Appendices

Appendix A: Installation

GeoCroissant support is available as an optional extra in the mlcroissant Python library. Installing it adds geospatial data processing capabilities and the converters needed to generate GeoCroissant metadata from existing formats.

pip install mlcroissant[geo]

For development installs (e.g., when working from a local clone of the repository):

pip install -e .[geo]

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Appendix B: GeoSPARQL Query Examples

The following SPARQL queries illustrate how GeoCroissant metadata exposed as RDF can be queried using GeoSPARQL predicates.

Query 1: Find Datasets by Geometry (Spatial Containment)

This query retrieves all GeoCroissant datasets and records whose geometry falls within a specified bounding polygon.

PREFIX geosparql: <http://www.opengis.net/ont/geosparql#>
PREFIX geocr:     <http://mlcommons.org/croissant/geo/1.0/>
PREFIX dct:       <http://purl.org/dc/terms/>

SELECT ?dataset ?record ?wkt
WHERE {
  ?dataset a geocr:Dataset ;
           geocr:recordSet ?record .

  ?record geosparql:hasGeometry ?geom .
  ?geom geosparql:asWKT ?wkt .

  FILTER(geof:sfWithin(?geom, "POLYGON((-120 30, -110 30, -110 40, -120 40, -120 30))"^^geosparql:wktLiteral))
}

Query 2: Discover Datasets by Exact Bounding Box Match

This query returns all datasets whose bounding box matches a specific coordinate range.

PREFIX geocr: <http://mlcommons.org/croissant/geo/1.0/>

SELECT ?dataset ?record ?bbox
WHERE {
  ?dataset a geocr:Dataset ;
           geocr:recordSet ?record .
  ?record geocr:BoundingBox ?bbox .
  FILTER(STR(?bbox) = "[-120.0, 30.0, -110.0, 40.0]")
}