scholarly journals Lane-Level Road Extraction from High-Resolution Optical Satellite Images

2019 ◽  
Vol 11 (22) ◽  
pp. 2672
Author(s):  
Jiguang Dai ◽  
Tingting Zhu ◽  
Yilei Zhang ◽  
Rongchen Ma ◽  
Wantong Li
Keyword(s):  
High Resolution ◽  
Rural Areas ◽  
Template Matching ◽  
City Planning ◽  
Urban Expansion ◽  
Satellite Image ◽  
Road Extraction ◽  
Urban And Rural Areas ◽  
Optical Satellite Images ◽  
Vehicle Management

High-quality updates of road information play an important role in smart city planning, sustainable urban expansion, vehicle management, urban planning, traffic navigation, public health and other fields. However, due to interference from road geometry and texture noise, it is difficult to avoid the decline of automation while accurately extracting roads. Therefore, we propose a high-resolution optical satellite image lane-level road extraction method. First, from the perspective of template matching and considering road characteristics and relevant semantic relations, an adaptive correction model, an MLSOH (multi-scale line segment orientation histogram) descriptor, a sector descriptor, and a multiangle beamlet descriptor are proposed to solve the interference from geometry and texture noise in road template matching and tracking. Second, based on refined lane-level tracking, single-lane and double-lane road-tracking modes are designed to extract single-lane and double-lane roads, respectively. In this paper, Pleiades satellite and GF-2 images are selected to set up different scenarios for urban and rural areas. Experiments are carried out on the phenomena that restrict road extraction, such as tree occlusion, building shadow occlusion, road bending, and road boundary blurring. Compared with other methods, the proposed method not only ensures the accuracy of lane-level road extraction but also greatly improves the automation of road extraction.

scholarly journals A Model-Driven-to-Sample-Driven Method for Rural Road Extraction

2021 ◽  
Vol 13 (8) ◽  
pp. 1417
Author(s):  
Jiguang Dai ◽  
Rongchen Ma ◽  
Litao Gong ◽  
Zimo Shen ◽  
Jialin Wu
Keyword(s):  
Rural Areas ◽  
Template Matching ◽  
Color Space ◽  
Recall Rate ◽  
Road Extraction ◽  
Rural Roads ◽  
Model Driven ◽  
The Road ◽  
Center Point ◽  
Manual Input

Road extraction in rural areas is one of the most fundamental tasks in the practical application of remote sensing. In recent years, sample-driven methods have achieved state-of-the-art performance in road extraction tasks. However, sample-driven methods are prohibitively expensive and laborious, especially when dealing with rural roads with irregular curvature changes, narrow widths, and diverse materials. The template matching method can overcome these difficulties to some extent and achieve impressive road extraction results. This method also has the advantage of the vectorization of road extraction results, but the automation is limited. Straight line sequences can be substituted for curves, and the use of the color space can increase the recognition of roads and nonroads. A model-driven-to-sample-driven road extraction method for rural areas with a much higher degree of automation than existing template matching methods is proposed in this study. Without prior samples, on the basis of the geometric characteristics of narrow and long roads and using the advantages of straight lines instead of curved lines, the road center point extraction model is established through length constraints and gray mean contrast constraints of line sequences, and the extraction of some rural roads is completed through topological connection analysis. In addition, we take the extracted road center point and manual input data as local samples, use the improved line segment histogram to determine the local road direction, and use the panchromatic and hue, saturation, value (HSV) space interactive matching model as the matching measure to complete the road tracking extraction. Experimental results show that, for different types of data and scenarios on the premise, the accuracy and recall rate of the evaluation indicators reach more than 98%, and, compared with other methods, the automation of this algorithm has increased by more than 40%.

A Novel Region-Based Approach for Automatic Road Extraction from High Resolution Satellite Images

2013 ◽  
Vol 284-287 ◽  
pp. 2998-3003
Author(s):  
Young Gi Byun
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Image ◽  
Road Surface ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Road Extraction ◽  
High Resolution Satellite Images ◽  
High Resolution Satellite Imagery ◽  
One Class Classification

With the constantly increasing public availability of high resolution satellite imagery, interest in automatic road extraction from this imagery has recently increased. Road extraction from high resolution satellite imagery refers to reliable road surface extraction instead of road line extraction because roads in the imagery mostly correspond to an elongated region with a locally constant spectral signature rather than traditional thin lines. This paper proposes a novel automatic road extraction approach that is based on a combination of image segmentation and one-class classification and consists of two main steps. First, the image is segmented using a modified previous segmentation algorithm to achieve more reliable segmentation for road extraction. The key road objects are then automatically extracted from the segmented image to obtain road training samples. Then one-class classification, based on a support vector data description classifier, is carried out to extract the road surface area from the image. The experimental results from a pan-sharpened KOMPSAT-2 satellite image demonstrate the correctness and efficiency of the proposed method for its application to road extraction from high resolution satellite image.

