Abstract

Rapid urbanization in India, driven by population growth and economic expansion, has intensified challenges such as unplanned sprawl, resource strain, environmental degradation, and inadequate infrastructure. The Smart Cities Mission, launched in 2015, seeks to address these issues by fostering sustainable, technology-driven urban environments across 100 selected cities. Remote sensing (RS) technologies, including satellite imagery (e.g., Landsat, IRS series, high-resolution platforms), play a pivotal role in enabling evidence-based urban planning and smart city initiatives. RS facilitates accurate mapping of land use/land cover changes, monitoring urban expansion, detecting illegal encroachments, and assessing green cover dynamics over time. Integrated with Geographic Information Systems (GIS), it supports multi-layer spatial analysis for infrastructure planning, transportation optimization, flood risk assessment, urban heat island identification, and sustainable resource management. In the Indian context, RS contributes to baseline data creation for master plans, performance monitoring under the Smart Cities Mission (via dashboards and observatories), and predictive modeling of growth patterns to guide resilient development. By providing cost-effective, repetitive, and synoptic data, RS enhances decision making, promotes transparency, and aids in achieving sustainable urban goals. This article examines key applications, case examples from Indian cities, technological advancements, policy frameworks, and future prospects, underscoring RS as an indispensable tool for transforming Indian cities into inclusive, efficient, and environmentally balanced smart urban ecosystems.

