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Title	Multi-Type Road Extraction And Analysis Of High-Resolution Images With D-Linknet50
ID_Doc	38457
Authors	Li S.; Liu X.
Year	2022
Published	2022 3rd International Conference on Geology, Mapping and Remote Sensing, ICGMRS 2022
DOI	http://dx.doi.org/10.1109/ICGMRS55602.2022.9849390
Abstract	Road data form remote sensing is important for GIS modeling, vector analysis, and smart cities. Recently, there has been many scholars have successively combined deep learning with road extraction to meet practical needs. Based on the former research, this paper uses D-LinkNet50 which combines the pretrained LinkNet architecture with the dilation convolution. Training on the data set provided by DigitalGlobe, the results have shown that this D-LinkNet50 has achieved 83.1%, 79.7%, 81.3% in accuracy, recall, and F1-score, respectively, which is higher than that of D-LinkNet34 network 0.7%, 1.4%, 1.0%. So the extraction accuracy is significantly improved. © 2022 IEEE.
Author Keywords	D-LinkNet; dilated convolution; remote sensing image; road extraction

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