Tecnologías de vigilancia en la frontera México–Estados Unidos: drones y sistemas de inteligencia artificial (2001–2025)

In the first two decades of the twenty-first century, the Mexico–United States border has consolidated itself as a strategic space for the experimentation and application of advanced surveillance technologies. Following the September 11, 2001, attacks, U.S. border management prioritized national security and the control of irregular migration, incorporating unmanned aerial systems (UAS) and artificial intelligence (AI) platforms as fundamental pillars of its strategy. This study analyzes the evolution, implementation, and impact of these technologies between 2001 and 2025, drawing on reports from U.S. Customs and Border Protection (CBP), the Department of Homeland Security (DHS), the Government Accountability Office (GAO), and specialized academic literature. It also examines how these systems reshape the built environment and urban–border dynamics by influencing the spatial organization of surveillance infrastructures, migratory corridors, and buffer zones. The findings show that UAS were adapted to civilian surveillance operations, expanding coverage in remote areas. The integration of machine learning algorithms, multispectral sensors, and 5G networks has enabled approximately 80% coverage of the border, with accuracy rates nearing 95% in nighttime detections. Key results include reduced response times to under 10 milliseconds and enhanced predictive capabilities, though limitations such as data underutilization (up to 70%), server saturation, dependence on private contractors, and false positives (10–12%) persist, alongside biases in institutional sources and challenges in replicating models in the Global South. Although irregular migration also responds to economic factors such as labor demand in the agricultural sector, this analysis focuses specifically on the technical aspects of the border surveillance syste ......