Classification of Urban and Rural Villages with Machine Learning on Satellite Image Data and Points of Interest

Abstract

An evaluation of the Sustainable Development Goals with data disaggregated by residential area, namely urban and rural areas, is essential. This study proposes the use of satellite imagery and point of interest (POI) data with machine learning methods to classify urban and rural villages, specifically in North Sumatra Province. The data used includes satellite imagery from various sources, such as NOAA-20, Sentinel-2, Sentinel-5P, and Terra, as well as Google Maps, covering various variables including NTL, NDVI, NDBI, NDWI, NO?, CO, and LST, along with POIs categorized under education, economy, health, and entertainment. The machine learning methods used were Decision Tree and Support Vector Machine, with data imbalance addressed through resampling techniques such as Random Under sampling (RUS). The results of the study show that the Support Vector Machine model with RUS produced the best weighted average F1-score of 87.74% for the classification of urban and rural villages, with NTL being the most important feature in the model formation. This study is expected to be an alternative for BPS in the classification of urban and rural villages.