Urban footprint extraction derived from worldview-2 satellite imagery by random forest and k-nearest neighbours algorithm

Abstract

High-resolution spatial data regarding the distribution of urban areas is fundamental concerning regional spatial planning and monitoring the development of built-up areas. Many researchers have extracted urban footprints using low to medium-resolution satellite imagery. For applications on a global and regional scale, low to medium image resolution are suitable. Nevertheless, higher image resolution is required on a local scale, down to a small urban area level. This study objective to mapping the built-up land and examine the accuracy of 2 machine learning algorithms. This investigation employs a novel approach that combines the utilization of remote sensing technology with the implementation of machine learning algorithms. We use Random Forest (RF) and K-Nearest Neighbours (KNN) machine learning algorithms. This study used a high-resolution (0.5 meter) satellite image derived formWorldView-2. We only used three visible channels (Red-Green-Blue) with a 450 – 690 nm wavelength. Integrating remote sensing and machine learning can adequately investigate the urban footprint area. Based on this research, the RF better than KNN algorithm. It is proven by the confidence iteration value and the overall accuracy of the RF and KNN algorithms, i.e., 73.32%, 71.99%, 82.08%, and 77.89% respectively. Based on WorldView-2 imagery acquired in 2015, the proportion of urban footprint is still lower than the green area with 41.75%: 58.24%, especially in the centre of the capital city of Bali Province. Such conditions are undoubtedly different in other urban areas in Bali. Even one city area, e.g., West Denpasar, which almost the entire area is dominated by the urban footprint area. Such conditions are a particular concern for the local government in managing future spatial planning regulations. It is recommended that the proportion of green open space remains a priority so that there are no environmental problems in urban areas (e.g., air pollution, flooding due to runoff problems, etc.).