Examples¶

This page provides comprehensive examples covering all features of geoextent using datasets under 100MB for fast execution.

Remote Repository Examples¶

Geoextent supports extracting geospatial extent from multiple research data repositories.

Zenodo Example¶

Extract extent from a Zenodo atmospheric data repository (~50MB):

python -m geoextent -b -t https://doi.org/10.5281/zenodo.4593540

Or using just the DOI:

python -m geoextent -b -t 10.5281/zenodo.4593540

PANGAEA Example¶

Extract extent from PANGAEA Arctic Ocean dataset (~1MB):

python -m geoextent -b -t https://doi.org/10.1594/PANGAEA.734969

PANGAEA datasets often include rich geospatial metadata.

OSF Example¶

Extract extent from OSF geographic research data (~5MB):

python -m geoextent -b -t https://doi.org/10.17605/OSF.IO/4XE6Z

Multiple OSF identifier formats are supported:

python -m geoextent -b -t OSF.IO/4XE6Z
python -m geoextent -b -t https://osf.io/4xe6z/

GFZ Data Services Example¶

Extract extent from GFZ geothermal resources dataset (~30MB):

python -m geoextent -b -t 10.5880/GFZ.4.8.2023.004

Dryad Example¶

Extract extent from Dryad dataset:

python -m geoextent -b -t https://datadryad.org/stash/dataset/doi:10.5061/dryad.0k6djhb7x

Advanced Features¶

Convex Hull Extraction¶

Calculate the convex hull instead of just the bounding box for vector files:

python -m geoextent -b --convex-hull https://doi.org/10.5281/zenodo.4593540

This provides a more accurate representation of the actual spatial extent.

Placename Lookup¶

Add geographic context to your extracts:

# Using default GeoNames gazetteer (requires API key in .env)
python -m geoextent -b --placename https://doi.org/10.5281/zenodo.4593540

# Using Nominatim (no API key needed)
python -m geoextent -b --placename nominatim https://doi.org/10.1594/PANGAEA.734969

# Using Photon (no API key needed)
python -m geoextent -b --placename photon https://osf.io/4xe6z/

Size Limiting¶

Control download size when processing large repositories:

# Limit to 10MB total download
python -m geoextent -b --max-download-size 10MB https://doi.org/10.5281/zenodo.4593540

# Random sampling with seed for reproducibility
python -m geoextent -b --max-download-size 50MB --max-download-method random --max-download-method-seed 42 https://doi.org/10.5281/zenodo.4593540

File Filtering¶

Skip non-geospatial files to save time:

python -m geoextent -b --download-skip-nogeo https://doi.org/10.5281/zenodo.4593540

Combine with size limits:

python -m geoextent -b --download-skip-nogeo --max-download-size 50MB https://osf.io/4xe6z/

Output Formats¶

Choose different output formats:

# Default GeoJSON format
python -m geoextent -b https://doi.org/10.1594/PANGAEA.734969

# Well-Known Text (WKT) format
python -m geoextent -b --format wkt https://doi.org/10.1594/PANGAEA.734969

# Well-Known Binary (WKB) hex format
python -m geoextent -b --format wkb https://doi.org/10.1594/PANGAEA.734969

Visualization¶

Generate geojson.io URLs for interactive visualization:

python -m geoextent -b --geojsonio https://doi.org/10.5281/zenodo.4593540

Combine with convex hull:

python -m geoextent -b --convex-hull --geojsonio https://doi.org/10.1594/PANGAEA.734969

Quiet Mode¶

Suppress progress bars and warnings for scripting:

python -m geoextent -b --quiet https://doi.org/10.1594/PANGAEA.734969

Perfect for shell scripts:

BBOX=$(python -m geoextent -b --format wkt --quiet https://doi.org/10.1594/PANGAEA.734969)
echo "Bounding box: $BBOX"

