no code implementations • 15 Mar 2024 • Changhong Hou, Junchuan Yu, Daqing Ge, Liu Yang, Laidian Xi, Yunxuan Pang, Yi Wen
We propose TransLandSeg, which is a transfer learning approach for landslide semantic segmentation based on a vision foundation model (VFM).
no code implementations • 2 Aug 2023 • Yiming Zhou, Yuexing Peng, Wei Li, Junchuan Yu, Daqing Ge, Wei Xiang
To extract accurate semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from boundaries of landslides through HPCL-Net and fuses heterogeneous infromation in the semantic space from high-resolution remote sensing images and digital elevation model data.
no code implementations • 24 Feb 2023 • Zili Lu, Yuexing Peng, Wei Li, Junchuan Yu, Daqing Ge, Wei Xiang
An object-level contrastive learning (OCL) strategy is employed in the object classification sub-network featuring a siamese network to realize the global features extraction, and a sub-object-level contrastive learning (SOCL) paradigm is designed in the semantic segmentation sub-network to efficiently extract salient features from boundaries of landslides.
no code implementations • 18 Jan 2022 • Ying Wang, Yuexing Peng, Xinran Liu, Wei Li, George C. Alexandropoulos, Junchuan Yu, Daqing Ge, Wei Xiang
Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide variety of applications, such as autonomous driving, path planning, and road navigation.