A classification model for power corridors based on the improved PointNet++ network

IF 3.3 4区地球科学 Q2 ENVIRONMENTAL SCIENCES

Geocarto International Pub Date : 2024-01-17 DOI:10.1080/10106049.2023.2297556

Li Bo, Liu Siyuan, Wang Fengxiang, Zou Cunyu

引用次数: 0

Abstract

Aiming at the existing deep learning classification model for power corridor point cloud still need to improve the classification efficiency and the robustness of the classification model to meet t...

查看原文本刊更多论文

基于改进型 PointNet++ 网络的电力走廊分类模型

针对现有的电力走廊点云深度学习分类模型，仍需提高分类效率和分类模型的鲁棒性，以满足电力走廊点云的分类需求。

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分钟内获得全文求助全文

来源期刊

Geocarto International ENVIRONMENTAL SCIENCES-GEOSCIENCES, MULTIDISCIPLINARY

CiteScore

6.30

自引率

13.20%

发文量

407

审稿时长

>12 weeks

期刊介绍： Geocarto International is a professional academic journal serving the world-wide scientific and user community in the fields of remote sensing, GIS, geoscience and environmental sciences. The journal is designed: to promote multidisciplinary research in and application of remote sensing and GIS in geosciences and environmental sciences; to enhance international exchange of information on new developments and applications in the field of remote sensing and GIS and related disciplines; to foster interest in and understanding of science and applications on remote sensing and GIS technologies; and to encourage the publication of timely papers and research results on remote sensing and GIS applications in geosciences and environmental sciences from the world-wide science community. The journal welcomes contributions on the following: precise, illustrated papers on new developments, technologies and applications of remote sensing; research results in remote sensing, GISciences and related disciplines; Reports on new and innovative applications and projects in these areas; and assessment and evaluation of new remote sensing and GIS equipment, software and hardware.