## 3 Literature Review  
UAVs function without a human pilot present and can be remotely commanded or autonomously managed by computers onboard. They are used in diverse fields such as surveillance, aerial photography, agriculture, environmental monitoring, and military  
Figure 1: Workflow of this study. operations. UAVs greatly enhance computer vision jobs by collecting several forms of image and video data, in addition to satellite images, within the realm of datasets. In addition to the datasets examined in this work, recent research has investigated novel applications of UAVs beyond conventional purposes. These include real-time 3D AQI mapping for air quality assessment [3], wildfire detection systems that integrate UAVs with satellite and ground sensors [4], federated learning frameworks for energy-efficient UAV swarm operations [5], and extensive surveys that tackle air pollution monitoring difficulties. These efforts underscore the adaptability of UAVs in tackling urgent environmental and computational issues, while also revealing prospects for additional research in particular fields like disaster management and machine learning integration. Tables 1 and 2 encapsulate the datasets examined, which form the foundation of our analysis. \begin{table}
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**Dataset** & **Dataset Details** & **Paper Findings** & **Limitations** & **Future Work** \\ \hline RescueNet [6] & High-resolution images with pixel-level annotations for 10 classes, collected via UAVs after Hurricane Michael. & Attention-based and transformer-based methods performed best. & Time-consuming annotation process and potential lack of comprehensive & Further evaluation across different disaster scenarios to \\ \hline UAV-Human [7] & Comprises 67,428 multi-modal video sequences for action recognition, pose estimation, person re-identification, and attribute recognition. & Highest action recognition accuracies with night-vision and IR videos. Pose estimation methods achieved mAP scores of 56.5 and 56.9. & Potential overfitting, lack of subject diversity, and constrained capturing conditions. & Increase sample size and diversity, and capture conditions to enhance model robustness and generalization. \\ \hline AIDER [8] & Includes 2565 manually gathered images of disaster events with augmentations. & Development of ERNet, achieving near state-of-the-art accuracy (90\%) and over 50 fps on a CPU platform. & Does not extensively discuss real-time implementation challenges, robustness in diverse conditions, or hyperparameter tuning. & Integrate ERNet with algorithms for detecting people and vehicles, use additional modalities like infrared cameras, and optimize the model for improved and accuracy. \\ \hline AU-AIR [9] & A total 32,823 labeled video frames with object annotations and flight data. & YOLOv3-tiny and MobileNetv2-SSD Lite for real-time object detection on UAVs showed potential for onboard computer applicability. & Focus on traffic surveillance may limit applicability to other scenarios, lacks advanced baselines for tasks like UAV navigation. & Enhance dataset diversity, incorporate more environmental contexts, and develop additional baselines leveraging sensor data for broader applications. \\ \hline ERA [10] & A total 2,864 videos capturing events in a wide range of settings and sizes. & DenseNet-201 achieved the highest accuracy of 62.3\% in single-frame classification. & Dataset size, class imbalance, and challenge of distinguishing events from normal videos. & Focus on attribute recognition, temporal cue exploitation, and addressing challenging cases like human action recognition. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of research and findings of UAV datasets discussed.