Revisiting the Task of Building Damage Assessment by Satellite Imaging
Automating this task, referred to as building damage assessment (BDA), from satellite imagery, has become an active research topic at the intersection of remote sensing, computer vision, and machine learning, particularly since supervised deep learning methods were shown to outperform earlier handcrafted approaches.
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Key points
- Natural disasters such as earthquakes, tropical storms, and floods cause extensive damage to buildings, and rapid, reliable damage maps are essential to guide emergency response.
- This chapter revisits the task of BDA from first principles, organized around three pillars: data, evaluation, and methods.
- We first clarify the candidate task for- mulations and argue for a sensor-agnostic, instance-based definition that tolerates missing pre-event imagery.
- On the evaluation side, we show that prevailing practices, largely based on a single aggregate score, are theoretically fragile, and we lay the foundations of a more informative, ranking-based, multi-score protocol.
Sources (1)
- [1]Revisiting the Task of Building Damage Assessment by Satellite ImagingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 05:07 PM
Automating this task, referred to as building damage assessment (BDA), from satellite imagery, has become an active research topic at the intersection of remote sensing, computer vision, and machine learning, particularly since supervised deep learning methods were shown to outperform earlier handcrafted approaches.
Natural disasters such as earthquakes, tropical storms, and floods cause extensive damage to buildings, and rapid, reliable damage maps are essential to guide emergency response.
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