Urban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach

ABSTRACT

Accurate spatial-temporal mapping of urban land-use and land-cover (LULC) provides critical information for planning and management of urban environments. While several studies have investigated the significance of machine learning classifiers for urban land-use mapping, the determination of the optimal classifiers for the extraction of specific urban LULC classes in time and space is still a challenge especially for multitemporal and multisensor data sets. This study presents the results of urban LULC classification using decision tree-based classifiers comprising of gradient tree boosting (GTB), random forest (RF), in comparison with support vector machine (SVM) and multilayer perceptron neural networks (MLP-ANN). Using Landsat data from 1984 to 2020 at 5-year intervals for the Greater Gaborone Planning Area (GGPA) in Botswana, RF was the best classifier with overall average accuracy of 92.8%, MLP-ANN (91.2%), SVM (90.9%) and GTB (87.8%). To improve on the urban LULC mapping, the study presents a post-classification multiclass fusion of the best classifier results based on the principle of feature in-feature out (FEI-FEO) under mutual exclusivity boundary conditions. Through classifier ensemble, the FEI-FEO approach improved the overall LULC classification accuracy by more than 2% demonstrating the advantage of post-classification fusion in urban land-use mapping.

KEYWORDS:

• Urban land-use land-cover
• Gradient tree boosting
• Random forest
• Support vector machine
• Multilayer perceptron neural networks
• Post-classification feature fusion

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data used in this study was obtained from the United States Geological Survey (USGS): https://earthexplorer.usgs.gov/. The rest of the data are as presented in this paper. The image data classification was carried out within the Google Earth Engine (GEE).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

 This research project was funded by both the USAID Partnerships for Enhanced Engagement in Research (PEER) under the PEER program cooperative agreement number: AID-OAA-A-11-00012 and the University of Botswana Office of Research and Development (ORD)