Secure and Privacy-Preserving UAV and Satellite Remote Sensing for Disaster Resilience: A Bibliometric Analysis and Deployment-Oriented Review

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Satellite and unmanned aerial vehicle (UAV) imagery supports disaster assessment and emergency response, particularly when ground access or terrestrial monitoring infrastructure is disrupted. However, unauthorized access, data manipulation, privacy breaches, and communication failures can compromise the reliability and security of information used for emergency-response decisions. This study examines research on secure and privacy-preserving UAV and satellite remote sensing, with particular attention to the security mechanisms proposed and their evaluation in disaster-related and other relevant sensing applications. Bibliometric analysis of 1320 Scopus records identifies 113 recurring author keywords organized into 11 thematic clusters, covering computer vision and sensor fusion, security and privacy, disaster management, artificial intelligence, distributed computing, federated learning, and related sensing applications. The keyword network reveals connections among UAV and satellite remote sensing, machine learning, blockchain, network security, and privacy-preserving technologies, indicating the breadth of research associated with protecting, processing, and sharing remotely acquired information. A separate analysis of 35 Web of Science (WoS) records identifies 17 recurring keywords, eight of which also appear in the Scopus network, indicating overlap in platform, security, and learning themes across the two datasets. A technical comparison of 35 selected studies examines security functions, application contexts, and evaluation approaches, including their relevance to disaster-imaging conditions. Seven studies receive closer examination using evidence from UAV and satellite imagery, application-specific datasets, physical testbeds, and disaster mapping validated through field observations. The technical comparison indicates that mechanisms addressing data acquisition, communication, privacy-preserving processing, provenance, and access control are often evaluated independently or under different experimental conditions. These findings highlight the need to evaluate how such mechanisms operate together in disaster-imaging applications, particularly under disrupted connectivity, limited computing resources, and information-sharing requirements involving multiple emergency-response organizations.