An Attention-Based System for Damage Assessment Using Satellite Imagery
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Building Damage AssessmentSimilar Papers 제목 키워드 기반
Now you see it, Now you don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery
This paper audits damage labels derived from coincident satellite and drone aerial imagery for 15,814 buildings across Hurricanes Ian, Michael, and Harvey, finding 29.02% label disagreement and significantly different di…
Building Damage AssessmentxBD: A Dataset for Assessing Building Damage from Satellite Imagery
We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate…
2D Semantic SegmentationBuilding Damage AssessmentChange DetectionDisaster Response+1Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning
Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery. Traditional methods, which often rely on manual inspections or sparse data, are typically slo…
Data AugmentationRapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event
Rapid and accurate building damage assessments from high-resolution satellite imagery following a natural disaster is essential to inform and optimize first responder efforts. However, performing such building damage ass…
Building Damage AssessmentDiversityBuilding Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks
After a hurricane, damage assessment is critical to emergency managers for efficient response and resource allocation. One way to gauge the damage extent is to quantify the number of flooded/damaged buildings, which is t…
Building Damage AssessmentGeneral Classificationimage-classificationImage Classification