open-gis

Production-grade geospatial workflows with an open-first stack and pragmatic hosted/SaaS choices when global scale, latency, SLA, or data quality makes local processing a poor fit. Cloud-native by default: STAC for discovery, GeoParquet + COG + PMTiles for storage, DuckDB and PostGIS for compute, MapLibre and Martin for delivery.

| If the task involves... | Read | |---|---| | Finding or sourcing data (OSM, Overture, Sentinel, Landsat, building footprints, regional portals, STAC catalogs, MCP-based discovery) | references/data-sources.md | | Choosing local processing vs online/hosted/SaaS services for global or continental scale; basemaps, elevation, routing, geocoding, place search, postcode lookup APIs | references/services-and-scale.md | | Choosing a format, converting between formats, or any CRS / projection / EPSG question | references/formats-and-crs.md | | Running GDAL/OGR, GeoPandas, xarray, DuckDB, PostGIS, or PDAL — the actual processing | references/processing.md | | Vector analytics, raster analytics, terrain/hydrology, network analysis, point cloud workflows | references/analytics.md | | Tile generation (PMTiles, MVT), tile servers (Martin, TiTiler), web map rendering (MapLibre, deck.gl) | references/web-delivery.md | | QGIS desktop, QGIS plugin ecosystem, QGIS MCP, PyQGIS scripting, Processing toolbox | references/qgis.md | | Reproducibility, validation, license attribution, tile smoke tests, deployment checks | references/validation-and-ops.md | For simple one-shot questions (single CRS conversion, one ogr2ogr invocation), the relevant reference alone is usually enough. For multi-stage pipelines, read data-sources.md and processing.md together; for end-to-end "from raw data to web map" tasks, also read web-delivery.md.

• Storage formats: GeoParquet (vector analytics), COG (raster), PMTiles (tile delivery), GeoPackage (desktop interchange). Never produce Shapefile as new output. • CRS: WGS84 (EPSG:4326) for storage; Web Mercator (EPSG:3857) for web rendering; local projected CRS for any metric computation (distance, area, buffer). For Estonia, EPSG:3301 (L-EST97). • Compute placement: push spatial joins and aggregations to DuckDB or PostGIS — not Python loops. R-tree / GIST / spatial indexing is mandatory at scale. • Discovery first: check STAC catalogs (Microsoft Planetary Computer, Earth Search, Overture STAC) before downloading anything. Lazy load with odc-stac or stackstac and only materialize what's needed. • Cloud-native access: prefer querying remote GeoParquet/COG over downloading. DuckDB with httpfs extension is the default pattern for Overture and similar S3-hosted datasets. • Scale first: local tools are fine for city/state work; at continental/global scale prefer cloud-native partitioned datasets, precomputed tiles, hosted APIs, or SaaS when they are more reliable than local batch processing. • License hygiene: preserve license metadata through every transformation. OSM is ODbL (share-alike); Overture varies by source; Sentinel is free-with-attribution; national data varies. • Runtime hygiene: prefer conda-forge environments or containers for GDAL/PROJ/GEOS/QGIS stacks. Avoid pip-only geospatial environments unless the project already proves they work.

| Use case | Format | |---|---| | Cloud analytics on vector | GeoParquet | | Streaming vector over HTTP | FlatGeobuf | | Desktop interchange | GeoPackage | | Web map vector tiles | PMTiles (containing MVT) | | Raster archive / serving | COG | | n-dimensional raster (time series, climate) | Zarr or NetCDF | | Point cloud archive | COPC (cloud-optimized LAZ) | | API response payload (small only) | GeoJSON | | Legacy compatibility (input only) | Shapefile |