Integrated GIS Multi-Criteria Analysis with AHP and Remote Sensing for Identifying and Monitoring High-Risk Areas of Illegal Border Crossing

Preventing large-scale illegal migration is one of the EU's highest priorities. In this study, we analyse the potential for integrating and fusing remote sensor data with a wider range of data streams to enhance border security situational awareness, specifical-ly targeting illegal migration. The aim was to develop a dynamic predictive risk analysis model to identify high-risk zones for illegal border crossings at Croatia's external EU borders. The model’s methodological framework is based on the integration of Geo-graphic Information Systems (GIS), Multi-Criteria Analysis (MCA), and the Analytic Hi-erarchy Process (AHP). The model utilizes various environmental and infrastructure var-iables derived from the open-source databases ESA WorldCover and OpenStreetMap to generate a categorised risk map showing areas of lowest, moderate, and highest risk for illegal border crossing. High-resolution historical satellite imagery showing activities re-lated to illegal migration is used for model verification and generation of labelled da-tasets for AI training. Features such as suspicious vans, river boats, tyre tracks, tents, il-legal campsites, and clusters of individuals were observed in high-resolution Airbus and Maxar historical satellite images. The model can be used for various practical applica-tions, including the strategic allocation of surveillance resources and the enhancement of frontier and pre-frontier intelligence, enabling more informed actions and optimised op-erations.