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Building Damage Assessment
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Predicting building damage levels from earth observation data
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Now you see it, Now you don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery
Building-Guided Pseudo-Label Learning for Cross-Modal Building Damage Mapping
DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization
Change3D: Revisiting Change Detection and Captioning from A Video Modeling Perspective
BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response
Benchmarking Attention Mechanisms and Consistency Regularization Semi-Supervised Learning for Post-Flood Building Damage Assessment in Satellite Images
Building Damage Assessment in Conflict Zones: A Deep Learning Approach Using Geospatial Sub-Meter Resolution Data
CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery
DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery
Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach
ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model
Learning Efficient Unsupervised Satellite Image-based Building Damage Detection
Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery
AB2CD: AI for Building Climate Damage Classification and Detection
Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event
xFBD: Focused Building Damage Dataset and Analysis
Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images
SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images
Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets
Dual-Tasks Siamese Transformer Framework for Building Damage Assessment
Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks
Building Damage Mapping with Self-PositiveUnlabeled Learning
Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment
Fully convolutional Siamese neural networks for buildings damage assessment from satellite images
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery
RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery
An Attention-Based System for Damage Assessment Using Satellite Imagery
Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion
xBD: A Dataset for Assessing Building Damage from Satellite Imagery
Building Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks
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