Abstract:
The classification of land cover and crop types through the analysis of satellite imagery is a fundamental task with wide-ranging implications in the fields of agriculture, environmental monitoring, and urban planning. This research embarks on a comprehensive journey to explore advanced methodologies for precise land cover classification and crop type identification, utilizing high-resolution satellite images as its primary data source. Leveraging the EuroSat dataset as a valuable resource, we harness the potential of cutting-edge deep learning models, with a focal point on DenseNet, renowned for its capacity to extract intricate features from remote sensing data. A meticulous data preprocessing pipeline is applied, encompassing image resizing, normalization, and class aggregation, to optimize the input data for superior model performance. The outcomes of this study underscore the remarkable ability of modern neural networks to capture the nuanced characteristics of land cover and crop types within satellite imagery, emphasizing their effectiveness in addressing real-world challenges across diverse applications. Moreover, this research outlines promising avenues for future exploration, including the development of fine-grained crop classification techniques, temporal analysis methods, and the integration of multi-sensor data fusion, all aimed at further enhancing the precision, adaptability, and practicality of land cover and crop type classification models.
