Cities are becoming smart. Over the past two decades, urban environments worldwide have integrated digital technologies into nearly every aspect of daily life—from energy grids and transportation systems to public administration and civic engagement. By 2023, over one thousand active smart city projects were underway worldwide, with roughly one hundred new smart cities added every year. The increasing adoption of data-driven urban technologies reshapes how cities operate—but this ongoing transformation is still commonly overlooked in US doctrine and training. Military experts note that the modern city constitutes “the least understood of potential conflict environments.” As cities evolve into digital terrains, the failure to adapt military understanding and readiness to this new urban reality poses blind spots.

Where individual smart city technologies are taken into consideration, they usually are presented as potential advantages for US and allied forces. This suggests a one-sided and potentially overly optimistic perception—one that may obscure an underappreciated threat landscape for the US armed forces. As urban conflict becomes more frequent, overlooking this evolving threat environment risks leaving forces underprepared—or unprepared—for the operational realities of emerging urban battlefields.

The number of conflicts in cities is expected to further increase in the years to come—particularly in the Global South, where the rise of smart cities coincides with urban population growth. City governments and private investors across Africa, Asia, and Latin America are accelerating their deployments of smart city technologies. Some Latin American cities are outperforming Western smart city initiatives. As of 2025, Santiago, Chile, for example, is home to the largest smart fleet of electric buses outside of China. Similarly, India’s national Smart Cities Mission has funded 8,067 projects across one hundred designated cities. African countries—including Kenya, Rwanda, Nigeria, Ethiopia, Morocco, and Mauritius—also initiated smart city programs. Together, these developments underscore the worldwide trend toward reconfiguring urban agglomerates into data-driven ecosystems.

The Urban Surveillance Environment

Traditionally, urban terrain has been assessed based on physical features like buildings, infrastructure, and chokepoints. According to retired Lieutenant Colonel Louis DiMarco “The nature of how armies conduct operations in cities is a function of city design and weapons technology.” However, in the hyperconnected smart city, networked sensors, pervasive data collection, and AI-powered analytics create a persistent surveillance overlay.

This environment, encompassing devices from public CCTV cameras to traffic sensors, offers unprecedented situational awareness to the controlling force. Already today, the metropolises of geopolitical competitors are blanketed by surveillance networks. Estimates suggest that Moscow hosts between 230,000 and 250,000 CCTV cameras, including around 217,000 tied to an AI-powered system allowing facial recognition. Across Russia, an estimated twenty-one million cameras exist. In Asia, the scale is more staggering. China alone reportedly runs up to seven hundred million CCTV cameras, with Beijing boasting 11.2 million—roughly one for every two of its inhabitants. China’s vast surveillance apparatus combines CCTV, facial recognition, drones, microphone arrays, “smart” lampposts, and citywide AI platforms. Together, they transform urban spaces into surveillance environments that compress the time window for maneuver, concealment, deception, and—arguably most critically—decision-making.

At the heart of the smart city lies the Internet of Things, where smart devices are interconnected, exchanging information via familiar technologies such as WiFi and Bluetooth but also other wireless communication technologies like low-power wide area network, ultra-wideband, and radio-frequency identification. The immense volume of data generated by a smart city—combined with its rapid refresh rate and AI-enabled big data analytics—allows for real-time correlation across diverse inputs. A cluster of motion sensor triggers, a handful of CCTV sightings of unfamiliar faces, and a spike in local radio traffic can be synthesized into a coherent pattern. From this, the system may autonomously infer the presence of an unauthorized group moving through a specific sector and immediately alert defending forces. Even if each individual signature by itself might be minuscule, the system compiles them into an actionable alarm.

