[HTML][HTML] Object detection and image segmentation with deep learning on earth observation data: A review-part i: Evolution and recent trends
Deep learning (DL) has great influence on large parts of science and increasingly
established itself as an adaptive method for new challenges in the field of Earth observation …
established itself as an adaptive method for new challenges in the field of Earth observation …
[HTML][HTML] Object detection and image segmentation with deep learning on Earth observation data: A review—Part II: Applications
In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …
investigating aggregated classes. The increase in data with a very high spatial resolution …
[HTML][HTML] HRCNet: High-resolution context extraction network for semantic segmentation of remote sensing images
Semantic segmentation is a significant method in remote sensing image (RSIs) processing
and has been widely used in various applications. Conventional convolutional neural …
and has been widely used in various applications. Conventional convolutional neural …
Semantic segmentation of large-size VHR remote sensing images using a two-stage multiscale training architecture
Very-high resolution (VHR) remote sensing images (RSIs) have significantly larger spatial
size compared to typical natural images used in computer vision applications. Therefore, it is …
size compared to typical natural images used in computer vision applications. Therefore, it is …
Automatic building extraction on high-resolution remote sensing imagery using deep convolutional encoder-decoder with spatial pyramid pooling
Automatic extraction of buildings from remote sensing imagery plays a significant role in
many applications, such as urban planning and monitoring changes to land cover. Various …
many applications, such as urban planning and monitoring changes to land cover. Various …
BMAnet: Boundary mining with adversarial learning for semi-supervised 2D myocardial infarction segmentation
Automatic segmentation of myocardial infarction (MI) regions in late gadolinium-enhanced
cardiac magnetic resonance images is an essential step in the computed diagnosis of …
cardiac magnetic resonance images is an essential step in the computed diagnosis of …
[HTML][HTML] Multi-scale adaptive feature fusion network for semantic segmentation in remote sensing images
Semantic segmentation of high-resolution remote sensing images is highly challenging due
to the presence of a complicated background, irregular target shapes, and similarities in the …
to the presence of a complicated background, irregular target shapes, and similarities in the …
ARC-Net: An efficient network for building extraction from high-resolution aerial images
Automatic building extraction based on high-resolution aerial images has important
applications in urban planning and environmental management. In recent years advances …
applications in urban planning and environmental management. In recent years advances …
[HTML][HTML] A deep learning model for automatic plastic mapping using unmanned aerial vehicle (UAV) data
Although plastic pollution is one of the most noteworthy environmental issues nowadays,
there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which …
there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which …
[HTML][HTML] A multiscale graph convolutional network for change detection in homogeneous and heterogeneous remote sensing images
To date, although numerous methods of Change detection (CD) in remote sensing images
have been proposed, accurately identifying changes is still a great challenge, due to the …
have been proposed, accurately identifying changes is still a great challenge, due to the …