[HTML][HTML] Deep learning-based building extraction from remote sensing images: A comprehensive review
L Luo, P Li, X Yan - Energies, 2021 - mdpi.com
Building extraction from remote sensing (RS) images is a fundamental task for geospatial
applications, aiming to obtain morphology, location, and other information about buildings …
applications, aiming to obtain morphology, location, and other information about buildings …
CMGFNet: A deep cross-modal gated fusion network for building extraction from very high-resolution remote sensing images
The extraction of urban structures such as buildings from very high-resolution (VHR) remote
sensing imagery has improved dramatically, thanks to recent developments in deep …
sensing imagery has improved dramatically, thanks to recent developments in deep …
[HTML][HTML] Building extraction from remote sensing images with sparse token transformers
Deep learning methods have achieved considerable progress in remote sensing image
building extraction. Most building extraction methods are based on Convolutional Neural …
building extraction. Most building extraction methods are based on Convolutional Neural …
An unsupervised remote sensing change detection method based on multiscale graph convolutional network and metric learning
As a fundamental application, change detection (CD) is widespread in the remote sensing
(RS) community. With the increase in the spatial resolution of RS images, high-resolution …
(RS) community. With the increase in the spatial resolution of RS images, high-resolution …
BOMSC-Net: Boundary optimization and multi-scale context awareness based building extraction from high-resolution remote sensing imagery
Automatic building extraction from high-resolution remote sensing imagery has various
applications, such as urban planning and land use management. However, the existing …
applications, such as urban planning and land use management. However, the existing …
[HTML][HTML] DR-Net: An improved network for building extraction from high resolution remote sensing image
M Chen, J Wu, L Liu, W Zhao, F Tian, Q Shen, B Zhao… - Remote Sensing, 2021 - mdpi.com
At present, convolutional neural networks (CNN) have been widely used in building
extraction from remote sensing imagery (RSI), but there are still some bottlenecks. On the …
extraction from remote sensing imagery (RSI), but there are still some bottlenecks. On the …
[HTML][HTML] Self-attention in reconstruction bias U-Net for semantic segmentation of building rooftops in optical remote sensing images
Deep learning models have brought great breakthroughs in building extraction from high-
resolution optical remote-sensing images. Among recent research, the self-attention module …
resolution optical remote-sensing images. Among recent research, the self-attention module …
SAGN: Semantic-aware graph network for remote sensing scene classification
The scene classification of remote sensing (RS) images plays an essential role in the RS
community, aiming to assign the semantics to different RS scenes. With the increase of …
community, aiming to assign the semantics to different RS scenes. With the increase of …
[HTML][HTML] Semantic segmentation of urban buildings using a high-resolution network (HRNet) with channel and spatial attention gates
S Seong, J Choi - Remote Sensing, 2021 - mdpi.com
In this study, building extraction in aerial images was performed using csAG-HRNet by
applying HRNet-v2 in combination with channel and spatial attention gates. HRNet-v2 …
applying HRNet-v2 in combination with channel and spatial attention gates. HRNet-v2 …
Dual attention deep fusion semantic segmentation networks of large-scale satellite remote-sensing images
Since DCNNs (deep convolutional neural networks) have been successfully applied to
various academic and industrial fields, semantic segmentation methods, based on DCNNs …
various academic and industrial fields, semantic segmentation methods, based on DCNNs …