Automatic extraction and 3D modeling of real road scenes using UAV imagery and deep learning semantic segmentation

IF 3.7 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL International Journal of Digital Earth Pub Date : 2024-06-19 DOI:10.1080/17538947.2024.2365970
Zhen Liu, Wenxiu Wu, Danyu Wang, Bingyan Cui, Xingyu Gu
{"title":"Automatic extraction and 3D modeling of real road scenes using UAV imagery and deep learning semantic segmentation","authors":"Zhen Liu, Wenxiu Wu, Danyu Wang, Bingyan Cui, Xingyu Gu","doi":"10.1080/17538947.2024.2365970","DOIUrl":null,"url":null,"abstract":"The extraction of roads from UAV images is challenged by lighting, noise, occlusions, and similar non-road objects, making high-quality road extraction difficult. To addressing these issues, this s...","PeriodicalId":54962,"journal":{"name":"International Journal of Digital Earth","volume":"11 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Digital Earth","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/17538947.2024.2365970","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

Abstract

The extraction of roads from UAV images is challenged by lighting, noise, occlusions, and similar non-road objects, making high-quality road extraction difficult. To addressing these issues, this s...
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用无人机图像和深度学习语义分割技术自动提取真实道路场景并进行三维建模
从无人机图像中提取道路面临着光照、噪声、遮挡物和类似非道路物体的挑战,使得高质量的道路提取变得困难。为了解决这些问题,本...
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.50
自引率
3.90%
发文量
88
审稿时长
3 months
期刊介绍: The International Journal of Digital Earth is a response to this initiative. This peer-reviewed academic journal (SCI-E) focuses on the theories, technologies, applications, and societal implications of Digital Earth and those visionary concepts that will enable a modeled virtual world. The journal encourages papers that: Progress visions for Digital Earth frameworks, policies, and standards; Explore geographically referenced 3D, 4D, or 5D models to represent the real planet, and geo-data-intensive science and discovery; Develop methods that turn all forms of geo-referenced data, from scientific to social, into useful information that can be analyzed, visualized, and shared; Present innovative, operational applications and pilots of Digital Earth technologies at a local, national, regional, and global level; Expand the role of Digital Earth in the fields of Earth science, including climate change, adaptation and health related issues,natural disasters, new energy sources, agricultural and food security, and urban planning; Foster the use of web-based public-domain platforms, social networks, and location-based services for the sharing of digital data, models, and information about the virtual Earth; and Explore the role of social media and citizen-provided data in generating geo-referenced information in the spatial sciences and technologies.
期刊最新文献
An improved bridge reference network for 3-D SAR tomography based on regional growing strategy MLFA-Net: multi-level feature-aggregated network for semantic change detection in remote sensing images MAL-YOLO: a lightweight algorithm for target detection in side-scan sonar images based on multi-scale feature fusion and attention mechanism Assessing water use efficiency reactivity to meteorological, hydrological, and agricultural droughts on the Mongolian Plateau PM2.5 estimation and its relationship with NO2 and SO2 in China from 2016 to 2020
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1