Geocodis Builtup Areas
Coming back soon: currently not available. Machine learning based algorithm for built up area detection.
Builtup Areas Sentinel-2 uses a U-Net convolutional deep neural network to detect built-up areas. The image is first segmented into smaller chunks which are than classified by a trained model and finally reassembled into the final image. Relevant packages: keras, tensorflow, rasterio, scikit-image, scipy.
Algorithm Training Details
The model was trained on data from Uganda which may limit its performance in other areas. The model sometimes detects dirt as built up areas and sometimes fails to detect larger structures and small huts. We plan to improve detection accuracy in next releases.
Algorithm accuracy: average more than 80%.