Artisanal and small-scale mine detection in semi-desertic areas by improved U-Net

Lorenzo Nava, Maria Cuevas, Sansar Raj Meena*, Filippo Catani, Oriol Monserrat

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
18 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number2507905
JournalIEEE geoscience and remote sensing letters
Volume19
DOIs
Publication statusPublished - 7 Nov 2022
Externally publishedYes

Keywords

  • Africa
  • Artisanal mines
  • ASM mapping
  • Convolution
  • convolution neural networks (CNNs)
  • deep learning (DL)
  • Encoding
  • Gold
  • image segmentation
  • remote sensing (RS)
  • Sentinel-2
  • Soil
  • TensorFlow
  • Training
  • Vegetation mapping

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