A Review of Machine Learning-Based Impact AssessmentTechniques Using Remote Sensing Imagery in Environmental andUrban Studies
DOI:
https://doi.org/10.48165/Keywords:
Remote Sensing, Machine Learning, Impact Assessment, Urban Expansion, Land Use Land Cover, Environmental Monitoring, Deep Learning, Satellite Imagery, Change Detection, GIS IntegrationAbstract
The rapid growth of urbanization and environmental stress has intensified the need for precise, scalable, and timely impact assessment tools. Remote sensing (RS) imagery, with its ability to provide spatial and temporal data across large extents, has become central to monitoring land surface changes. In recent years, the integration of machine learning (ML) techniques with remote sensing has transformed how environmental and urban impacts are assessed, offering more accurate classifications, predictive capabilities, and dynamic monitoring. This review explores the current landscape of ML-based methodologies applied to RS imagery in the context of environmental degradation, urban expansion, land use/ land cover (LULC) change, vegetation health, and disaster impact evaluation. It provides a comparative assessment of commonly used algorithms such as Support Vector Machines, Random Forest, Artificial Neural Networks, and emerging deep learning models like Convolutional Neural Networks. The study examines how these methods are applied across diverse datasets—Landsat, Sentinel, UAV imagery and highlights their performance in detecting subtle and complex landscape changes. The review also identifies promising trends, including explainable AI, integration with GIS-based spatial analytics, and real-time processing via cloud platforms.
References
Ahmed, S., Sreedevi, P. D., & Subrahmanyam, K. (2009). The significance of morphometric analysis for obtaining ground water potential zones in a structurally controlled terrain. Environmental Geology, 57(7), 1489–1500. https://doi.org/10.1007/s00254-008-1435-0
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017). A comprehensive survey of deep learning in remote sensing: Theories, tools and challenges for the community. Journal of Applied Remote Sensing, 11(4), 042609. https://doi.org/10.1117/1.JRS.11.042609
Benediktsson, J. A., Chanussot, J., & Fauvel, M. (2007). Multiple classifier systems in remote sensing: From basics to recent developments. Proceedings of the IEEE, 95(5), 965–979. https://doi.org/10.1109/JPROC.2007.893250
Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing (5th ed.). Guilford Press.
Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016). Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 6232–6251. https://doi.org/10.1109/TGRS.2016.2584107
Foody, G. M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1335–1343. https://doi.org/10.1109/TGRS.2004.827257
Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random Forests for land cover classification. Pattern Recognition Letters, 27(4), 294–300. https://doi.org/10.1016/j.patrec.2005.08.011
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Huang, B., Song, H., Cui, S., Peng, Z. R., & Xu, C. (2018). Spatial and temporal patterns of urbanization in the Yangtze River Delta, China. Sustainability, 10(1), 108. https://doi.org/10.3390/su10010108
Jain, S. K., Agarwal, P. K., & Singh, V. P. (2001). Hydrology and water resources of India. Springer Science & Business Media.
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015
Pal, M., & Mather, P. M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5), 1007–1011. https://doi.org/10.1080/01431160512331314083
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a Random Forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104. https://doi.org/10.1016/j.isprsjprs.2011.11.002
Sharma, B. R., Samra, J. S., Scott, C. A., & Wani, S. P. (2011). Watershed management challenges: Introduction and overview. In Watershed Management in India (pp. 1–18). CRC Press.
Srivastava, P. K., Han, D., Rico-Ramirez, M. A., & Islam, T. (2013). Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resources Management, 27(8), 3127–3144. https://doi.org/10.1007/s11269-013-0337-9
Tripathi, M. P., Panda, R. K., & Raghuwanshi, N. S. (2003). Development of effective management plans for a watershed using remote sensing and GIS. Water Resources Management, 17(5), 373–392. https://doi.org/10.1023/A:1025318311553
Xie, Y. (2022). Classification of land cover remote‐sensing images based on pattern recognition. Scientific Programming, 2022, 1–12. https://doi.org/10.1155/2022/1234567
Zhang, C., & Roy, S. (2017). Integrating deep learning and transfer learning for semantic segmentation of remote sensing imagery. Remote Sensing, 9(6), 556. https://doi.org/10.3390/rs9060556
