Comparison of machine learning and parametric methods for the discrimination of urban land cover types

IF 3.3 4区地球科学 Q2 ENVIRONMENTAL SCIENCES

Geocarto International Pub Date : 2024-07-22 DOI:10.1080/10106049.2024.2380372

Enkhmanlai Amarsaikhan, Damdin Enkhjargal, Enkhtuya Jargaldalai, Damdinsuren Amarsaikhan

引用次数: 0

Abstract

The aim of this study is to compare the performances of different machine learning and parametric techniques for differentiating highly mixed urban land cover classes in Ulaanbaatar, the capital ci...

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机器学习和参数方法在城市土地覆被类型判别方面的比较

本研究旨在比较不同机器学习技术和参数技术在区分首都乌兰巴托高度混合的城市土地覆被类别方面的性能。

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.