# Application of Image Recognition Methods to Determine Land Use Classes
###### Abstract  
The increasing availability of satellite data and advances in machine learning (ML) have significantly enhanced land use image classification for environmental monitoring. However, the primary challenge in land use classification using satellite imagery lies in the presence of cloud cover, variations in data resolution, and seasonal changes, which impact classification accuracy and reliability. This paper aims to improve the assessment of land cover changes by proposing a hybrid ML, cloud interpolation, and vegetation indices-based approach. The proposed approach was implemented by using a random forest (RF) classifier, combined with cloud interpolation and vegetation indices, to classify land use Sentinel-2 satellite imagery in the Baltic States. The experimental results demonstrate that the proposed approach achieves an accuracy rate above 90%, effectively demonstrating its capacity to distinguish between various land use types. We believe that this study and its results will inspire researchers and practitioners to further work towards land use classification by applying ML algorithms and offer valuable insights for future classification tasks involving noise digitalization and research. land use classification; image recognition; Sentinel-2; random forest; machine learning; cloud interpolation 2025  
21 March 202  
an outstanding accuracy of 98.66% and a kappa coefficient of 0.9823. Furthermore, the authors [6] demonstrated the applicability of RF in large-scale land cover classification by leveraging segmentation models in remote sensing imagery analysis. In [7], the authors investigated cropland and crop type classification in Ethiopia, highlighting the potential of integrating Sentinel-1 and Sentinel-2 time-series data within Google Earth Engine. The study demonstrates that RF remains highly effective when applied to large-scale agricultural monitoring, achieving remarkable classification accuracy. Additionally, the authors of [8] have employed RF with multi-temporal RADARSAT (a Canadian remote sensing Earth observation satellite program) constellation mission data, demonstrating its ability to improve crop classification performance. In [9], the authors extended the applicability of RF to coastal wetland vegetation classification, reinforcing its versatility across various ecosystems. These studies collectively emphasize the continued relevance and superiority of RF in remote sensing applications, positioning these authors as leading contributors to the field. However, the majority of land use classification studies focus on the classification of the territory under consideration. In many cases, these models are unable to be dynamically adapted independently of the input satellite data provided. Furthermore, it has been observed that solutions to the cloud problem, which is of significant importance when classifying territories such as the Baltic region, are not always distinguished in data preparation [10]. This paper aims to enhance the evaluation of land cover changes by proposing a hybrid machine learning (ML), cloud interpolation [11], and vegetation indices-based approach. This study specifically focuses on developing a robust classification pipeline that incorporates pre-processing techniques such as cloud interpolation, spectral index calculations, and an ML-based classification approach using the Random Forest algorithm [12]. The proposed method is tested on the territory of Lithuania, where diverse land use classes, seasonal variations, and frequent cloud cover pose significant challenges to classification accuracy. The main contribution and novelty of this research are as follows:  
1. An enhanced data pre-processing workflow is presented, demonstrating a substantial improvement in classification performance through effective management of cloud-covered satellite images. 2. An advanced feature selection strategy using vegetation indices to improve class separability was employed. 3.