Land Cover Mapping in the Amhara Region of Northwest Ethiopia Using Convolutional Neural Networks and Domain Adaptation Techniques

The Amhara region, in northwest Ethiopia, has a complex topography and highly fragmented croplands (averaging half a hectare). Mapping such fragmented LCLU areas requires very high-resolution satellite imagery and robust classification methodologies. To this end, we have used multi-temporal very-high-resolution (VHR) WorldView imagery (2 meters) in combination with Convolutional Neural Networks (CNNs), to map land cover classes across the entire Amhara region. This paper presents results from domain adaptation experiments using training data from Senegal to accurately map land cover classes at 2 m resolution in the Amhara region, Ethiopia. The Attention UNet CNNs provided promising results for predicting land cover in Ethiopia imagery using domain adaptation techniques and without the addition of local training labels, with an overall accuracy of 74%. We conclude that promising future research directions exist for transfer learning implementation to finetune our land cover classes with additional model refinement to the Amhara region.