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09-02-2023 | Research

Development of a deep convolutional neural network model for detection and delineation of coal mining regions

Authors: Ajay Kumar, Amit Kumar Gorai

Published in: Earth Science Informatics | Issue 2/2023

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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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Title: Development of a deep convolutional neural network model for detection and delineation of coal mining regions
Authors: Ajay Kumar
Amit Kumar Gorai
Publication date: 09-02-2023
Publisher: Springer Berlin Heidelberg
Published in: Earth Science Informatics / Issue 2/2023
Print ISSN: 1865-0473
Electronic ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-023-00955-3

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