.png){kind=icon}
返回
Mountainous road vector data update method based on matching point pair grouping
.png){kind=icon}
PRE
AI
DOI：10.1080/01431161.2024.2349264.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The vector information of mountainous roads is great significance, including regional economics, emergency response, disaster management, among others. Nevertheless, there are irregular deformations in existing vector data, coupled with incomplete extraction results for mountainous roadways. These issues pose challenges in establishing a matching model between road extraction results and existing vector data, significantly reducing the efficiency of automated updates for the current vector data. Targeting roads as a distinct artificial feature and leveraging their topological connectivity, we propose a vector data updating method based on matching point pair grouping. Firstly, we employ the SDUNet model to extract mountainous road and utilize the Superglue model to match road binary images with vector binary images, resulting in sets of initial matching point pairs. Subsequently, we propose a road line extraction model that groups matching point pairs, eliminates misaligned pairs based on road topological connectivity, and establishes a mapping relationship between road lines and vector lines. Ultimately, through road line optimization and vector line optimization methods, we accomplish the automatic updating of vector data for mountainous roadways. Through a comprehensive analysis of over 2600 vector data, our method demonstrates substantial advantages in terms of both F1 and IoU compared to alternative methods.
Keyword:
Mountainous roads
update vector data
matching point pair grouping
topological connectivity
期刊
IF:
2.6
论文数:
1.2W
被引数:
2.7W
机构
暂无机构信息
引用论文
Using Agile Practices to Solve Global Software Development Problems -- A Case Study采用敏捷实践解决全球软件开发问题——一个案例研究
.png){kind=icon}
PREAI
分享收藏DPENet: Dual-path extraction network based on CNN and transformer for accurate building and road extractionDPENet: 基于CNN和transformer的双路径提取网络，用于准确提取建筑物和道路
OAAI
OAAI分享收藏Split Depth-Wise Separable Graph-Convolution Network for Road Extraction in Complex Environments From High-Resolution Remote-Sensing Images分割深度方向可分离的图卷积网络，用于从高分辨率遥感图像中提取复杂环境中的道路
PREAI
分享收藏A Case study of cross cultural communication issues for Filipino call centre staff and their Australian customers一项关于菲律宾呼叫中心员工与其澳大利亚客户之间跨文化沟通问题的案例研究
PREAI
分享收藏
查看更多内容