The Geospatial Data Abstraction Library

By F. Warmerdam. 2008

318 citations recorded by the source corpus.

Subfields: Geography, Planning and Development

View Paper: https://www.semanticscholar.org/paper/27f442bf430f50932d361a72a97669d5d0a8d227

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Geography, Planning and Development

1. Michael Schmitt
2. Edzer J. Pebesma
3. Lloyd H. Hughes
4. Xiaoxiang Zhu
5. Chunping Qiu

Citations in the corpus

• Simple Features for R: Standardized Support for Spatial Vector Data (2018, background): “…(Herring, 2010), GeoJSON (Butler et al., 2016), GeoSPARQL (Perry and Herring, 2012), and open source libraries that empower the open source geospatial software landscape including GDAL (Warmerdam, 2008), GEOS (GEOS Development Team, 2017) and liblwgeom (a PostGIS component, Obe and Hsu (2015)).”
• SEN12MS - A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion (2019, methodology): “We therefore used GDAL (Warmerdam, 2008) to transform all exported data from the WGS84 lat/lon georeference to their local UTM coordinate representation, while resampling to actually square pixels of 10m×10m.”