Acknowledgment to the Reviewers of Journal of Environmental & Earth Sciences in 2025
2026-02-06
Unsupervised Intelligent Framework for Earth Science Remote Sensing Applications Based on Clustering Algorithms
Authors
-
Hongyan Zhang
Department of Information Technology and Engineering, Jinzhong University, Jinzhong 030619, China
-
Li Zhao
Department of Information Technology and Engineering, Jinzhong University, Jinzhong 030619, China
DOI:
https://doi.org/10.30564/jees.v8i8.13389Abstract
Archives of Earth observations have grown and continue to grow in volume, modality, and temporal density, providing unprecedented access for monitoring the Earth system and exacerbating the paucity and high cost of quality labels. This is a review of unsupervised intelligent methods for Earth science remote sensing based on clustering algorithms and capable of analyzing data in a scalable, interpretable manner under domain shift. We begin by summarizing the remote sensing data properties that make it difficult to learn unsupervised, and they are: high-dimensionality, mixed-pixels, sensor-specific noise, spatial autocorrelation, non-uniform time series, heterogeneous across sensors. We next list key clustering families that are applied in remote sensing, including centroid-based and medoid-based, probabilistic mixtures, density-based, hierarchical, graph clustering, spectral clustering, subspace/low-rank models of hyperspectral data, fuzzy clustering to ambiguity, and deep clustering using self-supervised representation learning. On these algorithmic mammoths, we introduce end-to-end patterns of framework design, i.e., preprocessing, harmonization, sampling, and tiling on a scale of archive computations, spatial-context synthesis with super pixels and regularization, multi-modal fusion with shared latent spaces and spatiotemporal clustering with consistency and drift management. Lastly, we also talk about application areas like stratifying land-covers, grouping of hyperspectral materials, discovering SAR regimes, hazard surveillance, and analyzing climate-hydrology regimes with the focus on not analyzing by labels but by stability tests, uncertainty reporting, and process-based validation. The review summarizes practical advice and presents open dilemmas to effective, operational unsupervised intelligence for Earth observation.
Keywords:
Unsupervised Learning; Clustering Algorithms; Remote Sensing; Earth Observation; Self-Supervised Representation LearningReferences
[1] Emery, W., Camps, A., 2017. Introduction to Satellite Remote Sensing: Atmosphere, Ocean, Land and Cryosphere Applications. Elsevier: Amsterdam, The Netherlands.
[2] Tedesco, M. (ed.), 2015. Remote sensing and the cryosphere. In Remote Sensing of the Cryosphere, 1st ed. Wiley: Hoboken, NJ, USA. pp. 1–16. DOI: https://doi.org/10.1002/9781118368909
[3] Rast, M., Painter, T.H., 2019. Earth Observation Imaging Spectroscopy for Terrestrial Systems: An Overview of Its History, Techniques, and Applications of Its Missions. Surveys in Geophysics. 40(3), 303–331. DOI: https://doi.org/10.1007/s10712-019-09517-z
[4] Meng, L., Yan, C., Lv, S., et al., 2024. Synthetic Aperture Radar for Geosciences. Reviews of Geophysics. 62(3), e2023RG000821. DOI: https://doi.org/10.1029/2023RG000821
[5] Bhattacharya, B.K., Mallick, K., Desai, D., et al., 2022. A coupled ground heat flux–surface energy balance model of evaporation using thermal remote sensing observations. Biogeosciences. 19(23), 5521–5551. DOI: https://doi.org/10.5194/bg-19-5521-2022
[6] Guo, Q., Su, Y., Hu, T., et al., 2021. Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective. IEEE Geoscience and Remote Sensing Magazine. 9(1), 232–257. DOI: https://doi.org/10.1109/MGRS.2020.3032713
[7] Zhang, L., Zhang, L., 2022. Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities. IEEE Geoscience and Remote Sensing Magazine. 10(2), 270–294. DOI: https://doi.org/10.1109/MGRS.2022.3145854
[8] Gino, V.L.S., Negri, R.G., Souza, F.N., et al., 2023. Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series. Sustainability. 15(6), 4725. DOI: https://doi.org/10.3390/su15064725
[9] Ghosh, R., Jia, X., Yin, L., et al., 2022. Clustering augmented self-supervised learning: an application to land cover mapping. In Proceedings of the 30th International Conference on Advances in Geographic Information Systems, Seattle, WA, USA, 1–4 November 2022; pp. 1–10. DOI: https://doi.org/10.1145/3557915.3560937
[10] Mirpulatov, I., Illarionova, S., Shadrin, D., et al., 2023. Pseudo-Labeling Approach for Land Cover Classification Through Remote Sensing Observations With Noisy Labels. IEEE Access. 11, 82570–82583. DOI: https://doi.org/10.1109/ACCESS.2023.3300967
[11] Roberts, D., Mueller, N., Mcintyre, A., 2017. High-Dimensional Pixel Composites From Earth Observation Time Series. IEEE Transactions on Geoscience and Remote Sensing. 55(11), 6254–6264. DOI: https://doi.org/10.1109/TGRS.2017.2723896
[12] Bergen, K.J., Johnson, P.A., De Hoop, M.V., et al., 2019. Machine learning for data-driven discovery in solid Earth geoscience. Science. 363(6433), eaau0323. DOI: https://doi.org/10.1126/science.aau0323
[13] Chaudhry, M., Shafi, I., Mahnoor, M., et al., 2023. A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective. Symmetry. 15(9), 1679. DOI: https://doi.org/10.3390/sym15091679
[14] Peng, J., Sun, W., Li, H.-C., et al., 2022. Low-Rank and Sparse Representation for Hyperspectral Image Processing: A review. IEEE Geoscience and Remote Sensing Magazine. 10(1), 10–43. DOI: https://doi.org/10.1109/MGRS.2021.3075491
[15] Saifuzzaman, M., Adamchuk, V., Buelvas, R., et al., 2019. Clustering Tools for Integration of Satellite Remote Sensing Imagery and Proximal Soil Sensing Data. Remote Sensing. 11(9), 1036. DOI: https://doi.org/10.3390/rs11091036
[16] Mudgal, P., 2026. Clustering of Temporal and Visual Data: Recent Advancements. Data. 11(1), 7. DOI: https://doi.org/10.3390/data11010007
[17] Gao, H., Tang, Y., Jing, L., et al., 2017. A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images. Sensors. 17(10), 2427. DOI: https://doi.org/10.3390/s17102427
[18] Hasmadi, M., Pakhriazad, H., Shahrin, M., 2009. Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data. Geografia: Malaysian Journal of Society and Space. 5(1), 1–10.
