Analysis of geo-spatiotemporal data using machine learning algorithms and reliability enhancement for urbanization decision support. Issue 12 (1st December 2020)

• Record Type: Journal Article
• Authors: Hackman, Kwame O.; Li, Xuecao; Asenso-Gyambibi, Daniel; Asamoah, Emmanuella A.; Nelson, Isaac. D.
• Is Part Of: International journal of digital earth. Volume 13:Issue 12(2020)
• Page Start: 1717
• Page End: 1732
• Publication Date: 2020-12-01
• Subjects: Urbanization -- Kumasi -- support vector machine -- random forest -- Landsat; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2020.1805036
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: We present systematic analyses of the temporal dynamics of the growth of Kumasi, the fastest growing city in Ghana using 20-year Landsat time-series data from 2000 to 2020 (with 1986 Landsat image as a baseline). Two classification algorithms – random forest (RF) and support vector machines (SVM) – were used to produce binary (built-up / non-built up) maps for all years within the temporal span. We further implemented an anomaly detection and temporal consistency algorithm followed by a changing logic to correct the classification anomalies due to image contamination from the cloud and other sources. The mean overall accuracies obtained for RF and SVM were 94.9% (kappa = 0.90) and 95.5% (kappa = 0.91), respectively. Our results reveal that the mean built-up area percentages of the metropolis are approximately 74, 65, 47, and 23 for the years 2020, 2010, 2000, and 1986, respectively, representing a mean annual change of 3.5% over the 34 years. With the present lack of labeled data in Ghana for in-depth analyses of the evolution of land use, we believe that this study serves as an initial attempt to a better understanding of the effects of increasing anthropogenic activities due to urbanization, on human and environment health.