Analysis of geo-spatiotemporal data using machine learning algorithms and reliability enhancement for urbanization decision support - Kwame O. Hackman; Xuecao Li; Daniel Asenso-Gyambibi; Emmanuella A. Asamoah; Isaac. D. Nelson (International Journal of Digital Earth, 2020)
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.