{"title":"利用VGI数据更新道路网络的深度学习方法","authors":"Prajowal Manandhar, P. Marpu, Z. Aung","doi":"10.1109/CSPIS.2018.8642728","DOIUrl":null,"url":null,"abstract":"In our earlier work, we worked on extraction of the total width of road by agents traversing in the direction guided by Volunteered Geographic Information (VGI). The only downfall of VGI approach is its inability to update the new road developments. In this paper, we introduce deep learning approach to update the road network. We make use of the output of our previous work which forms as an input to train the Convolutional Neural Network (CNN). Then, further post processing is performed to remove non-road segments (such as buildings, vegetation, etc) on the output of CNN and finally, obtain the updated road map.","PeriodicalId":251356,"journal":{"name":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Deep Learning Approach To Update Road Network using VGI Data\",\"authors\":\"Prajowal Manandhar, P. Marpu, Z. Aung\",\"doi\":\"10.1109/CSPIS.2018.8642728\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In our earlier work, we worked on extraction of the total width of road by agents traversing in the direction guided by Volunteered Geographic Information (VGI). The only downfall of VGI approach is its inability to update the new road developments. In this paper, we introduce deep learning approach to update the road network. We make use of the output of our previous work which forms as an input to train the Convolutional Neural Network (CNN). Then, further post processing is performed to remove non-road segments (such as buildings, vegetation, etc) on the output of CNN and finally, obtain the updated road map.\",\"PeriodicalId\":251356,\"journal\":{\"name\":\"2018 International Conference on Signal Processing and Information Security (ICSPIS)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Signal Processing and Information Security (ICSPIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSPIS.2018.8642728\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSPIS.2018.8642728","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep Learning Approach To Update Road Network using VGI Data
In our earlier work, we worked on extraction of the total width of road by agents traversing in the direction guided by Volunteered Geographic Information (VGI). The only downfall of VGI approach is its inability to update the new road developments. In this paper, we introduce deep learning approach to update the road network. We make use of the output of our previous work which forms as an input to train the Convolutional Neural Network (CNN). Then, further post processing is performed to remove non-road segments (such as buildings, vegetation, etc) on the output of CNN and finally, obtain the updated road map.