# Application of Image Recognition Methods to Determine Land Use Classes
## 2 Related Works  
The application of ML algorithms in remote sensing has gained considerable momentum. Researchers are focusing on various aspects of land cover and landscape pattern analysis to address ecological and urban development issues. Recent studies highlight the versatility and robustness of these algorithms in dealing with complex spatial data across diverse environments. The following analysis examines relevant related research on land cover classification. Sentinel-2 satellite imagery has demonstrated superior performance for land use and land cover mapping in the northern Congo Republic, achieving higher overall accuracy (93.80%) and Kappa coefficient (0.89) compared to Landsat 9 (91.60% accuracy, Kappa 0.85) [13]. Additionally, Sentinel-2 imagery surpassed Landsat-9 in terms of spatial resolution, effectively mitigating the issue of mixed pixels. It also demonstrated a stronger alignment with in situ spectral measurements during local field comparisons [14]. Sentinel-2 imagery has been shown to have a clear advantage over Landsat imagery for snow cover detection due to its higher spatial and temporal resolution, which enables the better monitoring of snow dynamics, especially in mountainous regions like Switzerland [15]. The authors of [16] used the Random Forest (RF) algorithm to analyze land fragmentation in Nice, France. They used metrics like mean fragment size, core area, and contrast index. They strategically used pre-processed, cloud-free Sentinel-2 images to ensure high data quality, which is crucial for achieving reliable classification results. This study highlights the importance of high-quality, clean datasets in environmental monitoring and the effectiveness of RF in extracting meaningful landscape patterns. In contrast, the authors [4] addressed the challenge of cloud coverage in remote sensing by integrating Sentinel-1 synthetic-aperture radar (SAR) and Sentinel-2 optical data in their analysis of Northern Croatia. Their approach utilized the RF algorithm and demonstrated the potential of multi-sensor data fusion in enhancing land cover classification under less-than-ideal conditions. The use of overall accuracy (OA) and Cohen's Kappa (CK) as performance metrics provided a quantitative assessment of the method's efficacy, illustrating the hybrid approach's advantage in cloud-prone areas. The authors of [5] explored the effectiveness of combining Random Forest (RF) and Support Vector Machines (SVM) in Inner Mongolia, expanding the scope of algorithm application. This dual-algorithm approach sought to leverage the strengths of both RF and SVM, with a particular focus on enhancing the robustness of the classification process under varied environmental conditions. Their innovative use of object-oriented methods to mitigate disturbances from cloud cover represents a significant step forward in preprocessing techniques, potentially setting a new standard for handling optical data distortions in remote sensing. Cloud interpolation is a critical process in remote sensing, addressing data gaps in satellite imagery caused by cloud cover [11; 17]. Recent advancements have introduced innovative methods to enhance the accuracy of reconstructing these obscured areas. One such approach is the spatial-spectral random forest (SSRF), which utilizes spatially adjacent and spectrally similar pixels to fill gaps, effectively removing thick clouds by leveraging spatial and spectral information simultaneously [18]. Another significant development is the integration of deep learning techniques, such as partial convolution within a U-Net architecture. This approach has shown significant improvements in interpolating land surface temperature data by considering local ground-site air temperature measurements, leading to a 44% reduction in root mean square error compared to traditional methods. These advancements highlight the continuous improvement of cloud interpolation techniques, enhancing the reliability of satellite-derived datasets for environmental monitoring and analysis [19; 20; 21].