Research on Embodiment and Utilization of Environmental Value Oriented Spatial Planning of Harbin Marginal Area

2011 ◽  
Vol 90-93 ◽  
pp. 899-904
Author(s):  
Bin Xia Xue ◽  
Zhi Qing Zhao ◽  
Sheng Jun Liu
Keyword(s):  
Natural Environment ◽  
Rural Areas ◽  
Spatial Planning ◽  
Environmental Planning ◽  
Urban Expansion ◽  
Symbiotic Relationship ◽  
Design Strategies ◽  
Environmental Value ◽  
Marginal Areas ◽  
Urban And Rural Areas

As the joint connecting urban and rural areas, the special ways are needed in the spatial urban planning & design of the urban marginal areas. Based on the cases of environmental planning in Harbin’s marginal area under urban expansion background, the paper discusses the interdependent and symbiotic relationship between city and natural environment, and explores the effective modes finding, changing and utilizing the value elements of suburban environment. Finally the paper puts forwards spatial planning & design strategies oriented in the discovery and utilization of environmental value for urban marginal area according to the location characteristics of the urban marginal areas and value embodiment of natural environmental conditions, such as ecological priority, advantages intensification and images construction.

scholarly journals Tracking Urban Expansion Using Random Forests for the Classification of Landsat Imagery (1986–2015) and Predicting Urban/Built-Up Areas for 2025: A Study of the Kumasi Metropolis, Ghana

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 44
Author(s):  
Bernard Fosu Frimpong ◽  
Frank Molkenthin
Keyword(s):  
City Planning ◽  
Landsat Imagery ◽  
Urban Expansion ◽  
Satellite Image ◽  
Unique Contribution ◽  
Land Use Land Cover ◽  
Rapid Urbanization ◽  
Lulc Changes ◽  
Spatio Temporal

Kumasi is a nodal city and functions as the administrative and economic capital of the Ashanti region in Ghana. Rapid urbanization has been experienced inducing the transformation of various Land Use Land Cover (LULC) types into urban/built-up areas in Kumasi. This paper aims at tracking spatio-temporal LULC changes utilizing Landsat imagery from 1986, 2013 and 2015 of Kumasi. The unique contribution of this research is its focus on urban expansion analysis and the utilization of Random Forest (RF) Classifier for satellite image classification. Change detection, urban land modelling and urban expansion in the sub-metropolitan zones, buffers, density decay curve and correlation analysis were methodologies adopted for our study. The classifier yielded better accuracy compared to earlier works in Ghana. The evaluation of LULC changes indicated that urban/built-up areas are continually increasing at the expense of agricultural and forestlands. The urban/built-up areas occupied 4622.49 hectares (ha) (23.78%), 13,447.50 ha (69.18%) and 14,004.60 ha (72.05%) in 1986, 2013 and 2015, respectively of the 19,438 ha area of Kumasi. Projection indicated that urban/built-up areas will occupy 15,490 ha (79.70%) in 2025. The urban expansion was statistically significant. The results revealed the importance of spatial modeling for environmental management and city planning.

scholarly journals Urban and rural areas as defined by population density in Japan

2002 ◽  
Vol 69 (415-417) ◽  
pp. 331-332
Author(s):  
Haruhiko Goto
Keyword(s):  
Population Density ◽  
Urban Design ◽  
21St Century ◽  
Graduate School ◽  
Rural Areas ◽  
City Planning ◽  
World Society ◽  
The World ◽  
Urban And Rural Areas ◽  
The City

Dr Goto, an architect and town planner with an MSc in Architecture and a Ph. D in City Planning from Waseda University, Japan, formerly Vice-Dean of the Graduate School, is now Professor of Urban Design at the same university. He is also a Principal of Kankyo to Zokei Inc., Architecture and Urban Design, Tokyo, and a member of the World Society for Ekistics (WSE). The text that follows is a slightly edited and revised version of a paper presented at the WSE Symposion "Defining Success of the City in the 21st Century," Berlin, 24-28 October, 2001.

scholarly journals Road Extraction from High-Resolution Remote Sensing Imagery Using Refined Deep Residual Convolutional Neural Network

2019 ◽  
Vol 11 (5) ◽  
pp. 552 ◽  
Author(s):  
Lin Gao ◽  
Weidong Song ◽  
Jiguang Dai ◽  
Yang Chen
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Transportation Systems ◽  
Network Architectures ◽  
Tensor Voting ◽  
Road Extraction ◽  
Complex Scene ◽  
Effective Performance ◽  
Optical Satellite Images

Road extraction is one of the most significant tasks for modern transportation systems. This task is normally difficult due to complex backgrounds such as rural roads that have heterogeneous appearances with large intraclass and low interclass variations and urban roads that are covered by vehicles, pedestrians and the shadows of surrounding trees or buildings. In this paper, we propose a novel method for extracting roads from optical satellite images using a refined deep residual convolutional neural network (RDRCNN) with a postprocessing stage. RDRCNN consists of a residual connected unit (RCU) and a dilated perception unit (DPU). The RDRCNN structure is symmetric to generate the outputs of the same size. A math morphology and a tensor voting algorithm are used to improve RDRCNN performance during postprocessing. Experiments are conducted on two datasets of high-resolution images to demonstrate the performance of the proposed network architectures, and the results of the proposed architectures are compared with those of other network architectures. The results demonstrate the effective performance of the proposed method for extracting roads from a complex scene.

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