Kolmogorov-Arnold Networks for Spatially Independent Multispectral Land Classification
arXiv:2608.13769v1 Announce Type: new Abstract: Land classification from satellite imagery is important for land management, environmental monitoring, and urban planning. Machine learning methods such as random forests and multilayer perceptrons have shown strong performance on multispectral data, while the Kolmogorov-Arnold network has emerged as an alternative architecture with compact model structures. This study evaluates the Kolmogorov-Arnold network for land classification using Landsat 8 imagery and compares it with random forest and multilayer perceptron models. The models were trained and tested on data from Edmonton, Alberta and evaluated on an independent dataset from Calgary, Alberta across five land classes: agriculture, urban, water, forest, and bare ground. For the Calgary dataset, the Kolmogorov-Arnold network matched the accuracy of the random forest and outperformed the multilayer perceptron, while requiring substantially fewer trainable parameters and providing greater interpretability.
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[Submitted on 13 Aug 2026]
Title:Kolmogorov-Arnold Networks for Spatially Independent Multispectral Land Classification
View a PDF of the paper titled Kolmogorov-Arnold Networks for Spatially Independent Multispectral Land Classification, by Katherine L. Bauer and 3 other authors
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Abstract:Land classification from satellite imagery is important for land management, environmental monitoring, and urban planning. Machine learning methods such as random forests and multilayer perceptrons have shown strong performance on multispectral data, while the Kolmogorov-Arnold network has emerged as an alternative architecture with compact model structures. This study evaluates the Kolmogorov-Arnold network for land classification using Landsat 8 imagery and compares it with random forest and multilayer perceptron models. The models were trained and tested on data from Edmonton, Alberta and evaluated on an independent dataset from Calgary, Alberta across five land classes: agriculture, urban, water, forest, and bare ground. For the Calgary dataset, the Kolmogorov-Arnold network matched the accuracy of the random forest and outperformed the multilayer perceptron, while requiring substantially fewer trainable parameters and providing greater interpretability.
Comments: 10 pages, 10 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.13769 [cs.CV]
(or arXiv:2608.13769v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.13769
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Katherine Bauer [view email] [v1] Thu, 13 Aug 2026 20:53:49 UTC (22,504 KB)
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