Machine Learning for Enhanced Classification and Geospatial Distribution Analysis

Yuxi Huang

doi:10.70393/6a6374616d.323535

OPEN ACCESS |Research Article ||1 January 2025

Machine Learning for Enhanced Classification and Geospatial Distribution Analysis

Yuxi Huang

University of Galway

yuxihuang0724@gmail.com

University of Galway, Ireland.

* Corresponding Author¹: Yuxi Huang, E-Mail: yuxihuang0724@gmail.com

Publication

Accepted Unknow ; Published 2025 January 1

Journal of Computer Technology and Applied Mathematics, 2025, 2(1), 3007-4126.

Abstract

Combining geospatial analysis with machine learning creates a novel synergy beyond conventional approaches to comprehending our spatial surroundings. The ability of machine learning to recognize intricate patterns and connections within data has made it an indispensable instrument for geospatial analysts. This integration makes complex analyses of satellite imagery, climatic data, and geographic data possible, providing previously complex insights to obtain via manual or rule-based methods.

Keywords

Machine Learning , Classification , Geospatial Distribution Analysis .

Metadata

DOI:

10.70393/6a6374616d.323535

ARK:

ark:/40704/JCTAM.v2n1a05

Pages: 27-32

References: 23

Disciplines: Artificial Intelligence

Subjects: Machine Learning

Cite This Article

APA Style

Huang, Y. (2025). Machine learning for enhanced classification and geospatial distribution analysis. Journal of Computer Technology and Applied Mathematics, 2(1), 27-32. https://doi.org/10.70393/6a6374616d.323535

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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