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Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward
Earth observation (EO) is increasingly used for map** and monitoring processes
occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a …
occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a …
Deep-learning-based semantic segmentation of remote sensing images: A survey
L Huang, B Jiang, S Lv, Y Liu… - IEEE Journal of Selected …, 2023 - ieeexplore.ieee.org
Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a
category to each pixel in remote sensing images, plays a vital role in a broad range of …
category to each pixel in remote sensing images, plays a vital role in a broad range of …
Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memory
J Zhu, Y Guo, G Sun, L Yang, M Deng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Semantic segmentation is a key technique involved in automatic interpretation of high-
resolution remote sensing (HRS) imagery and has drawn much attention in the remote …
resolution remote sensing (HRS) imagery and has drawn much attention in the remote …
Syndrone-multi-modal uav dataset for urban scenarios
The development of computer vision algorithms for Unmanned Aerial Vehicles (UAVs)
imagery heavily relies on the availability of annotated high-resolution aerial data. However …
imagery heavily relies on the availability of annotated high-resolution aerial data. However …
Marsscapes and udaformer: A panorama dataset and a transformer-based unsupervised domain adaptation framework for martian terrain segmentation
ResiDualGAN: Resize-residual DualGAN for cross-domain remote sensing images semantic segmentation
The performance of a semantic segmentation model for remote sensing (RS) images pre-
trained on an annotated dataset greatly decreases when testing on another unannotated …
trained on an annotated dataset greatly decreases when testing on another unannotated …
Multi-view graph convolutional network with spectral component decompose for remote sensing images classification
X Cheng, X He, M Qiao, P Li, P Chang… - … on Circuits and …, 2022 - ieeexplore.ieee.org
Automatic land cover classification from high-resolution remote sensing (RS) images
remains challenging due to the complex composition of classes. Given the potential of a …
remains challenging due to the complex composition of classes. Given the potential of a …
Category-level assignment for cross-domain semantic segmentation in remote sensing images
Deep learning-based semantic segmentation has made great progress in understanding
very-high-resolution (VHR) remote sensing images (RSIs). However, large-scale …
very-high-resolution (VHR) remote sensing images (RSIs). However, large-scale …
A fine-grained unsupervised domain adaptation framework for semantic segmentation of remote sensing images
Unsupervised domain adaptation (UDA) aims at adapting a model from the source domain
to the target domain by tackling the issue of domain shift. Cross-domain segmentation of …
to the target domain by tackling the issue of domain shift. Cross-domain segmentation of …