Assessment of Buildings and Electrical Facilities Damaged by Flood and Earthquake from Satellite Imagery

EasyChair Preprint 9582

Abstract

Natural disasters cause considerable losses to people’s lives and property. Satellite images can provide crucial information of the affected areas for the first time, conducive to relieving the people in disaster and reducing the economic loss. However, the traditional satellite image analysis method based on manual processing drains workforce and material resources, which slowed the government’s response to the disaster. Aiming at the natural disasters like floods and earthquakes that often happen in the south of China, we propose a dual-stage damage assessment method based on LEDNet and ResNet. Our method detects the changes between the satellite images captured before and after a disaster of the same area, segments the buildings, and evaluates the damage level of affected buildings. In addition, we calculate influence maps based on the damage scale to the building and estimate the damage situation for electrical facilities. We used images related to earthquakes and floods in the xBD dataset to train the network model. Moreover, qualitative and quantitative evaluations demonstrated that our method has higher accuracy than the xBD baseline.

Keyphrases: Earth Observation, computer vision, neural network

@booklet{EasyChair:9582,
  author    = {Yi Ma and Fangrong Zhou and Gang Wen and Hao Gen and Ran Huang and Guoqing Liu and Ling Pei},
  title     = {Assessment of Buildings and Electrical Facilities Damaged by Flood and Earthquake from Satellite Imagery},
  howpublished = {EasyChair Preprint 9582},
  year      = {EasyChair, 2023}}