Fine-scale classification of urban land use and land cover with PlanetScope imagery and machine-learning strategies in the City of Cape Town, South Africa
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Date
2022
Authors
Lefulebe, Bosiu E.
Van der Walt, Adriaan
Xulu, Sifiso
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
Urban land use and land cover (LULC) change can be efficiently monitored with high-resolution satellite products for a variety of purposes, including sustainable planning. These, together with machine learning strategies, have great potential to detect even subtle changes with satisfactory accuracy. In this study, we used PlaneScope Imagery and machine learning strategies (Random Forests, Support Vector Machines, Naïve Bayes and K-Nearest Neighbour) to classify and detect LULC changes over the City of Cape Town between 2016 and 2021. Our results showed that K-Nearest Neighbour outperformed other classifiers by achieving the highest overall classification of accuracy (96.54% with 0.95 kappa), followed by Random Forests (94.8% with 0.92 kappa), Naïve Bayes (93.71% with 0.91 kappa) and Support Vector Machines classifiers with relatively low accuracy values (92.28% with 0.88 kappa). However, the performance of all classifiers was acceptable, exceeding the overall accuracy of more than 90%. Furthermore, the results of change detection from 2016 to 2021 showed that the high-resolution PlanetScope imagery could be used to track changes in LULC over a desired period accurately.
Description
Keywords
Urban land use, Machine learning, PlanetScope, Random forests, Support vector machines, Naïve Bayes, K-nearest neighbour, Cape Town
Citation
Lefulebe, B.E., Van der Walt, A., & Xulu, S. (2022). Fine-scale classification of urban land use and land cover with PlanetScope imagery and machine-learning strategies in the City of Cape Town, South Africa. Sustainability, 14, 9139. https://doi.org/10.3390/su14159139