Determining the optimal generalization operators for building footprints using an improved graph neural network model

IF 3.3 4区地球科学 Q2 ENVIRONMENTAL SCIENCES

Geocarto International Pub Date : 2024-01-30 DOI:10.1080/10106049.2024.2306265

Xinyu Niu, Haizhong Qian, Xiao Wang, Limin Xie, Longfei Cui

引用次数: 0

Abstract

Determining the optimal generalization operators of city buildings is a crucial step during the building generalization process and an important aspect of realizing cross-scale updating of map data...

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利用改进的图神经网络模型确定建筑足迹的最佳泛化算子

确定城市建筑物的最佳泛化算子是建筑物泛化过程中的关键步骤，也是实现地图数据跨尺度更新的重要方面...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.