REMOTE SENSING AND GIS APPLICATIONS: Closing the Data Gap - From Static Maps to Dynamic Decision Support in the Global South

Abstract

In the developing world, and particularly in Sub-Saharan Africa, policies are often drafted in the dark. Decision-makers face a chronic data gap, a lack of consistent, high-resolution, and historical ground data regarding environmental change. Whether it is predicting the subsequent flood in a flood-prone area or estimating the maize harvest in an agricultural district, the physical infrastructure to monitor these dynamics is often missing. Remote Sensing (RS) and Geographic Information Systems (GIS) have long served as essential tools for spatial analysis, providing unparalleled insights into natural and human-made environments through the systematic collection and analysis of spatial data (Teshaev et al., 2024). More recently, machine learning has emerged as a transformative approach within GIS and RS, enabling more accurate, efficient, and scalable analysis of complex spatial datasets. This Special Issue, Remote Sensing and GIS Applications in the Developing World, demonstrates that geospatial science is no longer merely filling these gaps; it is leapfrogging traditional monitoring infrastructure altogether.