Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia

07 September 2022

This study assessed the potential of different satellite data modalities (single-date, multi-date, multi-resolution, and an ensemble of multi-sensor images) for classifying land-use following deforestation in Ethiopia using the U-Net deep neural network architecture enhanced with attention.

Scientists suggest that:

- Choosing the right satellite imagery (sensor) type is crucial. Either detailed spatial patterns (single-date Planet-NICFI) or detailed temporal patterns (multi-date Sentinel-2, Landsat-8) are required for identifying land-use following deforestation, while medium-resolution single-date imagery is not sufficient to achieve high classification accuracy.

- Adding soft-attention to the standard U-Net improved the classification accuracy, especially for small-scale land-uses.

- Models and products presented in this work can be used as a powerful data resource for governmental and forest monitoring agencies to design and monitor deforestation mitigation measures and data-driven land-use policy.