研究目的
To detect the change of road network from remote sensing images for practical usages such as navigation map updating, road construction supervision, and disaster survey.
研究成果
The proposed method can find new build road segments effectively, but further improvements are needed for wider application and handling of rural roads with narrow and curve shapes.
研究不足
1) The method should be tested with various areas for widely usage; 2) rural road with narrow and curve shapes should be further studied.
1:Experimental Design and Method Selection:
Utilizes deep convolutional neural network (DCNN) for road area segmentation and image registration for spatial coordinate reference unification.
2:Sample Selection and Data Sources:
Uses Open Street Map (OSM) for road network information and GF-2 satellite data for experimental test.
3:List of Experimental Equipment and Materials:
GF-2 satellite data with specific technical parameters.
4:Experimental Procedures and Operational Workflow:
Includes dataset generation, road region extraction using U-Net model, image registration, and road change analysis.
5:Data Analysis Methods:
Evaluates the difference between extracted road results and old road map to identify new or modified road sections.
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