A global context-aware and batch-independent network for road extraction from VHR satellite imagery
Road extraction is to automatically label the pixels of roads in satellite imagery with specific
semantic categories based on the extraction of the topographical meaningful features. For …
semantic categories based on the extraction of the topographical meaningful features. For …
[HTML][HTML] A review and meta-analysis of generative adversarial networks and their applications in remote sensing
Abstract Generative Adversarial Networks (GANs) are one of the most creative advances in
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Scribble-based weakly supervised deep learning for road surface extraction from remote sensing images
Road surface extraction from remote sensing images using deep learning methods has
achieved good performance, while most of the existing methods are based on fully …
achieved good performance, while most of the existing methods are based on fully …
Sat2graph: Road graph extraction through graph-tensor encoding
Inferring road graphs from satellite imagery is a challenging computer vision task. Prior
solutions fall into two categories:(1) pixel-wise segmentation-based approaches, which …
solutions fall into two categories:(1) pixel-wise segmentation-based approaches, which …
MRENet: Simultaneous extraction of road surface and road centerline in complex urban scenes from very high-resolution images
Automatic extraction of the road surface and road centerline from very high-resolution (VHR)
remote sensing images has always been a challenging task in the field of feature extraction …
remote sensing images has always been a challenging task in the field of feature extraction …
NIGAN: A framework for mountain road extraction integrating remote sensing road-scene neighborhood probability enhancements and improved conditional …
Mountain roads are a source of important basic geographic data used in various fields. The
automatic extraction of road images through high-resolution remote sensing imagery using …
automatic extraction of road images through high-resolution remote sensing imagery using …
Road extraction of high-resolution remote sensing images derived from DenseUNet
Road network extraction is one of the significant assignments for disaster emergency
response, intelligent transportation systems, and real-time updating road network. Road …
response, intelligent transportation systems, and real-time updating road network. Road …
Building extraction of aerial images by a global and multi-scale encoder-decoder network
Semantic segmentation is an important and challenging task in the aerial image community
since it can extract the target level information for understanding the aerial image. As a …
since it can extract the target level information for understanding the aerial image. As a …
Improving road semantic segmentation using generative adversarial network
Road network extraction from remotely sensed imagery has become a powerful tool for
updating geospatial databases, owing to the success of convolutional neural network (CNN) …
updating geospatial databases, owing to the success of convolutional neural network (CNN) …
DA-CapsUNet: A dual-attention capsule U-Net for road extraction from remote sensing imagery
The up-to-date and information-accurate road database plays a significant role in many
applications. Recently, with the improvement in image resolutions and quality, remote …
applications. Recently, with the improvement in image resolutions and quality, remote …