Results 51 to 60 of about 2,732,569 (203)
Strip Attention Networks for Road Extraction
Remote Sensing, 2022 In recent years, deep learning methods have been widely used for road extraction in remote sensing images. However, the existing deep learning semantic segmentation networks generally show poor continuity in road segmentation due to the high-class similarity between roads and buildings surrounding roads in remote sensing images, and the existence of ...
Hai Huan, Yu Sheng, Yi Zhang, Yuan Liu
openaire +2 more sources
Road Extraction from Remote Sensing Imagery with Spatial Attention Based on Swin Transformer
Remote SensingRoad extraction is a crucial aspect of remote sensing imagery processing that plays a significant role in various remote sensing applications, including automatic driving, urban planning, and path navigation.
Xianhong Zhu +5 more
semanticscholar +1 more source
International Journal of Digital EarthThe extraction of roads from UAV images is challenged by lighting, noise, occlusions, and similar non-road objects, making high-quality road extraction difficult.
Zhen Liu +4 more
doaj +1 more source
ROAD EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 Abstract. Roads are significant objects of an infrastructure and the extraction of roads from aerial and satellite images are important for different applications such as automated map generation and change detection. Roads are also important to detect other structures such as buildings and urban areas.
openaire +4 more sources
Sensors, 2023 Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods ...
Mohd Jawed Khan +4 more
doaj +1 more source
DA-RoadNet: A Dual-Attention Network for Road Extraction From High Resolution Satellite Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Recent advances in deep-learning methods have shown extraordinary performance in road extraction from high resolution satellite imagery. However, most existing deep-learning network models yield discontinuous and incomplete results because of shadows and
Jie Wan +4 more
semanticscholar +1 more source
WeavingUnet: Enhancing dense road extraction by integrating horizontal and vertical features
International Journal of Applied Earth Observations and GeoinformationRoad extraction plays a crucial role in automatically identifying road networks from remote sensing images, which is essential for various applications. However, existing road extraction methods perform poorly when dealing with dense roads.
Xianzhi Ma, Jianhui Li, Mingyang Lv
doaj +1 more source
IET Image Processing, 2021 The road extraction from high resolution remote sensing image is of great importance in a variety of applications. Recently, the abundant deep convolutional neural networks are proposed for road extraction task.
Yibo Han, Pu Han, Manlei Jia
doaj +1 more source
WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction
International Journal of Applied Earth Observations and GeoinformationEfficient and accurate extraction of road networks from high-resolution satellite images is essential for urban planning, construction, and traffic management.
Ningjing Wang +4 more
doaj +1 more source
Remote Sensing, 2022 Road networks play a fundamental role in our daily life. It is of importance to extract the road structure in a timely and precise manner with the rapid evolution of urban road structure.
Hao Chen +4 more
semanticscholar +1 more source