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In this work we present BrazilDAM, a novel public dataset based on Sentinel-2and Landsat-8 satellite images covering all tailings dams cataloged by theBrazilian National Mining Agency (ANM). The dataset was built usinggeoreferenced images from 769 dams, recorded between 2016 and 2019. The timeseries were processed in order to produce cloud free images. The dams containmining waste from different ore categories and have highly varying shapes,areas and volumes, making BrazilDAM particularly interesting and challenging tobe used in machine learning benchmarks. The original catalog contains, besidesthe dam coordinates, information about: the main ore, constructive method, riskcategory, and associated potential damage. To evaluate BrazilDAM's predictivepotential we performed classification essays using state-of-the-art deepConvolutional Neural Network (CNNs). In the experiments, we achieved an averageclassification accuracy of 94.11% in tailing dam binary classification task. Inaddition, others four setups of experiments were made using the complementaryinformation from the original catalog, exhaustively exploiting the capacity ofthe proposed dataset.

BrazilDAM: A Benchmark dataset for Tailings Dam Detection