1.3k citations · 3.5k across the 15 of their papers we have counts for
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UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
Libo Wang, Rui Li, Ce Zhang +4
Semantic segmentation of remotely sensed urban scene images is required in a wide range of practical applications, such as land cover mapping, urban change detection, environmental…
Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images
Libo Wang, Rui Li, Dongzhi Wang +3
Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classifi…
A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
Libo Wang, Rui Li, Chenxi Duan +3
The fully convolutional network (FCN) with an encoder-decoder architecture has been the standard paradigm for semantic segmentation. The encoder-decoder architecture utilizes an en…
Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images
Libo Wang, Ce Zhang, Rui Li +3
Assigning geospatial objects with specific categories at the pixel level is a fundamental task in remote sensing image analysis. Along with rapid development in sensor technologies…
ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images
Rui Li, Chenxi Duan
Semantic segmentation of remotely sensed images plays a crucial role in precision agriculture, environmental protection, and economic assessment. In recent years, substantial fine-…
A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
Rui Li, Shunyi Zheng, Ce Zhang +2
Semantic segmentation using fine-resolution remotely sensed images plays a critical role in many practical applications, such as urban planning, environmental protection, natural a…