1. Rawat, S., A. Rawat, D. Kumar and A.S. Sabitha, 2021. Application of machine learning and data visualization techniques for decision support in the insurance sector. Int. J. Inform. Manage. Data Insights, 10.1016/j.jjimei.2021.100012.
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2. Kumar, D., 2021. Urban objects detection from c-band synthetic aperture radar (SAR) satellite images through simulating filter properties. Sci. Rep., Vol. 11. 10.1038/s41598-021-85121-9.
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3. Kumar, D., 2021. Spatial variability analysis of the solar energy resources for future urban energy applications using meteosat satellite-derived datasets. Remote Sens. Appl.: Soc. Environ., Vol. 22. 10.1016/j.rsase.2021.100481.
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4. Kumar, D. and T. Tewary, 2021. Techno?economic assessment and optimization of a standalone residential hybrid energy system for sustainable energy utilization. Int. J. Energy Res., 10.1002/er.6389.
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5. Misra, M., D. Kumar and S. Shekhar, 2020. Assessing machine learning based supervised classifiers for built-up impervious surface area extraction from sentinel-2 images. Urban Forestry & Urban Greening, Vol. 53. 10.1016/J.UFUG.2020.126714.
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6. Misra, M. and D. Kumar, 2020. A hybrid indexing approach for sustainable smart cities development. J. Indian Soc. Remote Sens., 10.1007/S12524-020-01171-Y.
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7. Kumar, D., 2020. Urban energy system management for enhanced energy potential for upcoming smart cities. Energy Explor. Exploit., 38: 1968-1982.
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8. Kumar, D., 2020. Satellite-based solar energy potential analysis for southern states of India. Energy Rep., 6: 1487-1500.
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9. Kumar, D. and A. Kumar, 2020. Evaluating filter properties for urban feature identification from dual-pol SAR images. Model. Earth Syst. Environ., 10.1007/s40808-020-00981-w.
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10. Kaushik, K. and D. Kumar, 2020. Forest disturbance mapping with microwave remote sensing. J. Landscape Ecol., 13: 95-128.
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11. Kumar, D., 2019. Statistical image processing for enhanced scientific analysis. Smart Syst. IoT: Innovations Comput., 10.1007/978-981-13-8406-6_1.
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12. Kumar, D., 2019. Statistical Image Processing for Enhanced Scientific Analysis. In: Smart Systems and IoT: Innovations in Computing. Smart Innovation, Systems and Technologies, Vol. 141, Somani, A., R. Shekhawat, A. Mundra, S. Srivastava and V. Verma (Eds.)., Springer, Singapore, pp: 1-11.
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13. Kumar, D., 2019. Hyper-temporal variability analysis of solar insolation with respect to local seasons. Remote Sens. Applic.: Soc. Environ., Vol. 15. 10.1016/j.rsase.2019.100241.
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14. Kumar, D., T. Tewary and S. Shekhar, 2018. Enhanced adaptive technique for surface temperature variability analysis. Iran. J. Sci. Technol. Trans. Sci., 42: 1309-1316.
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15. Kumar, D., 2018. Mapping solar energy potential of southern India through geospatial technology. Geocarto Int., 34: 1477-1495.
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16. Kumar, D., 2017. Surface temperature variability analysis of an urban area using Landsat ETM+ thermal images. Asian Geogr., 34: 25-37.
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17. Kumar, D., 2016. Adaptive hierarchical cell sub-division (AHCS) method for enhanced surface radiance temperature variability analysis. Model. Earth Syst. Environ., Vol. 2. 10.1007/s40808-016-0194-7.
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18. Kumar, D. and S. Shekhar, 2016. Linear gradient analysis of kinetic temperature through geostatistical approach. Model. Earth Syst. Environ., Vol. 2. 10.1007/s40808-016-0198-3.
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19. Kumar, D. and S. Shekhar, 2015. Statistical analysis of land surface temperature–vegetation indexes relationship through thermal remote sensing. Ecotoxicol. Environ. Saf., 121: 39-44.
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