Urban surveillance is also progressively reshaping the very architecture of security, for instance by replacing gates and guard huts. AI-based video analytics, radio-frequency pattern-of-life monitoring, and anomaly-detection algorithms can raise instant alerts—for example, when lone figures move against rush-hour flows—thereby automating duties once assigned to guards at fixed checkpoints. This extends to the physical layout of public spaces, where traditional defensive architecture is increasingly supplemented, or even replaced, by strategically deployed sensor arrays, smart lighting, and integrated camera networks that create virtual perimeters. These adaptive, data-driven security zones can dynamically adjust their surveillance intensity and response protocols based on real-time threat assessments. The technologies alter how physical access and movement are managed, oftentimes replacing rigid structures like walls or security fences.

Implications for Urban Warfare

The increasingly smart urban surveillance environment challenges traditional assumptions. One such common belief is that urban terrain offers anonymity and concealment. In a sensor-rich city, this assumption may no longer hold. For instance, if soldiers cut power to a building to darken it as a stealth tactic, the sudden power outage might be logged by the city’s smart grid and raise suspicion. If they avoid radio use and move quietly (electromagnetic and acoustic discipline), but in doing so trigger a traffic camera or a motion detector, the mission may still be compromised.

Actions once considered low-signature—like moving in a crowd or driving an unmarked vehicle—might now trigger alerts if AI analytics notice irregular patterns. For instance, a group of individuals not using cell phones at all in a cell phone–saturated city might itself be a red flag. In some authoritarian smart cities where the population is bio-tagged and tracked, outsiders may stand out immediately. Additionally, machine learning and trajectory models—already fielded in smart cities—forecast where and when suspicious activity is likely, letting security forces pre‑position assets or lock down quarters before an assault begins. Together, these capabilities chip away at long‑standing beliefs that urban clutter provides reliable concealment and prevents the targeting of US and allied forces.

While a smart city may deploy similar surveillance technologies as other smart cities, their implementation and impact are unique to each urban environment. As urban studies experts suggest, “every city has its approach to digitalization, and it is probably impossible to unify every city’s digitalization process.” Consider, for example, Bengaluru, Wuhan, and New York City. The three cities are home to roughly fourteen, nine, and nineteen million people respectively. Wuhan’s per capita budget is thirty-eight times as large as Bengaluru’s, and New York City’s budget dwarfs Bengaluru’s by a staggering 164 times. These comparisons suggest that the integration of smart technologies plays out notably differently in each city. As a 2014 study by US Army Strategic Studies Group fellows rightly concluded, “every megacity is unique and must be understood within its own historical, cultural, local, regional and international context.” Intimate knowledge of the specific city is paramount.

In the best-case scenario, this knowledge includes information on how a city’s Internet of Things combines the varied sensor networks. Usually, it does so imperfectly. Brand‑new smart lampposts may sit next to decades‑old traffic lights that still run on isolated controllers, creating digital blind spots where data drops out or systems cannot talk to one another. For US military planners, such seams are opportunities. If enough intelligence and data of an adversary city is available, optimally these blind spots could be mapped and tested through digital-shadow models, which are virtual replicas of the city. Even when the shadow is incomplete, AI‑enabled course‑of‑action models can ingest the partial data, run thousands of simulated scenarios, and expose vulnerabilities in the city’s smart infrastructure.

In an era where cities are evolving into data-driven surveillance environments, the future of urban warfare hinges not just on mastering concrete and steel, but on navigating surveillance and data. Smart cities complicate assumptions about concealment, mobility, and surprise, transforming established tactics into potential liabilities. To prevail in this shifting battlespace, US forces must train to understand and control digital infrastructure, surveillance networks, and data flows. Tomorrow’s victories in urban warfare will belong not just to those with superior firepower—but to those who can outmaneuver both the adversary and the algorithm.

Anna M. Gielas holds a PhD in the history of science from the University of St Andrews (United Kingdom). After earning fellowships at Harvard University and, most recently, the University of Cambridge, she is currently pursuing a second PhD focused on the integration of emerging (neuro)technologies into the armed forces. Anna has published widely in academic journals.

The views expressed are those of the author and do not reflect the official position of the United States Military Academy, Department of the Army, or Department of Defense.

Image credit: Tbatb