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Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia

journal contribution
posted on 2023-08-22, 20:34 authored by Jagannath Aryal, Chiranjibi Sitaula, Alejandro FreryAlejandro Frery
AbstractAccurate spatial information on Land use and land cover (LULC) plays a crucial role in city planning. A widely used method of obtaining accurate LULC maps is a classification of the categories, which is one of the challenging problems. Attempts have been made considering spectral (Sp), statistical (St), and index-based (Ind) features in developing LULC maps for city planning. However, no work has been reported to automate LULC performance modeling for their robustness with machine learning (ML) algorithms. In this paper, we design seven schemes and automate the LULC performance modeling with six ML algorithms-Random Forest, Support Vector Machine with Linear kernel, Support Vector Machine with Radial basis function kernel, Artificial Neural Network, Naïve Bayes, and Generalised Linear Model for the city of Melbourne, Australia on Sentinel-2A images. Experimental results show that the Random Forest outperforms remaining ML algorithms in the classification accuracy (0.99) on all schemes. The robustness and statistical analysis of the ML algorithms (for example, Random Forest imparts over 0.99 F1-score for all five categories and p value $$\le$$ ≤  0.05 from Wilcoxon ranked test over accuracy measures) against varying training splits demonstrate the effectiveness of the proposed schemes. Thus, providing a robust measure of LULC maps in city planning.

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Preferred citation

Aryal, J., Sitaula, C. & Frery, A. C. (n.d.). Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-40564-0

Journal title

Scientific Reports

Volume

13

Issue

1

Publisher

Springer Science and Business Media LLC

Publication status

Published online

Online publication date

2023-08-19

eISSN

2045-2322

Article number

13510

Language

en

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