Edge-AI Sensor Fusion for UAV-Based Perception in Large-Scale Surveillance and Environmental Monitoring

Unmanned Aerial Vehicles (UAVs) equipped with multi-sensor payloads (cameras, LiDAR, multispectral and thermal imagers) are increasingly deployed for large-scale surveillance and environmental monitoring, from infrastructure inspection to crop health assessment and wildlife tracking. These deployments generate large volumes of heterogeneous sensor data that must be processed reliably, often with limited onboard compute and intermittent connectivity to centralized systems. Edge-AI addresses this by moving perception and decision-making closer to the sensor, reducing latency and bandwidth demands while enabling real-time response in the field. Achieving robust perception under these conditions requires effective fusion of multiple sensor modalities, lightweight models suited to onboard hardware, and architectures that remain reliable across varied, often unpredictable environments.This Research Topic aims to bring together recent advances in edge-AI and sensor fusion techniques applied to UAV-based perception, with a particular focus on large-scale surveillance and environmental monitoring applications. We welcome contributions that advance onboard processing under constrained compute and power, robust fusion of visual, LiDAR, thermal, and multispectral data for scene understanding, and the deployment of these systems in real-world settings such as security surveillance, agricultural and environmental monitoring, and infrastructure inspection. The intention is to build a collection that speaks to both the technical challenge of edge-AI sensor fusion and its practical application at scale, giving researchers working on onboard architectures a venue alongside those applying UAV perception in the field.We welcome Original Research, Review, and Methods articles from researchers across robotics, computer vision, remote sensing, and environmental science, addressing themes including, but not limited to:- Lightweight deep learning architectures for onboard UAV perception- Edge-cloud collaborative processing for large-scale deployments- Multi-sensor calibration and synchronization for UAV platforms- Fusion of visual, LiDAR, thermal, and multispectral data for scene understanding- Target detection and tracking in cluttered or dynamic environmentsUAV-based security surveillance and wide-area monitoring- Environmental and agricultural monitoring (crop health, land cover, wildlife tracking)- Infrastructure inspection and anomaly detection using UAV sensor networksContributions combining strong technical development with clear real-world validation are especially encouraged, as are collaborations across institutions and disciplines that reflect the interdisciplinary nature of this field. Topic editors: Patrick Sebastian, Vooi Voon Yap, Utkal Mehta, Kishore Bingi Source: https://www.frontiersin.org/research-topics/80311/edge-ai-sensor-fusion-for-uav-based-perception-in-large-scale-surveillance-and-environmental-monitoring