Remote sensing (RS) image semantic segmentation using deep convolutional neural
networks (DCNNs) has shown great success in various applications. However, the high …

The intelligent segmentation of high-resolution remote sensing (HRS) image, also called as
dense prediction task for HRS image, has been and will continue to be important research in …

Assessing the solar photovoltaic (PV) potential on buildings is essential for environmental
protection and sustainable development. However, currently, the high costs of data …

Quick and accurate extraction of un-collapsed buildings from post-disaster High-resolution
Remote Sensing Images (HRSIs) is imperative for emergency response. Pre-disaster HRSIs …

The semantic segmentation of point clouds is a crucial undertaking in 3D reconstruction and
holds great importance. However, achieving precise semantic segmentation represents a …

Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation (UDA-
RSSeg) addresses the challenge of adapting a model trained on source domain data to …

Unsupervised domain adaptation (UDA) techniques have the potential to enhance the
transferability of neural network models in unknown scenarios and reduce the labelling …

This study proposes the Memory-Efficient Flow Accumulation (MEFA) algorithm using a “look-
around” approach. In a shared-memory model such as the one provided by OpenMP, it is …

Cross-domain semantic segmentation of remote sensing (RS) imagery based on
unsupervised domain adaptation (UDA) has become a research hotspot in geoscience …

This study proposes the Memory-E cient Flow Accumulation (MEFA) algorithm using a look-
around approach. In a shared-memory model such as the one provided by OpenMP, it is …