Keywords

References

  1. [1] Jensen, J.R., 2016. Introductory Digital Image Processing: A Remote Sensing Perspective, 4th ed. Pearson Education: Glenview, IL, USA.
  2. [2] Weng, Q., 2012. Remote Sensing of Impervious Surfaces in the Urban Areas: Requirements, Methods, and Trends. Remote Sensing of Environment. 117, 34–49. DOI: https://doi.org/10.1016/j.rse.2011.02.030
  3. [3] Herold, M., Goldstein, N.C., Clarke, K.C., 2003. The Spatiotemporal Form of Urban Growth: Measurement, Analysis, and Modeling. Remote Sensing of Environment. 86(3), 286–302.
  4. [4] Taubenböck, H., Wiesner, M., Felbier, A., et al., 2014. New Dimensions of Urban Landscapes: The Spatio-Temporal Evolution of Urban Form. Applied Geography. 47, 137–153.
  5. [5] Wakode, H.B., Baier, K., Jha, R., et al., 2014. Analysis of urban growth using Landsat TM/ETM data and GIS—A case study of Hyderabad, India. Arabian Journal of Geosciences. 7(1), 109–121. DOI: https://doi.org/10.1007/s12517-013-0843-3
  6. [6] Rana, D., Kumari, M., Kumari, R., 2022. Land use and land coverage analysis with Google Earth Engine and change detection in the Sonipat District of the Haryana State in India. Engineering Proceedings. 27(1), 85. DOI: https://doi.org/10.3390/ecsa-9-13366
  7. [7] United Nations Department of Economic and Social Affairs, 2019. World Urbanization Prospects: The 2018 Revision. UN Department of Economic and Social Affairs: New York, NY, USA.
  8. [8] Madadi, M.R., Azamathulla, H.M., Yakhkeshi, M., 2015. Application of Google Earth to investigate the change of flood inundation area due to flood detention dam. Earth Science Informatics. 8(3), 627–638. DOI: https://doi.org/10.1007/s12145-014-0197-8
  9. [9] Patra, S., Sahoo, S., Mishra, P., et al., 2018. Impacts of urbanization on land use/cover changes and its probable implications on local climate and groundwater level. Journal of Urban Management. 7(2), 70–84. DOI: https://doi.org/10.1016/j.jum.2018.04.006
  10. [10] Cheruto, M.C., Kauti, M.K., Kisangau, P.D., et al., 2016. Assessment of land use and land cover change using GIS and remote sensing techniques: A case study of Makueni County, Kenya. Journal of Remote Sensing & GIS. 5(4), 1–6. Available from: https://umanyi.makueni.go.ke/storage/publications/Compressed_GoMC-KM-2025-5606.pdf
  11. [11] Yu, D., Fang, C., 2023. Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective. Remote Sensing. 15(5), 1307. DOI: https://doi.org/10.3390/rs15051307
  12. [12] Du, H., Cai, W., Xu, Y., et al., 2017. Quantifying the cool island effects of urban green spaces using remote sensing data. Urban Forestry &Urban Greening. 27, 24–31.
  13. [13] Choorapulakkal, A.A., Madandola, M.G., Al-Kandari, A., et al., 2024. The Resilience of the Built Environment to Flooding: The Case of Alappuzha District in the South Indian State of Kerala. Sustainability. 16(12), 5142. DOI: https://doi.org/10.3390/su16125142
  14. [14] Avtar, R., Aggarwal, R., Kharrazi, A., et al., 2020. Utilizing Geospatial Information to Monitor SDGs and Monitor Their Progress. Environmental Monitoring and Assessment. 192, 35.
  15. [15] Fu, B., Yu, D., Zhang, Y., 2019. The livable urban landscape: GIS and remote sensing extracted land use assessment for urban livability in Changchun Proper, China. Land Use Policy. 87, 104048.
  16. [16] Patel, A., Vyas, D., Chaudhari, N., et al., 2024. Novel approach for the LULC change detection using GIS & Google Earth Engine through spatiotemporal analysis to evaluate the urbanization growth of Ahmedabad city. Results in Engineering. 21, 101788. DOI: https://doi.org/10.1016/j.rineng.2024.101788
  17. [17] Government of India, 2011. Primary Census Abstract Indicators Search up to town/village level. Available from: https://censusindia.gov.in/census.website/data/data-visualizations/PopulationSearch_PCA_Indicators (cited 17 June 2025).
  18. [18] Ministry of Housing and Urban Affairs (MoHUA), 2023. Smart Cities Mission: Guidelines and Progress Reports. Government of India: New Delhi, India.
  19. [19] Bose, A., Chowdhury, I.R., 2020. Monitoring and modeling of spatio-temporal urban expansion and land-use/land-cover change using Markov chain model: A case study in Siliguri Metropolitan Area, West Bengal, India. Modeling Earth Systems and Environment. 6(4), 2235–2249. DOI: https://doi.org/10.1007/s40808-020-00842-6
  20. [20] Jain, A., Babu, K.A., 2024. An Examination of Cutting-Edge Design and Construction Methods Concerning Green Architecture and Renewable Energy Efficiency for Tier-II Cities of India. Archives for Technical Sciences. 2(31), 57–69. DOI: https://doi.org/10.70102/afts.2024.1631.057
  21. [21] Jain, A., Babu, K.A., 2024. Role of Green Buildings in the Sustainable Development of Tier-II Cities in India. Archives for Technical Sciences. 2(31), 368–378. DOI: https://doi.org/10.70102/afts.2024.1631.368
  22. [22] Tapiador, F.J., Casanova, J.L., 2003. Land use mapping methodology using remote sensing for the regional planning directives in Segovia, Spain. Landscape and Urban Planning. 62(2), 103–115.
  23. [23] Patel, A., 2019. Rainfall-runoff modelling and simulation using remote sensing and hydrological model for Banas River, Gujarat, India. In: AlKhaddar, R., Singh, R., Dutta, S., et al. (Eds.). Advances in Water Resources Engineering and Management: Select Proceedings of TRACE 2018. Springer: Singapore. pp. 153–162. DOI: https://doi.org/10.1007/978-981-13-8181-2_12
  24. [24] Jain, A., Babu, K.A., 2025. Reviewing the Green Building Concepts along with the Applications of AI and IoT for Environmental Monitoring and Designing Sustainable Infrastructures. Journal of Informatics and Mathematical Sciences. 17(1), 123–145. DOI: https://doi.org/10.26713/jims.v17i1.3056
  25. [25] Verma, S., Verma, M.K., Prasad, A.D., et al., 2023. Simulating the hydrological processes under multiple land use/land cover and climate change scenarios in the Mahanadi Reservoir Complex, Chhattisgarh, India. Water. 15(17), 3068.

How to Cite

Anshul Jain, Chinar Garg. Role of Remote Sensing in Urban Planning and Smart City Development in India. Urban Planning and Construction. 4 November 2025. https://doi.org/10.55121/upc.v3i2.539
Citation formats

Copyright & License

Copyright © 2025 Anshul Jain, Chinar Garg. Published by Japan Bilingual Publishing Co..

Article Metrics

1,039Abstract views
1,044Downloads