a promising automatic system for studying of coal mine surfaces using sentinel-2 data to assess a classification on a pixel-based pattern

Land use (lu) classification based on remote sensing images is a challenging task that can be effectively addressed using a learning framework. however, accurately classifying pixels according to their land use poses a significant difficulty. despite advancements in feature extraction techniques, the effectiveness of learning algorithms can vary considerably. in this study conducted in talcher, odisha, india, the researchers proposed the use of artificial neural networks (anns) to classify land use based on a dataset collected by the sentinel-2 satellite. the study focused on the talcher region, which was divided into five distinct land use classes: coal area, built-up area, barren area, vegetation area, and waterbody area. by applying anns to the mining region of talcher, the researchers aimed to improve the accuracy of land use classification. the results obtained from the study demonstrated an overall accuracy of 79.4%. this research work highlights the importance of utilizing remote sensing images and a learning framework to address the challenges associated with pixel-based land use classification. by employing anns and leveraging the dataset from the sentinel-2 satellite, the study offers valuable insights into effectively classifying different land use categories in the talcher region of india. the findings contribute to the advancement of techniques for accurate land use analysis, with potential applications in various fields such as urban planning, environmental monitoring, and resource management.