Fusing Sentinel-2 and Sentinel-1 Data in Google Earth Engine for Road Infrastructure Mapping in Data-Scarce and Conflict Environments

Abstract

Reliable road network information is a prerequisite for infrastructure planning, humanitarian operations, and disaster response; however, such information often remains incomplete or outdated in conflict-affected and data-scarce regions. In Juba County, South Sudan, decades of conflict, institutional limitations, and environmental challenges have resulted in substantial gaps in road network data. This study introduces a low-cost, cloud-based approach that fuses radar and optical satellite imagery within Google Earth Engine to enhance road visibility and mapping under these conditions. By integrating multi-sensor satellite data with local knowledge and publicly available reference datasets, the approach enables the identification of previously unmapped roads and the correction of outdated road surface classifications. The results show that more than 71 km of undocumented roads were detected, increasing the known road network by approximately 1%, while achieving a connectivity rate of 98.8% with the existing network. These findings demonstrate that multi-sensor data fusion, combined with informed local interpretation, provides a scalable foundation for improving road inventories and generating GeoAI-ready baseline data in fragile and data-scarce environments.