Abstract

Reliable data on human mobility is critical for understanding population displacement during armed conflict, yet such information is often scarce, delayed, or inaccessible in crisis-affected regions. In this study, we present a case study of the Syrian Civil War, where we use satellite-derived vehicle detections as a potential complementary source of information on internal displacement patterns. We develop an approach that integrates a region-adapted vehicle detection model with a partial coverage correction method to estimate car counts from incomplete satellite observations. Using IDP reports from official sources, we evaluate the relationship between vehicle dynamics and displacement patterns across Syrian cities. We find a positive but moderate association between changes in car counts and reported displacement, driven primarily by agreement in the direction rather than the magnitude of change; this directional agreement strengthens substantially under conditions of high temporal satellite revisit, but at the aggregate level is not clearly distinguishable from a naive baseline. We also observe that satellite imagery and IDP reports are often complementary in terms of exact geography and time periods covered, suggesting that our remote sensing approach could help fill data gaps where official reporting is sparse. These findings suggest that satellite-based vehicle detection can offer complementary, qualitative information about displacement direction in data-scarce conflict settings under favorable observation conditions, though further work is needed to establish its reliability for estimating displacement magnitude or as a general-purpose monitoring tool.

Dataset

Syria-IDP-Car-Counts dataset (TSV (Tab-Separated Values) file) includes mapped humanitarian reports and vehicles detected from satellite imagery. Each column's description is provided below: