Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery
Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery
复制标题
根据飓风后遥感图像自动检测受损建筑物的基准数据集
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Youngjun Choe
中科院分区：
文献类型：
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作者：
S. Chen;Andrew Escay;C. Haberland;Tessa Schneider;Valentina Staneva;Youngjun Choe
飓风灾害的快速评估对应急响应至关重要，然而，评估过程往往缓慢、劳动密集、成本高且容易出错。计算机视觉和遥感的新进展为以不同尺度观察地球提供了可能性。然而，仍需要进行大量的预处理工作，以便采用最先进的应急方法。为了比较飓风后从机载和卫星传感器拍摄的遥感图像中自动检测受损建筑物的方法，本文介绍了从公开数据中开发基准数据集的方法。这项工作的主要贡献包括：（1）创建飓风受损建筑物基准数据集的可扩展框架;（2）在2017年飓风哈维之后，公开分享大休斯顿地区的基准数据集。所提出的方法可用于构建其他飓风损坏的建筑物数据集，研究人员可以在这些数据集上训练和测试对象检测模型，以自动识别损坏的建筑物。
Rapid damage assessment is of crucial importance to emergency responders during hurricane events, however, the evaluation process is often slow, labor-intensive, costly, and error-prone. New advances in computer vision and remote sensing open possibilities to observe the Earth at a different scale. However, substantial pre-processing work is still required in order to apply state-of-the-art methodology for emergency response. To enable the comparison of methods for automatic detection of damaged buildings from post-hurricane remote sensing imagery taken from both airborne and satellite sensors, this paper presents the development of benchmark datasets from publicly available data. The major contributions of this work include (1) a scalable framework for creating benchmark datasets of hurricane-damaged buildings and (2) public sharing of the resulting benchmark datasets for Greater Houston area after Hurricane Harvey in 2017. The proposed approach can be used to build other hurricane-damaged building datasets on which researchers can train and test object detection models to automatically identify damaged buildings.