{"title":"基于D-LinkNet50的多类型道路高分辨率图像提取与分析","authors":"Shenglong Li, Xianglei Liu","doi":"10.1109/ICGMRS55602.2022.9849390","DOIUrl":null,"url":null,"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.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Multi-type road extraction and analysis of high-resolution images with D-LinkNet50\",\"authors\":\"Shenglong Li, Xianglei Liu\",\"doi\":\"10.1109/ICGMRS55602.2022.9849390\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":129909,\"journal\":{\"name\":\"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICGMRS55602.2022.9849390\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICGMRS55602.2022.9849390","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-type road extraction and analysis of high-resolution images with D-LinkNet50
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.
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