Multi-Scale Earthquake Damaged Building Feature Set

Guorui Gao, Futao Wang (), Zhenqing Wang, Qing Zhao, Litao Wang, Jinfeng Zhu, Wenliang Liu, Gang Qin and Yanfang Hou
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Guorui Gao: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Futao Wang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Zhenqing Wang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Qing Zhao: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Litao Wang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Jinfeng Zhu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Wenliang Liu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Gang Qin: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yanfang Hou: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Data, 2024, vol. 9, issue 7, 1-19

Abstract: Earthquake disasters are marked by their unpredictability and potential for extreme destructiveness. Accurate information on building damage, captured in post-earthquake remote sensing images, is critical for an effective post-disaster emergency response. The foundational features within these images are essential for the accurate extraction of building damage data following seismic events. Presently, the availability of publicly accessible datasets tailored specifically to earthquake-damaged buildings is limited, and existing collections of post-earthquake building damage characteristics are insufficient. To address this gap and foster research advancement in this domain, this paper introduces a new, large-scale, publicly available dataset named the Major Earthquake Damage Building Feature Set (MEDBFS). This dataset comprises image data sourced from five significant global earthquakes and captured by various optical remote sensing satellites, featuring diverse scale characteristics and multiple spatial resolutions. It includes over 7000 images of buildings pre- and post-disaster, each subjected to stringent quality control and expert validation. The images are categorized into three primary groups: intact/slightly damaged, severely damaged, and completely collapsed. This paper develops a comprehensive feature set encompassing five dimensions: spectral, texture, edge detection, building index, and temporal sequencing, resulting in 16 distinct classes of feature images. This dataset is poised to significantly enhance the capabilities for data-driven identification and analysis of earthquake-induced building damage, thereby supporting the advancement of scientific and technological efforts for emergency earthquake response.

Keywords: earthquakes; damaged buildings; feature sets; remote sensing images (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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