6 citations · 15 across the 5 of their papers we have counts for
5 papers
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
Junshi Xia, Naoto Yokoya, Bruno Adriano +1
We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images cov…
Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets
Zaishuo Xia, Zelin Li, Yanbing Bai +2
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. In deep learning,…
Building Damage Mapping with Self-PositiveUnlabeled Learning
Junshi Xia, Naoto Yokoya, Bruno Adriano
Humanitarian organizations must have fast and reliable data to respond to disasters. Deep learning approaches are difficult to implement in real-world disasters because it might be…
Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping
Bruno Adriano, Naoto Yokoya, Junshi Xia +4
Earth observation technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to monitor ever-growing urban environments continuously. Notably…
Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
Naoto Yokoya, Kazuki Yamanoi, Wei He +4
We propose a framework that estimates inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learnin…