Deep learning for secure imaging and video surveillance in smart cities: from bibliometric mapping to real-world implementations

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The integration of imaging sensors, unmanned aerial vehicles (UAVs), and satellite platforms has expanded capabilities in surveillance, smart-city systems, and remote sensing. Advances in deep learning and computer vision enable automated detection, tracking, and scene understanding across diverse environments, including safety-critical settings; however, these systems impose requirements for security, privacy, and adversarial robustness. This paper presents a dual-database bibliometric and science-mapping analysis of deep learning research for secure imaging and video surveillance. Two independently constructed corpora, derived from Scopus (𝒟 S ) and Web of Science (𝒟 W ) for the period 2010–early 2026, are analyzed using VOSviewer-based keyword co-occurrence clustering to identify dominant research themes and methodological trends across surveillance, smart cities, remote sensing, and computer vision; annual publication trend interpretation is restricted to complete calendar years from 2010–2025. Quantitative bibliometric analyses (e.g., publication trends, geographic distribution, institutional productivity, and keyword co-occurrence structure) are conducted on both 𝒟 S and 𝒟 W , with cross-database comparison used for validation. Complementarily, detailed analysis of prominent keywords and inter-cluster connectivity, as well as the review of real-world deployment systems and field-dataset studies, are conducted based on data from the Scopus dataset (𝒟 S ), which provides detailed author-keyword information for cluster-level interpretation and broader topical coverage. The deployment-oriented review distinguishes physical or edge implementations from studies using real-world datasets. The findings indicate deployment-oriented secure visual intelligence within the mapped literature, with the Scopus cluster structure separating spatio-temporal surveillance understanding, real-time object detection, remote sensing, biometric recognition, edge/cloud smart-city security, re-identification, adversarial robustness, UAV/drone sensing, crowd analytics, event summarization, and low-cost edge implementation. Increased emphasis on UAV-based imaging and adversarial robustness is also observed. This study provides a structured mapping of the research landscape and identifies gaps relevant to the development of reliable imaging systems in connected environments.