Search Results for author:
Found 5 papers, 2 papers with code
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping.
Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets
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.
Building Damage Mapping with Self-PositiveUnlabeled Learning
Humanitarian organizations must have fast and reliable data to respond to disasters.
Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping
In this study, we have developed a global multisensor and multitemporal dataset for building damage mapping.
Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
We propose a framework that estimates inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learning and numerical simulation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
