# Application of Image Recognition Methods to Determine Land Use Classes
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Algorithm** & **Accuracy Metrics** & **Analyzed Region** & **Cloud Problem Handling** \\ \hline \hline
**(1)** & **(2)** & **(3)** & **(4)** & **(5)** \\ \hline
[16] & RF & OA = 74.3\% & Nice, France & Used pre-processed cloud-free Sentinel-2 images \\ \hline
[4] & RF & OA = 91.78\% & Northern Croatia & Used Sentinel-1 SAR and Sentinel-2 optical data, applied cloud masking strategies \\ \hline
[5] & RF, SVM & OA = 98.66\%, CK = 98.23\% & Inner Mongolia & Applied object-oriented methods to mitigate noise, no specific mention of cloud handling \\ \hline
[7] & RF & OA = 88.18\%, P = 93.55\%, F = 91.6\% & Ethiopia & Multi-temporal cloud-free compositing approach \\ \hline
[9] & RF & OA = 95.64\%, CK = 94\% & Yancheng, China & Sentinel-2 cloud masking and shadow removal techniques \\ \hline
[8] & RF & OA = 91.2\%, FL = 91.04\% & Various crop fields & Multi-temporal imagery for cloud interpolation \\ \hline
[22] & CNN & F1 = 75.49\% & Global & Used deep learning for cloud detection and mitigation in high-resolution images \\ \hline
[23] & RF & OA = 94.78\%, F1 = 78.32\% & Flood-prone areas & Combined cloud-free mosaicking with SAR data \\ \hline
[24] & RF & OA = 91.46\% & Canada & Scene Classification Layer (SCL) for cloud segmentation \\ \hline
[25] & NDVIML & OA = 83.75\% & Agroforestry areas & Cloud filtering via NDVI-based thresholding \\ \hline
[6] & RF, CNN & OA = 90.3\% & Remote sensing datasets & Combination of cloud masking and deep learning interpolation \\ \hline
[26] & CNN & GA = 88\% & Manitoba, Canada & Not specified \\ \hline
[27] & SVM, RF & OA = 86\% & Krakow, Poland & Sentinel-2 imagery pre-processing, addressed cloud interference \\ \hline
[28] & RF & OA = 88.16\% & Lingbei Rare Earth Mining Area, China & Not explicitly mentioned (hypersperstal data used, which is generally less affected by cloud issues than optical data) \\ \hline
[29] & OBIAFL & OA = 95.32\% & Pavia, Italy & Not specified \\ \hline
[30] & VSM & Not specified & Coastal zones in California, USA & Not specified \\ \hline
[31] & GBM, RF & OA = 87\% & Queensland, Australia & Explicitly addresses cloud issue; RF efficiently interpolates \\  & & & missing pixels caused by clouds \\ \hline
[32] & KNN, DT, SVM, RF & OA = 81\% & Algarve, Portugal & Uses good quality pre-processed Sentinel-2 Level-2A products \\ \hline
[33] & SVM, RF & F1 = 91.4\% & Bulgaria & Uses images with cloud cover \(<\)10\% and apply cloud, shadows and defective pixel masking \\ \hline
[34] & RF, SVM, XGB & OA = 82.96\% F1 = 73\%-97\% & Tatra Mountains, Central Europe & Authors use high quality Sentinel-2 Level-2A products \\ \hline
[35] & RF, SVM & F1 = 93\% & Tatra Mountains, Central Europe & Selects only those Sentinel-2 Level-2A scores that exhibit \\  & & & minimal cloud cover \\ \hline
[36] & KNN, SVM, DT, RF & CK = 87\% & Central Iran & Cloudy images omitted (dry climate) \\ \hline
[37] & RE KNN & R\({}^{2}\) = 87\% & Central Greece & Cloud masking \\ \hline
[38] & KNN, SVM, RF & OA = 86-93\% & China & Not specified \\ \hline
[39] & RF, SVM, DT, CART & OA = 94.8\%, CK = 93\% & Nainital, India & Authors use Google Earth Engine (GEE) for cloud free images \\ \hline
[40] & SVM, RF & OA = 901.1\%, FL = 87.8\% & Catalonia & Authors handle cloud problems in their analysis by using \\  & & F1 = 88.46\% & atmospheric and geometric corrections \\ \hline
[41] & SVM, RF, KNN, LR & R\({}^{2}\) = 79\% & Jilin Province, Northeast China & Sen2 Cor tool applied \\ \hline
[42] & RF, SVM & OA = 94.03\%, Lake Basin, Iran & Not specified \\ \hline
[43] & RF, CART & R\({}^{2}\) = 94.1\% & Romania & Authors use Google Earth Engine (GEE) for cloud free images \\ \hline
[44] & RF & OA = 87.15\% & Central Italy & Cloud masking \\ \hline \end{tabular}
\end{table}