58 citations · 108 across the 10 of their papers we have counts for
11 papers
A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges
Lei Ding, Danfeng Hong, Maofan Zhao +6
In the last decade, the rapid development of deep learning (DL) has made it possible to perform automatic, accurate, and robust Change Detection (CD) on large volumes of Remote Sen…
OpenEarthMap-SAR: A Benchmark Synthetic Aperture Radar Dataset for Global High-Resolution Land Cover Mapping
Junshi Xia, Hongruixuan Chen, Clifford Broni-Bediako +3
High-resolution land cover mapping plays a crucial role in addressing a wide range of global challenges, including urban planning, environmental monitoring, disaster response, and…
Plug-and-Play DISep: Separating Dense Instances for Scene-to-Pixel Weakly-Supervised Change Detection in High-Resolution Remote Sensing Images
Zhenghui Zhao, Chen Wu, Lixiang Ru +3
Existing Weakly-Supervised Change Detection (WSCD) methods often encounter the problem of "instance lumping" under scene-level supervision, particularly in scenarios with a dense d…
Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark
Clifford Broni-Bediako, Junshi Xia, Jian Song +3
Learning with limited labelled data is a challenging problem in various applications, including remote sensing. Few-shot semantic segmentation is one approach that can encourage de…
Dual-Tasks Siamese Transformer Framework for Building Damage Assessment
Hongruixuan Chen, Edoardo Nemni, Sofia Vallecorsa +3
Accurate and fine-grained information about the extent of damage to buildings is essential for humanitarian relief and disaster response. However, as the most commonly used archite…
Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer
Hongruixuan Chen, Chen Wu, Yonghao Xu +1
Unsupervised domain adaptation for semantic segmentation aims to make models trained on synthetic data (source domain) adapt to real images (target domain). Previous feature-level…