Robosat Versions Save

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

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/