Artisanal and Small-Scale Mine Detection in Semi-Desertic Areas by Improved U-Net
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 mining (ASM). The study area is in central northern Burkina Faso (Africa) and is characterized by a semi-desert environ...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| Repositorio: | r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| OAI Identifier: | oai:cttc.fundanetsuite.com:p7710 |
| Acceso en línea: | https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=7710 |
| Access Level: | acceso abierto |
| Palabra clave: | Sentinel-2 TensorFlow Artisanal mines artisanal and small-scale mining (ASM) mapping convolution neural networks (CNNs) deep learning (DL) image segmentation remote sensing (RS) |
| Sumario: | 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 mining (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, ASM 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. The performance evaluation of two state-of-the-art DL architectures [U-Net and attention deep supervised multiscale 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. |
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