open-gis

name

open-gis

description

Use this skill for production GIS/geospatial work, open-first but pragmatic about hosted/SaaS services when scale or data quality requires them: spatial data pipelines; vector/raster/point-cloud processing; satellite/EO imagery; LiDAR; CRS/projection/EPSG troubleshooting; spatial joins, buffers, distance/area analysis; spatial SQL / GeoSQL; routing, isochrones, geocoding; terrain/hydrology; tile generation; and web maps. Trigger when the user mentions GIS, geospatial, OpenStreetMap/OSM, Overture Maps, Sentinel, Landsat, STAC, LiDAR, GeoTIFF/COG, GeoParquet, Shapefile, GeoPackage, PMTiles, vector tiles, raster, CRS, EPSG, projections, WMS/WFS/WMTS/OGC API, QGIS, GDAL/OGR, GeoPandas, Shapely, xarray/rioxarray, DuckDB Spatial, PostGIS, BigQuery GIS, Snowflake geospatial, Sedona, PDAL, OSRM, Valhalla, GraphHopper, tippecanoe, Martin, MapLibre, Estonia data (Maa- ja Ruumiamet, ETAK, EPSG:3301/L-EST97), INSPIRE, or regional data portals. Do not trigger for simple location lookups, travel directions, or casual map references without analytical or production GIS work.

Open GIS Toolkit

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.

Modules — read the relevant reference(s) before starting work

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
Writing or reviewing spatial SQL / GeoSQL in DuckDB Spatial, PostGIS, BigQuery GIS, Snowflake, or Sedona	references/spatial-sql.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.

Global defaults — apply unless the user specifies otherwise

• 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.

Format decision matrix

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

Compute decision matrix

Scale / context	Use
< 50M features, single machine, ad-hoc	DuckDB Spatial
Multi-user, web app backend, OLTP	PostGIS
> 100M features, distributed	Apache Sedona
Continental/global lookup/search/routing/elevation	Hosted API or SaaS where coverage, SLA, terms, and price fit
Planet-scale basemap delivery	Prebuilt PMTiles/vector tiles or managed basemap service
n-dim raster, lazy/dask-backed	xarray + rioxarray (+ odc-stac for STAC ingest)
CLI batch jobs on raster	GDAL utilities (gdalwarp, gdal_translate -of COG)
Point clouds	PDAL pipelines
Terrain & hydrology beyond gdaldem	WhiteboxTools or GRASS
Desktop styling, cartography, ad-hoc exploration	QGIS (see qgis.md)

Universal anti-patterns — flag and correct

• Producing Shapefile as new output (column truncation, 2GB limit, no UTF-8, multi-file)
• Calling .distance(), .buffer(), or .area on geographic CRS (EPSG:4326) — degrees are not meters; unless specific tool explicitly supports wgs84 based geodesic calculations
• Web Mercator (EPSG:3857) for area or distance calculations — it is not equal-area, and the units are not in meters except at the equator
• Spatial joins in Python loops when DuckDB / PostGIS / R-tree-backed sjoin is one line away
• Using bbox containment for area queries when features can cross the boundary — use bbox overlap as the scan gate, then an exact spatial predicate
• Downloading entire datasets when STAC + cloud-native formats allow lazy/range-request access
• Running planet-scale local processing for lookup/search problems when reliable hosted services or precomputed global products already exist
• Treating MBTiles as the default for new web deployments — PMTiles is the modern default
• Using GeoTIFF when COG is one flag away (-of COG)
• Mixing CRS silently — every join must assert matching CRS
• Hand-rolling routing or geocoding when OSRM, Valhalla, or Nominatim are one Docker pull away
• Pinning data to "latest" in a reproducible pipeline — pin Overture release version and STAC item IDs, not just collections. For Overture, verify the pinned release is still available or mirror it.

Quick triage — recognize the request type

Before diving into a task, classify it:

1. Discovery ("what data exists for…?", "is there a dataset of…?") → start with data-sources.md. STAC search if raster; Overture or OSM if vector basemap.
2. Conversion / CRS ("convert this to…", "reproject to…", "the projection looks wrong") → formats-and-crs.md. Usually one ogr2ogr or gdalwarp call.
3. Analysis ("what's the average elevation in…", "how many buildings within 500m of…", "where are the hotspots?") → analytics.md and likely processing.md. Push to DuckDB/PostGIS first.
4. Delivery ("publish this as a web map", "generate tiles for…") → web-delivery.md. PMTiles + Martin + MapLibre is the default.
5. Desktop / cartography ("style this in QGIS", "make a print map", "automate this in QGIS") → qgis.md. Consider QGIS MCP for agentic workflows.

Most real tasks span 2–3 of these — read the relevant references in order.

Reproducibility checklist for any pipeline you produce

• Pin dataset versions (Overture release, STAC item IDs, OSM extract dates)
• Document CRS at every stage; never assume
• Use conda-forge envs or container images (ghcr.io/osgeo/gdal:alpine-small-latest is a sensible base — note the registry; the legacy Docker Hub path osgeo/gdal no longer publishes new images) — pip-only geospatial envs break frequently
• Validate outputs: gpq for GeoParquet, rio-cogeo validate for COG, pmtiles show for PMTiles, is_valid for geometries
• Preserve license metadata in column or sidecar JSON and carry required attribution into maps/APIs