Application of transfer learning of deep CNN model for classification of time-series satellite images to assess the long-term impacts of coal mining activities on land-use patterns

Abstract

The study aims to analyse the long-term impacts of mining activities in Jharia coalfield (JCF) on land-use (LU) patterns using transfer learning of the deep convolutional neural network (Deep CNN) model. A new database was prepared by extracting 10,000 image samples of 6 × 6 size for five LU types (barren land, built-up area, coal mining region, vegetation and waterbody) from Landsat data to train and validate the model. The satellite data from 1987 to 2021 at an interval of two years was used for change analysis. The study results revealed that the model offers 95 and 88% accuracy on the training and the validation dataset. The results indicate that barren land, coal mining region, and waterbody have been decreased from 237.30 sq. km. (=39.88%) to 171.25 sq. km (=28.78%), 118.77 sq. km. (=19.96%) to 68.73 sq. km (=11.55%), and 35.58 sq. km (=5.98%) to 18.68 sq. km (=3.14%) during 1987–2021, respectively. On the other hand, the built-up area and vegetation have been increased from 120.14 sq. km (=20.19%) to 233.02 sq. km (=39.16%) and 83.19 sq. km (=13.98%) to 103.36 sq. km (=17.37%) during 1987–2021. The time-series correlation results indicate that coal mining is the most sensitive LU type from 1987 to 2021, whereas barren land is least sensitive up to 2011, and thereafter vegetation is the least sensitive.