[HTML][HTML] Road extraction in remote sensing data: A survey
Automated extraction of roads from remotely sensed data come forth various usages ranging
from digital twins for smart cities, intelligent transportation, urban planning, autonomous …
from digital twins for smart cities, intelligent transportation, urban planning, autonomous …
Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective
Image segmentation is a critical and important step in (GEographic) Object-Based Image
Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly …
Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly …
Deep learning approaches applied to remote sensing datasets for road extraction: A state-of-the-art review
One of the most challenging research subjects in remote sensing is feature extraction, such
as road features, from remote sensing images. Such an extraction influences multiple …
as road features, from remote sensing images. Such an extraction influences multiple …
Multi-scale and multi-task deep learning framework for automatic road extraction
Road detection and centerline extraction from very high-resolution (VHR) remote sensing
imagery are of great significance in various practical applications. Road detection and …
imagery are of great significance in various practical applications. Road detection and …
BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery
Automatic road extraction from high-resolution remote sensing imagery has various
applications like urban planning and automatic navigation. Existing methods for automatic …
applications like urban planning and automatic navigation. Existing methods for automatic …
Cascaded attention DenseUNet (CADUNet) for road extraction from very-high-resolution images
J Li, Y Liu, Y Zhang, Y Zhang - ISPRS International Journal of Geo …, 2021 - mdpi.com
The use of very-high-resolution images to extract urban, suburban and rural roads has
important application value. However, it is still a problem to effectively extract the road area …
important application value. However, it is still a problem to effectively extract the road area …
[HTML][HTML] Large-scale road extraction from high-resolution remote sensing images based on a weakly-supervised structural and orientational consistency constraint …
Fully supervised road segmentation neural networks from remote sensing images rely on a
large number of densely labeled road samples, limiting their potential in large-scale …
large number of densely labeled road samples, limiting their potential in large-scale …
Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images
Road networks are one of the main urban features. Therefore, road parts extraction from
high-resolution remotely sensed imagery and updated road database are beneficial for …
high-resolution remotely sensed imagery and updated road database are beneficial for …
[HTML][HTML] Cascaded residual attention enhanced road extraction from remote sensing images
Efficient and accurate road extraction from remote sensing imagery is important for
applications related to navigation and Geographic Information System updating. Existing …
applications related to navigation and Geographic Information System updating. Existing …
Boundary-aware refined network for automatic building extraction in very high-resolution urban aerial images
Y **, W Xu, C Zhang, X Luo, H Jia - Remote Sensing, 2021 - mdpi.com
Convolutional Neural Networks (CNNs), such as U-Net, have shown competitive
performance in the automatic extraction of buildings from Very High-Resolution (VHR) aerial …
performance in the automatic extraction of buildings from Very High-Resolution (VHR) aerial …