GDAL - Geospatial Data Abstraction Library. GDAL is a translator library for raster and vector geospatial data formats that is released under an X/MIT style Open Source license by the Open Source Geospatial Foundation. As a library, it presents a single raster abstract data model and single vector abstract data model to the calling application for all supported formats. It also comes with a variety of useful command line utilities for data translation and processing. The NEWS page describes the November 2017 GDAL/OGR 2.2.3 release.

References in zbMATH (referenced in 14 articles )

Showing results 1 to 14 of 14.
Sorted by year (citations)

  1. Johannes N. Hansen: nd - A Framework for the Analysis of n-dimensional Earth Observation Data (2022) not zbMATH
  2. Michael J. Mahoney, Colin M. Beier, Aidan C. Ackerman: terrainr: An R package for creating immersive virtual environments (2022) not zbMATH
  3. Changjie Chen, Jasmeet Judge, David Hulse: PyLUSAT: An open-source Python toolkit for GIS-based land use suitability analysis (2021) arXiv
  4. Hugo Ledoux, Filip Biljecki, Balázs Dukai, Kavisha Kumar, Ravi Peters, Jantien Stoter, Tom Commandeur: 3dfier: automatic reconstruction of 3D city models (2021) not zbMATH
  5. Jon Schwenk; Jayaram Hariharan: RivGraph: Automatic extraction and analysis of river and delta channel network topology (2021) not zbMATH
  6. Vieilledent G: forestatrisk: a Python package for modelling and forecasting deforestation in the tropics (2021) not zbMATH
  7. Tobias Stål, Anya M. Reading: A Grid for Multidimensional and Multivariate Spatial Representation and Data Processing (2020) not zbMATH
  8. Jon Hill: HRDS: A Python package for hierarchical raster datasets (2019) not zbMATH
  9. Sebastian Lamprecht: Pyoints: A Python package for point cloud, voxel and raster processing (2019) not zbMATH
  10. Jason Laura; Kelvin Rodriguez; Adam C. Paquette; Evin Dunn: AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data (2018) not zbMATH
  11. Griebel, Michael (ed.); Schüller, Anton (ed.); Schweitzer, Marc Alexander (ed.): Scientific computing and algorithms in industrial simulations. Projects and products of Fraunhofer SCAI (2017)
  12. Korosov, A.A., Hansen, M.W., Dagestad, K.-F., Yamakawa, A., Vines, A., Riechert, M.: Nansat: a Scientist-Orientated Python Package for Geospatial Data Processing (2016) not zbMATH
  13. Tomislav Hengl; Pierre Roudier; Dylan Beaudette; Edzer Pebesma: plotKML: Scientific Visualization of Spatio-Temporal Data (2015) not zbMATH
  14. Sarah Goslee: Analyzing Remote Sensing Data in R: The landsat Package (2011) not zbMATH