论文标题
Brazildam：用于尾矿大坝检测的基准数据集
BrazilDAM: A Benchmark dataset for Tailings Dam Detection
论文作者
论文摘要
在这项工作中，我们提出了Brazildam，这是一种基于Sentinel-2和Landsat-8卫星图像的新型公共数据集，涵盖了由巴西国家矿业局（ANM）分类的所有尾矿大坝。该数据集是使用来自2016年至2019年间记录的769大坝的地理参考图像构建的。为了产生无云图像，对时间序列进行了处理。大坝包含来自不同矿石类别的采矿废物，并具有高度不同的形状，区域和量，使Brazildam特别有趣且具有挑战性，可用于机器学习基准。原始目录除了大坝坐标外，还包含有关：主要矿石，建设性方法，风险类别和相关潜在损害的信息。为了评估Brazildam的预测潜力，我们使用最先进的深卷积神经网络（CNN）进行了分类论文。在实验中，我们在尾巴二进制分类任务中达到了平均分类精度为94.11％。此外，使用原始目录中的互补信息进行了四个实验设置，从而详尽利用了所提出的数据集的容量。
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.
