Digital and Geographical Feature Detection by Machine Learning Techniques Using Google Earth Engine for CPEC Traffic Management

Abstract

The center of human settlements is in the cities, which must have high-quality habitats for their inhabitants. Many megachallenges of urbanization, population development, global advancement, environmental destruction, traffic management, and climate change must be addressed. This study is aimed at understanding how to maintain balanced land development in rapidly urbanizing towns to solve this challenge and mobility issues. Climate and weather forecasts, land cover, environmental indices, nonoptical and optical wavelengths, water history, and air quality are only some of the datasets available on Google Earth Engine, a publicly usable data repository. Machine learning techniques, i.e., random forest (RF), support vector machine (SVM), and classification and regression tree (CART), are used to monitor spatial-temporal change regarding water, vegetation, and urbanization for Pakistan from 2013 to 2021 using Landsat 8. The detection of urban land suitability concerning multiple metrics such as ecological response variables, environmental tension, socio-economic development potential, and natural resource potential is also found. Dataset features were classified as bands in the Google Earth Engine. Moreover, for 2020 and 2021, classification results showing the change in water, vegetation, and urbanization are also represented concerning China Pakistan Economic Corridor (CPEC) highway and the railway track to monitor and control traffic and its management.