I’m happy to announce the RoboSat v1.1.0 release — our open source end-to-end pipeline for feature extraction from aerial and satellite imagery.

This release is powered by major community contributions from features like fine-tuning trained models, to training and prediction speedups, and bug fixes! Thank you to our contributors!

Here is an overview of what you will find in the v1.1.0 release

• 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 at https://hub.docker.com/r/mapbox/robosat/

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For our next release we already have a couple amazing community contributions

• rs extract: a new road extraction handler to automatically generate a training dataset for roads; Thanks https://github.com/DragonEmperorG

• New state of the art losses (like the Lovasz loss) and improved metrics among other improvements. Thanks https://github.com/ocourtin

Here’s why you should be excited for the Lovasz loss: the following shows a preview

Left to right: image / label / cross entropy / miou / lovasz

In contrast to the current default cross entropy loss the Lovasz loss will be an amazing default for robosat’s binary models and greatly improve common feature extraction use-cases!

Again, thanks for these amazing community contributions!

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As usual hit us up for feedback be it at OSM hack weekends, on tickets, IRC, or the OSMUS Slack. Also check out previous diary posts about robosat:

• https://www.openstreetmap.org/user/daniel-j-h/diary/44145
• https://www.openstreetmap.org/user/daniel-j-h/diary/44321