[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 …
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 …
An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images
A Abdollahi, B Pradhan, AM Alamri - Geocarto International, 2022 - Taylor & Francis
Building objects is one of the principal features that are essential for updating the geospatial
database. Extracting building features from high-resolution imagery automatically and …
database. Extracting building features from high-resolution imagery automatically and …
VNet: An end-to-end fully convolutional neural network for road extraction from high-resolution remote sensing data
A Abdollahi, B Pradhan, A Alamri - Ieee Access, 2020 - ieeexplore.ieee.org
One of the most important tasks in the advanced transportation systems is road extraction.
Extracting road region from high-resolution remote sensing imagery is challenging due to …
Extracting road region from high-resolution remote sensing imagery is challenging due to …
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 …
Road extraction methods in high-resolution remote sensing images: A comprehensive review
R Lian, W Wang, N Mustafa… - IEEE Journal of Selected …, 2020 - ieeexplore.ieee.org
Road extraction from high-resolution remote sensing images is a challenging but hot
research topic in the past decades. A large number of methods are invented to deal with this …
research topic in the past decades. A large number of methods are invented to deal with this …
RCNet: road classification convolutional neural networks for intelligent vehicle system
DK Dewangan, SP Sahu - Intelligent Service Robotics, 2021 - Springer
Vision-based techniques for intelligent vehicles in heterogeneous road environments are
gaining significant attention from researchers and industrialists. Unfortunately, the …
gaining significant attention from researchers and industrialists. Unfortunately, the …
Building footprint extraction from high resolution aerial images using generative adversarial network (GAN) architecture
Building extraction with high accuracy using semantic segmentation from high-resolution
remotely sensed imagery has a wide range of applications like urban planning, updating of …
remotely sensed imagery has a wide range of applications like urban planning, updating of …
Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images
A Abdollahi, B Pradhan - Expert Systems with Applications, 2021 - Elsevier
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 …
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) …