Machine Learning in Remote Sensing and GIS: Progress, Applications, and Future Prospects
Keywords:
Machine Learning, Supervised learning techniques, classificationAbstract
This research paper explores the intersection of machine learning, remote sensing, and Geographic Information Systems (GIS) with a focus on state-of-the-art systems, applications, and future developments. Machine learning techniques have made significant progress in recent years, enhancing data analysis, classification, and prediction functions to provide better and more efficient solutions in remote sensing and GIS.
References
Adab, H., Morbidelli, R., Saltalippi, C., Moradian, M., & Ghalhari, G. A. F. (2020). Machine learning to estimate surface soil moisture from remote sensing data. Water, 12(11), 3223. https://doi.org/10.3390/w12113223
Ahmad, S., Kalra, A., & Stephen, H. (2010). Estimating soil moisture using remote sensing data: A machine learning approach. Advances in Water Resources, 33(1), 69-80. https://doi.org/10.1016/j.advwatres.2009.10.002
Avand, M., & Moradi, H. (2021). Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability. Journal of Hydrology, 595, 125663. https://doi.org/10.1016/j.jhydrol.2021.125663
Camps-Valls, G. (2009, September). Machine learning in remote sensing data processing. In 2009 IEEE International Workshop on Machine Learning for Signal Processing (pp. 1-6). IEEE. https://doi.org/10.1109/MLSP.2009.5284194
Chang, N. B., & Bai, K. (2018). Multisensor data fusion and machine learning for environmental remote sensing. CRC Press.
Diaz-Gonzalez, F. A., Vuelvas, J., Correa, C. A., Vallejo, V. E., & Patino, D. (2022). Machine learning and remote sensing techniques applied to estimate soil indicators–review. Ecological Indicators, 135, 108517. https://doi.org/10.1016/j.ecolind.2022.108517
Holloway, J., & Mengersen, K. (2018). Statistical machine learning methods and remote sensing for sustainable development goals: A review. Remote Sensing, 10(9), 1365. https://doi.org/10.3390/rs10091365
Huang, X., & Jensen, J. R. (1997). A machine-learning approach to automated knowledge-base building for remote sensing image analysis with GIS data. Photogrammetric Engineering and Remote Sensing, 63(10), 1185-1193. https://www.asprs.org/wp
content/uploads/pers/1997journal/october/1997_oct_1185-1193.pdf
Kisekka, I., Peddinti, S. R., Kustas, W. P., McElrone, A. J., Bambach-Ortiz, N., McKee, L., & Bastiaanssen, W. (2022). Spatial–temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing. Irrigation Science, 40(4-5), 761-777. https://doi.org/10.1007/s00271-022-00873-2
Lary, D. J., Alavi, A. H., Gandomi, A. H., & Walker, A. L. (2016). Machine learning in geosciences and remote sensing. Geoscience Frontiers, 7(1), 3-10.
https://doi.org/10.1016/j.gsf.2015.06.003
Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784-2817. https://doi.org/10.1080/01431161.2018.1432164
Potić, I., Srdić, Z., Vakanjac, B., Bakrač, S., Đorđević, D., Banković, R., & Jovanović, J. M. (2023). Improving forest detection using machine learning and remote sensing: A case study in Southeastern Serbia. Applied Sciences, 13(14), 8289. https://doi.org/10.3390/app13148289
Quinn, J. A., Nyhan, M. M., Navarro, C., Coluccia, D., Bromley, L., & Luengo-Oroz, M. (2018). Humanitarian applications of machine learning with remote-sensing data: Review and case study in refugee settlement mapping. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2128), 20170363. https://doi.org/10.1098/rsta.2017.0363
Schmitt, M., Ahmadi, S. A., & Hänsch, R. (2021, July). There is no data like more data: Current status of machine learning datasets in remote sensing. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 1206-1209). IEEE.
https://doi.org/10.1109/IGARSS47720.2021.9553605
Schulz, K., Hänsch, R., & Sörgel, U. (2018). Machine learning methods for remote sensing applications: An overview. Earth Resources and Environmental Remote Sensing/GIS Applications IX, 10790, 1079002. https://doi.org/10.1117/12.2327647
Shirmard, H., Farahbakhsh, E., Müller, R. D., & Chandra, R. (2022). A review of machine learning in processing remote sensing data for mineral exploration. Remote Sensing of Environment, 268, 112750. https://doi.org/10.1016/j.rse.2021.112750
Tuia, D., Merenyi, E., Jia, X., & Grana-Romay, M. (2014). Foreword to the special issue on machine learning for remote sensing data processing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4), 1007-1011.
https://doi.org/10.1109/JSTARS.2014.2325309
Vargas-Munoz, J. E., Srivastava, S., Tuia, D., & Falcao, A. X. (2020). OpenStreetMap: Challenges and opportunities in machine learning and remote sensing. IEEE Geoscience and Remote Sensing Magazine, 9(1), 184-199. https://doi.org/10.1109/MGRS.2020.2960343