Revisiting the Task of Building Damage Assessment by Satellite Imaging
cs.CV
Submitted: 2026-10-09
Updated: 2026-10-09
Comments: 82 pages, 2 figures, 9 tables, 160 references
Code: https://github.com/keras-team/keras
Project page: https://clay-foundation.github.io/model/index.html
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach
- CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery
- xFBD: Focused Building Damage Dataset and Analysis
- Event Detection in Videos: A Framework for the Development of New Methods
- Multi-domain performance analysis with scores tailored to user preferences
- The Tile: A 2D Map of Ranking Scores for Two-Class Classification
- Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation
- Foundation Models for Generalist Geospatial Artificial Intelligence
- AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
- SeeFar: Satellite Agnostic Multi-Resolution Dataset for Geospatial Foundation Models
- Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques
- Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks
- Debiased Learning for Remote Sensing Data
- xView: Objects in Context in Overhead Imagery
- A Survey on Remote Sensing Foundation Models: From Vision to Multimodality
- PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models
- SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models
- ARC Prize 2024: Technical Report
- The Benchmark Lottery
- EvalAI: Towards Better Evaluation Systems for AI Agents
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