Results 241 to 250 of about 185,046 (290)
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Road Structure Refined CNN for Road Extraction in Aerial Image
IEEE Geoscience and Remote Sensing Letters, 2017In this letter, we propose a road structure refined convolutional neural network (RSRCNN) approach for road extraction in aerial images. In order to obtain structured output of road extraction, both deconvolutional and fusion layers are designed in the architecture of RSRCNN.
Yanan Wei, Zulin Wang, Mai Xu
exaly +2 more sources
Context-Supported Road Extraction
, 1997 Contextual information can facilitate automatic extraction of objects from digital imager). This paper addresses the use of context for the automatic extraction of roads from aerial imagery. Context is restricted to knowledge about relations between roads and other objects and is hierarchically structured.
A. Baumgartner +4 more
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Evaluation of Road Marking Feature Extraction
2008 11th International IEEE Conference on Intelligent Transportation Systems, 2008 This paper proposes a systematic approach to evaluate algorithms for extracting road marking features from images. This specific topic is seldom addressed in the literature while many road marking detection algorithms have been proposed. Most of them can be decomposed into three steps: extracting road marking features, estimating a geometrical marking ...
Thomas Veit +3 more
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Cascaded Multi-Task Road Extraction Network for Road Surface, Centerline, and Edge Extraction
IEEE Transactions on Geoscience and Remote Sensing, 2022Xiaoyan Lu, Yanfei Zhong, Zhuo Zheng
exaly +2 more sources
, 2004 This paper presents a methodology for semi-automatic road extraction from digital images using active contour models or snakes. Snakes was proposed almost two decades ago, consisting in a parametric curve controlled by photometric and geometric constraints. This last ones generate internal forces that control the shape of the snakes curve.
DE OLIVEIRA, RAFAEL MONTANHINI SOARES +1 more
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Extraction of Simple Road Crossing
, 2010 Road crossings of roads are important components of the road network. However, they generally are not explicit in the existing research on extraction of road network. This is due to the wide variety of road crossings may contain. Thus this paper presents
R B Zanin +3 more
core +3 more sources
Road finding for road-network extraction
Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition, 2003Automatic extraction of roads from aerial photos has been demonstrated in a number of systems, but the systems which display the better capabilities usually rely on manual selection of road starting points. This interaction with a human operator is eliminated by integrating a road-finding module into a road network extraction system.
Z. Aviad, P. D. Carnine Jr.
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Spatial Attention Network for Road Extraction
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020Road extraction from high-resolution remote sensing images has become an important method to achieve real-time updating of road network to meet the application requirements. However, due to the diversity of road scenes, various road width, and occlusion, the detected roads in recent researches are often discontinuous and these narrow or fragmented road
Ruonan Chen, Yuan Hu, Tong Wu, Ling Peng
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Road extraction in suburban areas by region-based road subgraph extraction and evaluation
2009 Joint Urban Remote Sensing Event, 2009In this paper, a road extraction approach for suburban areas from high resolution CIR images is presented. The approach is region-based: the image is first segmented using the normalized cuts algorithm, then the initial segments are grouped to form segments, and road parts are extracted from these segments. Ideally roads in the image correspond to only
Anne Grote +3 more
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Extraction of road segments by spatial filters
Systems and Computers in Japan, 1994AbstractThis paper proposes a new approach for extracting segments from map data. In general, it is difficult to construct a system that can completely extract all road data noise free. On the other hand, it will be easy to construct a system that can extract all road data although noises are included.
Wookhyun Kim +3 more
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