An Attention-Based System for Damage Assessment Using Satellite Imagery
| Autor: | Latisha Konz, Kevin J. LaTourette, Moses W. Chan, Sriram Baireddy, Mary L. Comer, Michael Gribbons, Edward J. Delp, Hanxiang Hao, Emily R. Bartusiak |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition High resolution Image segmentation Computer vision Satellite Satellite imagery Segmentation Artificial intelligence business Scale (map) Image resolution Effective response |
| Zdroj: | IGARSS |
| Popis: | When disaster strikes, accurate situational information and a fast, effective response are critical to save lives. Widely available, high resolution satellite images enable emergency responders to estimate locations, causes, and severity of damage. Quickly and accurately analyzing the extensive amount of satellite imagery available, though, requires an automatic approach. In this paper, we present Siam-U-Net-Attn model - a multi-class deep learning model with an attention mechanism - to assess damage levels of buildings given a pair of satellite images depicting a scene before and after a disaster. We evaluate the proposed method on xView2, a large-scale building damage assessment dataset, and demonstrate that the proposed approach achieves accurate damage scale classification and building segmentation results simultaneously. 10 pages, 9 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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