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Dual attention guided deep encoder-decoder network for change analysis in land use/land cover for Dakshina Kannada District, Karnataka, India

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Abstract

The Earth is frequently changed by natural occurrences and human actions that have threatened our environment to a certain extent. Therefore, accurate and timely monitoring of transformations at the surface of the Earth is crucial for precisely facing their harmful effects and consequences. This paper aims to perform a change detection (CD) analysis and assessment of the Dakshina Kannada region, being one of the coastal districts of Karnataka, India. The spatial and temporal variations in land use and land cover (LULC) are being monitored and examined from the data received as LULC maps from the National Remote Sensing Agency, Indian Space Research Organization, India. The time-series data from advanced wide-field sensor (AWiFS) Resourcesat2 satellite as LULC maps (1:250k) are analyzed using a deep learning approach with an encoder–decoder architecture with dual-attention modules for the change analysis. The model provides an overall accuracy and meanIOU(intersection over union) of 94.11% and 74.1%. The LULC maps from 2005 to 2018 (13 years) are utilized to decide the variations in the LULC, including urban development, agricultural variations, vegetation dynamics, forest areas, barren land, littoral swamp, and water bodies, current fallow, etc. The multiclass area-wise changes in terms of percentage show a decline in most LULC classes, which raises a point of concern for the environmental safety of the considered area, which is highly exposed to coastal flooding due to increased urbanization.

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source:https://stategisportal.nic.in/stategisportal/Karnataka_BharatMaps/map.aspx

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Data availibility Statement

The data that support the findings of this study are available from [National Remote Sensing Centre, Indian Space Research Organization (ISRO), Government of India, Hyderabad India] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [National Remote Sensing Centre, Indian Space Research Organization (ISRO), Government of  India, Hyderabad India].

Code availibility

Not applicable.

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Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Karnataka, Mangalore, Karnataka, 575025, India

    Nitesh Naik & Kandasamy Chandrasekaran

  2. Department of Computer Science and Engineering, National Institute of Technology, Puducherry, Karaikal, Puducherry, 609609, India

    Venkatesan Meenakshi Sundaram

  3. School of Information Technology and Engineering, Vellore Insitute of Technology, Katpadi, Vellore, TamilNadu, 632014, India

    Prabhavathy Panneer

Authors
  1. Nitesh Naik
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  2. Kandasamy Chandrasekaran
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  3. Venkatesan Meenakshi Sundaram
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  4. Prabhavathy Panneer
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by NN, KC, VMS and PP. The first draft of the manuscript was written by NN and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Nitesh Naik.

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This article is part of a Topical Collection in Environmental Earth Sciences on Deep learning for earth resource and environmental remote sensing, guest edited by Carlos Enrique Montenegro Marin, Xuyun Zhang and Nallappan Gunasekaran.

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Naik, N., Chandrasekaran, K., Sundaram, V.M. et al. Dual attention guided deep encoder-decoder network for change analysis in land use/land cover for Dakshina Kannada District, Karnataka, India. Environ Earth Sci 82, 33 (2023). https://doi.org/10.1007/s12665-022-10713-1

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