For more information, please read the block description.
Land cover classification that assigns each of the input image pixels to a discrete land cover class (water, vegetation, urban, barren land etc.).
This block is a beta version and the algorithm is trained with Pléiades/SPOT images over multiple locations. The block now supports running inference globally.
The block accepts 4 or 5 land cover classes. When selecting 4 classes, the model treats the classes Barren Land and Urban as a single class. Currently, the classification output consists of the following classes:
|Number||Land cover class|
|2||High vegetation (including trees)|
|3||Low vegetation (including bushes and grass)|
|5||Urban (including roads and buildings)|
If you would like to know more about the development of this land cover classifier and the scientific approach, we recommend reading this blog post. In addition, the model architecture we used is based on the work of Robinson et al. (2019). The source code is available publicly in this repository.
Based on an independent test dataset, the algorithm accuracy is presented in the table below.
|Accuracy metrics||Land cover classifier with 4 classes||Land cover classifier with 5 classes|
nclasses: Number of classes to infer (4 or 5)
AOI.clipped GeoTIFF format.
Robinson, C., Hou, L., Malkin, K., Soobitsky, R., Czawlytko, J., Dilkina, B.N., Jojic, N., 2019. Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 12718–12727. https://doi.org/10.1109/CVPR.2019.01301