TITLE: BrazilDAM: A Benchmark dataset for Tailings Dam Detection 

ABSTRACT: In this work we present BrazilDAM, a novel public dataset based on Sentinel-2
and Landsat-8 satellite images covering all tailings dams cataloged by the
Brazilian National Mining Agency (ANM). The dataset was built using
georeferenced images from 769 dams, recorded between 2016 and 2019. The time
series were processed in order to produce cloud free images. The dams contain
mining waste from different ore categories and have highly varying shapes,
areas and volumes, making BrazilDAM particularly interesting and challenging to
be used in machine learning benchmarks. The original catalog contains, besides
the dam coordinates, information about: the main ore, constructive method, risk
category, and associated potential damage. To evaluate BrazilDAM's predictive
potential we performed classification essays using state-of-the-art deep
Convolutional Neural Network (CNNs). In the experiments, we achieved an average
classification accuracy of 94.11% in tailing dam binary classification task. In
addition, others four setups of experiments were made using the complementary
information from the original catalog, exhaustively exploiting the capacity of
the proposed dataset.
In this work we present BrazilDAM, a novel public dataset based on Sentinel-2
and Landsat-8 satellite images covering all tailings dams cataloged by the
Brazilian National Mining Agency (ANM). The dataset was built using
georeferenced images from 769 dams, recorded between 2016 and 2019. The time
series were processed in order to produce cloud free images. The dams contain
mining waste from different ore categories and have highly varying shapes,
areas and volumes, making BrazilDAM particularly interesting and challenging to
be used in machine learning benchmarks. The original catalog contains, besides
the dam coordinates, information about: the main ore, constructive method, risk
category, and associated potential damage. To evaluate BrazilDAM's predictive
potential we performed classification essays using state-of-the-art deep
Convolutional Neural Network (CNNs). In the experiments, we achieved an average
classification accuracy of 94.11% in tailing dam binary classification task. In
addition, others four setups of experiments were made using the complementary
information from the original catalog, exhaustively exploiting the capacity of
the proposed dataset.
BrazilDAM: A Benchmark dataset for Tailings Dam Detection
http://arxiv.org/abs/2003.07948v2