Geocodis Builtup Areas
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.
- Sentinel-2 L2A Analytic (GeoTIFF) -> Builtup Areas Sentinel-2
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%
Input data format
Results from block Sentinel-2 L2A Analytic (GeoTIFF)
Parameter: probabilities - if false (default) the result is a binary classification indicating where the built-up areas are. If set to true, the resulting image represents the likelihood of built-up areas as estimated by the algorithm.
Output data format
- If ‘probabilities’ parameter is false: GeoTIFF (byte) with binary classification (0, 1), dtype : uint8, resolution: propagated (10m x 10m)
- If ‘probabilities’ parameter is true: GeoTIFF (float) with values between 0 and 1,
dtype : float, resolution: propagated (10m x 10m)
Terms & conditions
View the End User License Agreement conditions.