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Artisanal and Small-scale Gold Mining (ASGM) is an important source of incomefor many households, but it can have large social and environmental effects,especially in rainforests of developing countries. The Sentinel-2 satellitescollect multispectral images that can be used for the purpose of detectingchanges in water extent and quality which indicates the locations of miningsites. This work focuses on the recognition of ASGM activities in PeruvianAmazon rainforests. We tested several semi-supervised classifiers based onSupport Vector Machines (SVMs) to detect the changes of water bodies from 2019to 2021 in the Madre de Dios region, which is one of the global hotspots ofASGM activities. Experiments show that SVM-based models can achieve reasonableperformance for both RGB (using Cohen's $\kappa$ 0.49) and 6-channel images(using Cohen's $\kappa$ 0.71) with very limited annotations. The efficacy ofincorporating Lab color space for change detection is analyzed as well.

Semi-supervised Change Detection of Small Water Bodies Using RGB and Multispectral Images in Peruvian Rainforests