Abstract
The present study aims to design a deep convolutional neural network (DCNN) model for the detection and delineation of coal mining regions. The study also examined the effect of the image size of the training dataset and the validation dataset using three different image size databases [DB6 ∈ (6 × 6 × 3), DB12 ∈ (12 × 12 × 3), and DB24 ∈ (24 × 24 × 3)]. The results indicated that the classification accuracies of DB6, DB12, and DB24 training datasets are 99.89%, 99.96%, and 99.91%, respectively, and that of validation datasets are 99.60%, 99.50%, and 99.87%, respectively. The results indicated that the classification accuracies with DB6, DB12, and DB24 training and validation datasets are nearly the same (> 99%) in each case but the boundary delineation with lower size image training dataset was more smooth. Therefore, the dataset of DB6 ∈ (6 × 6) was further used for change detection analysis through transfer learning. Landsat series data for 1989, 2000, 2011, and 2022 were selected for classification using the proposed model. The results indicate that the coverage area of the coal mining region increased by 2% (= 0.04 Sq.km) in 1989-00, and 22.37% (= 0.58 Sq.km) in 2011-22, and thereafter decreased by 3.36% (= 0.07 Sq.km) in 2000-11. Therefore, proper land management practices and active organization of coal production should be advanced to protect against undesirable land-use change. The results of the study on change detection could provide valuable information for decision-makers to land-use planners in mining regions.
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Acknowledgements
Authors sincerely thanks to Director, NIT Rourkela for facilitating the computing facility to execute the work. The USGS Earth Explorer is also acknowledged for providing the Landsat series satellite data used in current research.
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Ajay Kumar: Conceptualization, model development, software, producing results, original draft preparation. Amit Kumar Gorai: Conceptualization, methodology, formal analysis, review & editing, visualization, supervision.
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Kumar, A., Gorai, A.K. Development of a deep convolutional neural network model for detection and delineation of coal mining regions. Earth Sci Inform 16, 1151–1171 (2023). https://doi.org/10.1007/s12145-023-00955-3
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DOI: https://doi.org/10.1007/s12145-023-00955-3