Artisanal and Small-Scale Mine Detection in Semi-Desertic Areas by Improved U-Net

This is the authors' abstract. We don't add key points for this paper.

In this letter, we propose a Deep Learning (DL) based approach which exploits multispectral Sentinel-2 open-source data and a small-size inventory to map artisanal and small-scale mines (ASM). The study area is in central northern Burkina Faso (Africa) and is characterized by a semi-desert environment that makes mapping challenging. In sub-Saharan Africa, artisanal and small-scale mining represents a source of subsistence for a significant number of individuals. However, because ASM are often illegal and uncontrolled, the materials employed in the excavation process are highly dangerous for the environment as well as for the lives of the people involved in the mining activities. One of the most important aspects regarding ASM is the record of their spatial location which, at the moment, is missing in most of the African regions. Performance evaluation of two state-of-art DL architectures (U-Net, and Attention Deep Supervised Multi-Scale U-Net - ADSMS U-Net) is provided, along with an in-depth analysis of the predictions when dealing with both dry and rainy seasons. The ADSMS U-Net architecture yields generally more accurate predictions than the basic U-Net allowing us to better discriminate ASM in such an environment. The findings show that the proposed approach can detect ASM in semi-desertic areas starting with a few samples at a low cost in terms of both human and financial resources.