v1.2.0

This release brings incredible new features and improvements from the
community accumulated over the last months. We recommend to upgrade.

The pre-built docker images are the recommended way of using robosat:

• https://github.com/mapbox/robosat#installation
• https://hub.docker.com/r/mapbox/robosat/

Changes

• rs train: state of the art losses and metrics. Lovasz loss as default,
  many many more small features and fixes in training and related tools.
  Thanks https://github.com/ocourtin

• rs extract: fully automatated road training dataset creation
  Thanks https://github.com/DragonEmperorG

• rs extract: batch feature extraction for datasets too big for memory
  Thanks http://github.com/daniel-j-h

• rs rasterize: batch rasterization for datasets too big for memory
  Thanks http://github.com/daniel-j-h

• Infrastructure: improved docker images, pre-trained weights in images,
  upgrades to CUDA 10.1, cudnn 7, and pytorch 1.1.
  Thanks http://github.com/daniel-j-h

v1.1.0

Changes

• rs train: new --checkpoint argument to re-start training (fine-tune)
  from a trained model checkpoint. Thanks https://github.com/ocourtin

• rs train: memory usage reduction during validation by disabling
  expensive gradient computation. Thanks https://github.com/Jesse-jApps

• rs train, rs predict: speedups using multiple workers and
  doing metric calculation on GPU. Thanks https://github.com/ocourtin

• rs merge: polygon orientation fixes to respect the GeoJSON
  specification (right-hand rule). Thanks https://github.com/marsbroshok

You can find automatically built Docker images as usual at https://hub.docker.com/r/mapbox/robosat/