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Building Damage Assessment

Predicting building damage levels from earth observation data

Papers

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Showing 1–25 of 32 papers

TitleStatusHype
ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space ModelCode4
BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster responseCode3
Change3D: Revisiting Change Detection and Captioning from A Video Modeling PerspectiveCode2
Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite ImagesCode1
xBD: A Dataset for Assessing Building Damage from Satellite ImageryCode1
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite ImagesCode1
Learning Efficient Unsupervised Satellite Image-based Building Damage DetectionCode1
Fully convolutional Siamese neural networks for buildings damage assessment from satellite imagesCode1
Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal FusionCode1
Building-Guided Pseudo-Label Learning for Cross-Modal Building Damage MappingCode1
Dual-Tasks Siamese Transformer Framework for Building Damage Assessment—0
AB2CD: AI for Building Climate Damage Classification and Detection—0
An Attention-Based System for Damage Assessment Using Satellite Imagery—0
Benchmarking Attention Mechanisms and Consistency Regularization Semi-Supervised Learning for Post-Flood Building Damage Assessment in Satellite Images—0
Building Damage Assessment in Conflict Zones: A Deep Learning Approach Using Geospatial Sub-Meter Resolution Data—0
Building Damage Mapping with Self-PositiveUnlabeled Learning—0
Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery—0
Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment—0
CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery—0
Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery—0
DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery—0
Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach—0
Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event—0
RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery—0
SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images—0
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