Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
From degrade to upgrade: Learning a self-supervised degradation guided adaptive network for blind remote sensing image super-resolution
Over the past few years, single image super-resolution (SR) has become a hotspot in the
remote sensing area, and numerous methods have made remarkable progress in this …
remote sensing area, and numerous methods have made remarkable progress in this …
EDiffSR: An efficient diffusion probabilistic model for remote sensing image super-resolution
Recently, convolutional networks have achieved remarkable development in remote
sensing image (RSI) super-resolution (SR) by minimizing the regression objectives, eg, MSE …
sensing image (RSI) super-resolution (SR) by minimizing the regression objectives, eg, MSE …
The eyes of the gods: A survey of unsupervised domain adaptation methods based on remote sensing data
With the rapid development of the remote sensing monitoring and computer vision
technology, the deep learning method has made a great progress to achieve applications …
technology, the deep learning method has made a great progress to achieve applications …
SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data
In China, the demand for a more precise perception of the national land surface has become
most urgent given the pace of development and urbanization. Constructing a very-high …
most urgent given the pace of development and urbanization. Constructing a very-high …
[HTML][HTML] Enabling country-scale land cover mapping with meter-resolution satellite imagery
High-resolution satellite images can provide abundant, detailed spatial information for land
cover classification, which is particularly important for studying the complicated built …
cover classification, which is particularly important for studying the complicated built …
The outcome of the 2022 landslide4sense competition: Advanced landslide detection from multisource satellite imagery
The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the
Institute of Advanced Research in Artificial Intelligence are presented here. The objective of …
Institute of Advanced Research in Artificial Intelligence are presented here. The objective of …
A full-level fused cross-task transfer learning method for building change detection using noise-robust pretrained networks on crowdsourced labels
Accurate building change detection is crucial for understanding urban development.
Although fully supervised deep learning-based methods for building change detection have …
Although fully supervised deep learning-based methods for building change detection have …
Seeing beyond the patch: Scale-adaptive semantic segmentation of high-resolution remote sensing imagery based on reinforcement learning
In remote sensing imagery analysis, patch-based methods have limitations in capturing
information beyond the sliding window. This shortcoming poses a significant challenge in …
information beyond the sliding window. This shortcoming poses a significant challenge in …
A fast dynamic graph convolutional network and CNN parallel network for hyperspectral image classification
Deep learning has achieved impressive results on hyperspectral image (HSI) classification.
Among them, both convolutional neural networks (CNNs) and graph neural networks …
Among them, both convolutional neural networks (CNNs) and graph neural networks …