Building Damage Assessment
16 papers with code • 1 benchmarks • 1 datasets
Predicting building damage levels from earth observation data
Benchmarks
These leaderboards are used to track progress in Building Damage Assessment
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BRIGHT
Most implemented papers
Building Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks
In this paper, we propose to improve the efficiency of building damage assessment by applying image classification algorithms to post-hurricane satellite imagery.
xBD: A Dataset for Assessing Building Damage from Satellite Imagery
DIUx-xView/xview2-baseline
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xBD is the largest building damage assessment dataset to date, containing 850, 736 building annotations across 45, 362 km\textsuperscript{2} of imagery.
Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images
nka77/dahitra
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In this work, a novel transformer-based network is proposed for assessing building damage.
Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion
Automatic change detection and disaster damage assessment are currently procedures requiring a huge amount of labor and manual work by satellite imagery analysts.
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
ShaneShen/BDANet-Building-Damage-Assessment
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With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings.
Fully convolutional Siamese neural networks for buildings damage assessment from satellite images
In this work, we develop a computational approach for an automated comparison of the same region's satellite images before and after the disaster, and classify different levels of damage in buildings.
Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks
In the aftermath of disasters, building damage maps are obtained using change detection to plan rescue operations.
Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets
xia-zs/SSLBDA
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In the field of post-disaster assessment, for timely and accurate rescue and localization after a disaster, people need to know the location of damaged buildings.
Learning Efficient Unsupervised Satellite Image-based Building Damage Detection
fzmi/ubdd
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Existing Building Damage Detection (BDD) methods always require labour-intensive pixel-level annotations of buildings and their conditions, hence largely limiting their applications.
ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model
Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD).
