## 8 Results and Discussion of Reviewed Papers  
The datasets discussed in this section represent the application of the papers reviewed in this survey. Our analysis of the datasets revealed that KITE, RescueNet, and Biodrone are relatively new and have not been thoroughly investigated in the literature. While one of the datasets we reviewed, ERA, is not very recent, it still lacks a sufficient amount of study to fully emphasize its potential. The datasets included in our review were selected based on the number of citations their associated papers have received, emphasizing those with higher citation counts. We delved into several papers that make compelling use of the datasets we evaluated. In our examination, we carefully reviewed the details of the analysis of results and experiments conducted by other researchers. These researchers utilized the datasets we assessed as benchmarks and applied various methods. We have included the best results for the methods applied to the datasets we reviewed in this section and in Tables 6-8. \begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Name** & **Reference** & **Methods** & **Performance** \\ \hline \multirow{8}{*}{UAV-Assistant [15]} & \multirow{8}{*}{[75]} & \multirow{8}{*}{BPnP [76]} & ACC2 & ACC5 \\  & & & 95.2 & 98.36 \\  & & & 55.31 & 85.34 \\ \cline{3-4}  & & & HigherHRNet [25] & ACC2 & ACC5 \\  & & & 89.92 & 97.75 \\ \cline{3-4}  & & & \multirow{2}{*}{HRNet [77]} & ACC2 & ACC5 \\  & & & 90.75 & 98.04 \\ \hline \hline \end{tabular}  
* \({}^{1}\): \"Plot\" here refers to the forest types that were looked at in the FOR-instance dataset release.  
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Name** & **Reference** & **Methods** & **Performance** \\ \hline \multirow{8}{*}{AIDER [8]} & \multirow{8}{*}{[78]} & EmergencyNet & memory (MB) & F1 Score (\%) \\  & & 0.368 & 95.7 \\ \cline{2-4}  & & & memory (mB) & F1 Score (\%) \\  & & & 59.39 & 96.4 \\ \cline{2-4}  & & & memory (MB) & F1 Score (\%) \\  & & & 96.4 & 96.1 \\ \cline{2-4}  & & & MSE & PSNR \\  & & & 0.042 & 61.898 \\ \cline{2-4}  & & & MSE & PSNR \\  & & Genetic Algorithm & 0.06 & 60.349 \\ \cline{2-4}  & & & MSE & PSNR \\  & & & 0.12 & 57.339 \\ \cline{2-4}  & & & & MSE & PSNR \\  & & & & 0.165 & 55.956 \\ \hline \hline \multirow{8}{*}{DarkTrack2021 [19]} & \multirow{8}{*}{[80]} & SAM-DA-Track & AUC & Precision (normalized) & Precision \\  & & 0.451 & 0.524 & 0.593 \\ \cline{1-1} \cline{2-4}  & & UDAT & AUC & Precision (normalized) & Precision \\  & & & 0.421 & 0.499 & 0.570 \\ \cline{1-1} \cline{2-4}  & & SiamBAN & AUC & Precision (normalized) & Precision \\  & & & 0.422 & 0.491 & 0.566 \\ \cline{1-1} \cline{2-4}  & & SiamAPN & & DP & NDP & AUC \\  & & & 0.43 & 0.389 & 0.446 \\ \cline{1-1} \cline{2-4}  & & SiamAPN++ & & DP & NDP & AUC \\  & & & 0.494 & 0.446 & 0.375 \\ \hline \hline \multirow{8}{*}{UAV-Human [7]} & \multirow{8}{*}{[82]} & \multirow{8}{*}{Proposed Novel Method} & Precision & Recall & F1 Score \\  & & & 0.49 & 0.49 & 0.48 \\ \cline{1-1} \cline{2-4}  & & & \multirow{2}{*}{CLIP [84]} & Top1/Top5 (Filtering ratio 90\%) \\  & & & & 1.79/7.05 \\ \cline{1-1} \cline{2-4}  & & & \multirow{2}{*}{ViFi CLIP [85]} & Top1/Top5 (Filtering ratio 90\%) \\  & & & & 4.67/15.18 \\ \cline{1-1} \cline{2-4}  & & & & CSv1 & CSv2 \\ \cline{1-1} \cline{2-4}  & & & 44.33 & 70.69 \\ \cline{1-1} \cline{2-4}  & & & CSv1 & CSv2 \\ \cline{1-1} \cline{2-4}  & & & 45.72 & 71.84 \\ \cline{1-1} \cline{2-4}  & & & \multirow{2}{*}{FR-AGCN [87]} & CSv1 & CSv2 \\  & & & 43.98 & 69.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance metrics and results for different datasets and methods.