Difference between revisions of "Torchgeo"

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(Created page with "377px|thumb|right|MapServer logo '''torchGeo is an incubating OSGeo project.''' == Description == ''From the OSGeo project page:'' [https://www.osgeo...")
 
 
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[[File:Mapserver.png|377px|thumb|right|MapServer logo]]
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[[File:Torchgeo logo 370x206.png|thumb|right|torchGeo logo]]
  
 
'''torchGeo is an incubating OSGeo project.'''
 
'''torchGeo is an incubating OSGeo project.'''
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TorchGeo is a PyTorch domain library, similar to torchvision, providing datasets, samplers, transforms, and pre-trained models specific to geospatial data.
 
TorchGeo is a PyTorch domain library, similar to torchvision, providing datasets, samplers, transforms, and pre-trained models specific to geospatial data.
  
The goal of this library is to make it simple:
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The goal of this library is to make it simple: for machine learning experts to work with geospatial data, and for remote sensing experts to explore machine learning solutions.
 
 
for machine learning experts to work with geospatial data, and
 
for remote sensing experts to explore machine learning solutions.
 
  
 
== Core Features ==
 
== Core Features ==

Latest revision as of 21:06, 5 May 2024

torchGeo logo

torchGeo is an incubating OSGeo project.

Description

From the OSGeo project page:

[1] TorchGeo is a PyTorch domain library, similar to torchvision, providing datasets, samplers, transforms, and pre-trained models specific to geospatial data.

The goal of this library is to make it simple: for machine learning experts to work with geospatial data, and for remote sensing experts to explore machine learning solutions.

Core Features

  • Geospatial Datasets and Samplers
  • 20+ data loaders for satellite imagery and masks
  • Automatic CRS reprojection and resampling
  • Support for multimodal learning and data fusion
  • Benchmark Datasets
  • 50+ data loaders for benchmark datasets
  • Quickly experiment with a variety of models
  • Automatically download most datasets
  • Pre-Trained Weights
  • 40+ models pre-trained on geospatial data
  • Native support for multispectral imagery
  • Enables transfer learning on smaller datasets
  • Reproducibility with Lightning
  • Builtin train/val/test splits and data augmentation
  • Classification, regression, detection, segmentation
  • Command-line interface, support for YAML files