Categorizing Toll Gates using Data of Sentinel-1, Sentinel-2, Sentinel-5P, NOAA-20 Satellite, and Objects on Google Map: A Case Study in Indonesia

Abstract

This study proposes a framework to categorize toll gates based on the development level of their surrounding areas and localized economic activity profiles. The methodology integrates multi-source satellite imagery with geospatial features extracted from the Google Maps API. The localized economic context of 419 toll gates was analysed within a 1-km circular buffer zone. Within this radius, Sentinel-2 spectral bands (B2, B3, B4 and B8) extracted from Google Earth Engine (GEE) were classified into built-up and non-built-up pixels using a Random Forest algorithm to quantify the built-up area percentage. Gates with a built-up density of at least 40% were categorized as Class-1, representing infrastructure in highly developed regions. For the remaining gates, Sentinel-1 VV polarization, Sentinel-5P tropospheric NO₂ column density, and NOAA-20 VIIRS nighttime lights data were extracted via GEE, alongside point-of-interest (POI) counts from Google Maps. Following feature engineering across five initial variables, the backscatter coefficient (VV) and POI density were identified as the optimal features for clustering. Using these features, a k-Means algorithm grouped the remaining gates into Class-2 and Class-3 clusters, whose final socioeconomic profiles were interpreted using all variables. The classification yielded 297 Class-1 gates (highly developed areas with dense human and commercial activities), 51 Class-2 gates (moderate activity zones), and 71 Class-3 gates (low-to-moderate activity zones). To assess the long-term impacts of toll roads on regional economic growth, Class-3 gates warrant continuous observation, as their surrounding economic activity remains suboptimal. This research contributes to the fields of applied remote sensing and transportation infrastructure mapping by providing a cost-effective, automated methodology to periodically monitor spatiotemporal economic shifts around critical transport infrastructure.