Application of Image Recognition Methods to Determine Land Use Classes

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.