Fallback manual geometry if bbox: return { "type": "Polygon", "coordinates": [[ [bbox[0], bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]], [bbox[0], bbox[1]] ]] } return None def build_collection(data_root, output_dir): """Build the STAC Collection JSON.""" collection = OrderedDict() collection["type"] = "Collection" collection["stac_version"] = "1.0.0" collection["id"] = "matchgeo-dem-v1" collection["title"] = "MatchGeo: Multi-City DEM Dataset for Local Feature Matching" collection["description"] = ( "MatchGeo is a curated, multi-city Digital Elevation Model (DEM) dataset " "designed for training and benchmarking local feature matching algorithms " "in urban and natural terrain analysis. It aggregates high-resolution elevation " "data from 13 distinct environments across 6 continents." ) collection["license"] = "CC-BY-4.0" collection["keywords"] = [ "DEM", "DSM", "elevation", "local feature matching", "computer vision", "geospatial", "LiDAR", "photogrammetry" ] collection["providers"] = [ { "name": "Correa, S. P. L. P.; Santos, A. de Paula; Oliveira, H. N.; Beltons, D.", "roles": ["producer", "licensor"], "url": "https://doi.org/10.5281/zenodo.19339008" } ] collection["extent"] = { "spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": { "interval": [["2011-01-01T00:00:00Z", "2026-12-31T23:59:59Z"]] } } collection["links"] = [ {"rel": "self", "href": "./collection.json", "type": "application/json"}, {"rel": "root", "href": "./collection.json", "type": "application/json"}, {"rel": "license", "href": "../LICENSE", "type": "text/plain"}, {"rel": "cite-as", "href": "https://doi.org/10.5281/zenodo.19339008", "type": "text/html"} ] # Summaries collection["summaries"] = { "gsd": [0.5, 0.53, 0.87, 1.0, 1.6, 2.0, 10.0], "eo:bands": [{"name": "elevation", "common_name": "elevation", "unit": "meter"}] } # Assets collection["assets"] = { "manifest": { "href": "../manifest.json", "type": "application/json", "title": "Central dataset manifest (JSON-LD)" }, "dataset_description": { "href": "../DATASET_DESCRIPTION.md", "type": "text/markdown", "title": "FAIR-compliant dataset description" } } out_path = output_dir / "collection.json" out_path.write_text(json.dumps(collection, indent=2), encoding="utf-8") print(f"✅ Collection written: {out_path}") return collection def build_item(city, data_root, output_dir): """Build a STAC Item for one city.""" city_id = city["id"] city_dir = Path(data_root) / city_id merged_tif = city_dir / f"{city_id}.tif" tiles_dir = city_dir / "tiles" anno_dir = city_dir / "annotations" has_annotations = anno_dir.exists() and any(anno_dir.iterdir()) # Read merged DEM bounds bbox, crs_wkt, width, height = read_raster_bounds(merged_tif) geometry = bbox_to_geometry(bbox) # Date handling year_start = city.get("year_start", 2020) year_end = city.get("year_end", 2020) dt_start = f"{year_start}-01-01T00:00:00Z" dt_end = f"{year_end}-12-31T23:59:59Z" item = OrderedDict() item["type"] = "Feature" item["stac_version"] = "1.0.0" item["id"] = city_id item["collection"] = "matchgeo-dem-v1" item["bbox"] = bbox if bbox else [-180, -90, 180, 90] item["geometry"] = geometry if geometry else {"type": "Polygon", "coordinates": [[]]} item["properties"] = { "title": city["name"], "description": f"{city['name']} — {city['method']} at {city['resolution']} m resolution", "datetime": dt_start, "start_datetime": dt_start, "end_datetime": dt_end, "providers": [{"name": city["provider"], "roles": ["producer"]}], "gsd": city["resolution"], "proj:epsg": city["epsg"], "matchgeo:method": city["method"], "matchgeo:n_tiles": city["n_tiles"], "matchgeo:labelled": city["labelled"], "matchgeo:has_annotations": has_annotations, } if crs_wkt: item["properties"]["proj:wkt2"] = crs_wkt # Assets item["assets"] = {} if merged_tif.exists(): item["assets"]["dem"] = { "href": str(merged_tif.relative_to(Path(data_root).parent)), "type": "image/tiff; application=geotiff; profile=cloud-optimized", "title": f"Merged DEM — {city_id}", "roles": ["data"], "eo:bands": [{"name": "elevation", "common_name": "elevation", "unit": "meter"}] } if tiles_dir.exists(): item["assets"]["tiles"] = { "href": str(tiles_dir.relative_to(Path(data_root).parent)) + "/", "type": "application/x-geotiff-tiles", "title": f"333×333 pixel tiles — {city_id}", "roles": ["data"], "x-asset-count": city["n_tiles"] } extent_geojson = city_dir / f"{city_id}_extent.geojson" if extent_geojson.exists(): item["assets"]["extent"] = { "href": str(extent_geojson.relative_to(Path(data_root).parent)), "type": "application/geo+json", "title": "Coverage extent polygon", "roles": ["metadata"] } tiles_geojson = city_dir / f"{city_id}_tiles.geojson" if tiles_geojson.exists(): item["assets"]["tile_index"] = { "href": str(tiles_geojson.relative_to(Path(data_root).parent)), "type": "application/geo+json", "title": "Tile index (grid)", "roles": ["metadata"] } meta_json = city_dir / f"{city_id}_metadata.json" if meta_json.exists(): item["assets"]["metadata"] = { "href": str(meta_json.relative_to(Path(data_root).parent)), "type": "application/json", "title": "ISO 19115-2 + OGC 23-008r3 metadata", "roles": ["metadata"] } if has_annotations: item["assets"]["annotations"] = { "href": str(anno_dir.relative_to(Path(data_root).parent)) + "/", "type": "application/json", "title": "Keypoint annotations", "roles": ["metadata"] } item["links"] = [ {"rel": "self", "href": f"./{city_id}.json", "type": "application/json"}, {"rel": "collection", "href": "../collection.json", "type": "application/json"}, {"rel": "root", "href": "../collection.json", "type": "application/json"} ] out_path = output_dir / "items" / f"{city_id}.json" out_path.parent.mkdir(parents=True, exist_ok=True) out_path.write_text(json.dumps(item, indent=2), encoding="utf-8") print(f" ✅ Item written: {out_path}") return item def main(): parser = argparse.ArgumentParser(description="Generate STAC metadata for MatchGeo-DEM") parser.add_argument("--data-root", required=True, help="Path to MatchGeo-DEM-v1/data/") parser.add_argument("--output", default="stac", help="Output directory for STAC files") args = parser.parse_args() data_root = Path(args.data_root) output_dir = Path(args.output) output_dir.mkdir(parents=True, exist_ok=True) print("=" * 60) print("MatchGeo-DEM STAC Generator v1.0") print("=" * 60) # Build collection print("\n📦 Building Collection...") collection = build_collection(data_root, output_dir) # Build items print("\n🗺️ Building Items...") for city in CITIES: build_item(city, data_root, output_dir) # Update collection links with item references for city in CITIES: collection["links"].append({ "rel": "item", "href": f"./items/{city['id']}.json", "type": "application/json" }) # Rewrite collection with item links (output_dir / "collection.json").write_text( json.dumps(collection, indent=2), encoding="utf-8" ) print("\n" + "=" * 60) print("✅ STAC metadata complete!") print(f" Collection: {output_dir / 'collection.json'}") print(f" Items: {output_dir / 'items/'}") print("=" * 60) if __name__ == "__main__": main()