首页    期刊浏览 2024年10月06日 星期日
登录注册

文章基本信息

  • 标题:An Interactive platform for low-cost 3D building modeling from VGI data using convolutional neural network
  • 本地全文:下载
  • 作者:Hongchao Fan ; Gefei Kong ; Chaoquan Zhang
  • 期刊名称:Big Earth Data
  • 印刷版ISSN:2096-4471
  • 电子版ISSN:2574-5417
  • 出版年度:2021
  • 卷号:5
  • 期号:1
  • 页码:49-65
  • DOI:10.1080/20964471.2021.1886391
  • 出版社:Taylor & Francis Group
  • 摘要:The applications of 3D building models are limited as producing them requires massive labor and time costs as well as expensive devices. In this paper, we aim to propose a novel and web-based interactive platform, VGI3D , to overcome these challenges. The platform is designed to reconstruct 3D building models by using free images from internet users or volunteered geographic information (VGI) platform, even though not all these images are of high quality. Our interactive platform can effectively obtain each 3D building model from images in 30 seconds, with the help of user interaction module and convolutional neural network (CNN). The user interaction module provides the boundary of building facades for 3D building modeling. And this CNN can detect facade elements even though multiple architectural styles and complex scenes are within the images. Moreover, user interaction module is designed as simple as possible to make it easier to use for both of expert and non-expert users. Meanwhile, we conducted a usability testing and collected feedback from participants to better optimize platform and user experience. In general, the usage of VGI data reduces labor and device costs, and CNN simplifies the process of elements extraction in 3D building modeling. Hence, our proposed platform offers a promising solution to the 3D modeling community.
  • 关键词:3D building modeling ; VGI ; convolutional neural network ; user interaction ; low cost
国家哲学社会科学文献中心版权所有