Local File Examples¶

Single File Processing¶

Extract from GeoJSON:

python -m geoextent -b -t tests/testdata/geojson/muenster_ring_zeit.geojson

Extract from CSV:

python -m geoextent -b -t tests/testdata/csv/cities_NL.csv

Extract from Shapefile:

python -m geoextent -b -t tests/testdata/shapefile/muenster_ring.shp

Directory Processing¶

Process all files in a directory:

python -m geoextent -b -t tests/testdata/geojson/

With convex hull:

python -m geoextent -b --convex-hull tests/testdata/geojson/

Multiple Files¶

Process specific files together:

python -m geoextent -b -t tests/testdata/shapefile/muenster_ring.shp tests/testdata/csv/cities_NL.csv

Combined Examples¶

All Features Together¶

Extract with all features enabled:

python -m geoextent -b -t \
  --convex-hull \
  --placename nominatim \
  --max-download-size 50MB \
  --download-skip-nogeo \
  --format wkt \
  --geojsonio \
  https://doi.org/10.5281/zenodo.4593540

Multiple Repositories¶

Process multiple repositories together:

python -m geoextent -b \
  --max-download-size 20MB \
  https://doi.org/10.5281/zenodo.4593540 \
  https://doi.org/10.1594/PANGAEA.734969 \
  https://osf.io/4xe6z/

Docker Examples¶

Basic Docker Usage¶

Using Docker for remote repositories:

docker run --rm geoextent -b https://doi.org/10.5281/zenodo.4593540

With placename lookup:

docker run --rm --env-file .env geoextent -b --placename https://doi.org/10.5281/zenodo.4593540

Local files with Docker:

docker run --rm -v ${PWD}/tests/testdata:/data geoextent -b -t /data/geojson/

Performance Options¶

Parallel Downloads¶

Control download workers:

# Use 8 parallel workers
python -m geoextent -b --max-download-workers 8 https://doi.org/10.5281/zenodo.4593540

# Sequential downloads (slower but safer)
python -m geoextent -b --max-download-workers 1 https://doi.org/10.5281/zenodo.4593540

Testing Examples¶

Quick Repository Exploration¶

Explore a repository with minimal download:

python -m geoextent -b \
  --max-download-size 5MB \
  --max-download-method random \
  --quiet \
  https://doi.org/10.5281/zenodo.4593540

Format Coverage Examples¶

These examples demonstrate all supported formats using small datasets:

GeoJSON¶

python -m geoextent -b -t tests/testdata/geojson/muenster_ring_zeit.geojson

CSV¶

python -m geoextent -b -t tests/testdata/csv/cities_NL.csv

Shapefile¶

python -m geoextent -b tests/testdata/shapefile/muenster_ring.shp

GeoTIFF¶

python -m geoextent -b tests/testdata/tif/wf_100m_klas.tif

GeoPackage¶

python -m geoextent -b tests/testdata/geopackage/nc.gpkg

GPX¶

python -m geoextent -b -t tests/testdata/gpx/gpx1.1_with_all_fields.gpx

KML¶

python -m geoextent -b tests/testdata/kml/aasee.kml

GML¶

python -m geoextent -b tests/testdata/gml/clc_1000_PT.gml

FlatGeobuf¶

python -m geoextent -b tests/testdata/flatgeobuf/sample.fgb

Repository Provider Coverage¶

All Supported Providers¶

Examples for each repository provider:

Zenodo:

python -m geoextent -b https://doi.org/10.5281/zenodo.4593540

Figshare:

python -m geoextent -b https://doi.org/10.6084/m9.figshare.12345678

Dryad:

python -m geoextent -b https://datadryad.org/stash/dataset/doi:10.5061/dryad.0k6djhb7x

PANGAEA:

python -m geoextent -b https://doi.org/10.1594/PANGAEA.734969

OSF:

python -m geoextent -b https://doi.org/10.17605/OSF.IO/4XE6Z

GFZ Data Services:

python -m geoextent -b 10.5880/GFZ.4.8.2023.004

Dataverse:

python -m geoextent -b https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/12345

Pensoft:

python -m geoextent -b https://doi.org/10.3897/BDJ.2.e1068