[19] Saleh, P., 2025. Advanced Unsupervised Analysis of Spatio-Temporal Pattern Analytics [Master’s Thesis]. University of Ottawa: Ottawa, ON, Canada.
[20] Alasali, T., Ortakci, Y., 2024. Clustering Techniques in Data Mining: A Survey of Methods, Challenges, and Applications. Computer Science. 9(1), 32–50. DOI: https://doi.org/10.53070/bbd.1421527
[21] Alshammari, A.H., Mahdi, M.F., Hirotsu, T., et al., 2025. Beyond Binary: A Machine Learning Framework for Interpreting Organismal Behavior in Cancer Diagnostics. Biomedicines. 13(10), 2409. DOI: https://doi.org/10.3390/biomedicines13102409
[22] Zhou, S., Xu, H., Zheng, Z., et al., 2025. A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions. ACM Computing Surveys. 57(3), 1–38. DOI: https://doi.org/10.1145/3689036
[23] Tao, B., 2025. Near-Real-Time Insight Delivery Pipeline for Earth Observation Satellite Images [Master’s Thesis]. University of Illinois Urbana-Champaign: Urbana/Champaign, IL, USA.
[24] Hauser, S., Augner, L., Schmitt, A., 2025. Perfect Labelling: A Review and Outlook of Label Optimization Techniques in Dynamic Earth Observation. Remote Sensing. 17(7), 1246. DOI: https://doi.org/10.3390/rs17071246
[25] Navalgund, R.R., Jayaraman, V., Roy, P.S., 2007. Remote sensing applications: An overview. Current Science. 93(2), 1747–1766.
[26] Takhtkeshha, N., Mandlburger, G., Remondino, F., et al., 2024. Multispectral Light Detection and Ranging Technology and Applications: A Review. Sensors. 24(5), 1669. DOI: https://doi.org/10.3390/s24051669
[27] Hong, D., Li, C., Yokoya, N., et al., 2026. Hyperspectral imaging. Nature Reviews Methods Primers. 6(1), 19. DOI: https://doi.org/10.1038/s43586-026-00470-x
[28] Singh, R.P., Paramanik, S., Bhattacharya, B.K., et al., 2020. Modelling of evapotranspiration using land surface energy balance and thermal infrared remote sensing. Tropical Ecology. 61(1), 42–50. DOI: https://doi.org/10.1007/s42965-020-00076-8
[29] Ussyshkin, V., Theriault, L., 2011. Airborne Lidar: Advances in Discrete Return Technology for 3D Vegetation Mapping. Remote Sensing. 3(3), 416–434. DOI: https://doi.org/10.3390/rs3030416
[30] Clifford, D., 2010. Global estimates of snow water equivalent from passive microwave instruments: history, challenges and future developments. International Journal of Remote Sensing. 31(14), 3707–3726. DOI: https://doi.org/10.1080/01431161.2010.483482
[31] Petcu, D., Panica, S., Neagul, M., et al., 2010. Earth observation data processing in distributed systems. Informatica. 34(4), 463–476.
[32] Pompilio, L., Pepe, M., Pedrazzi, G., et al., 2014. Informational Clustering of Hyperspectral Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(6), 2209–2223. DOI: https://doi.org/10.1109/JSTARS.2013.2294053
[33] Aboneh, T., Rorissa, A., Srinivasagan, R., 2022. Stacking-Based Ensemble Learning Method for Multi-Spectral Image Classification. Technologies. 10(1), 17. DOI: https://doi.org/10.3390/technologies10010017
[34] Hanafy, M.E., Roggemann, M.C., Guney, D.O., 2015. Reconstruction of images degraded by aerosol scattering and measurement noise. Optical Engineering. 54(3), 033101. DOI: https://doi.org/10.1117/1.OE.54.3.033101
[35] Argenti, F., Lapini, A., Bianchi, T., et al., 2013. A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images. IEEE Geoscience and Remote Sensing Magazine. 1(3), 6–35. DOI: https://doi.org/10.1109/MGRS.2013.2277512
[36] Macarringue, L.S., Bolfe, É.L., Pereira, P.R.M., 2022. Developments in Land Use and Land Cover Classification Techniques in Remote Sensing: A Review. Journal of Geographic Information System. 14(1), 1–28. DOI: https://doi.org/10.4236/jgis.2022.141001
[37] Feng, Z., Chen, Y., Hyyppa, J., et al., 2018. Estimating Ground Level and Canopy Top Elevation with Airborne Microwave Profiling Radar. IEEE Transactions on Geoscience and Remote Sensing. 56(4), 2283–2294. DOI: https://doi.org/10.1109/TGRS.2017.2778024
[38] Marmanis, D., Schindler, K., Wegner, J.D., et al., 2018. Classification with an edge: Improving semantic image segmentation with boundary detection. ISPRS Journal of Photogrammetry and Remote Sensing. 135, 158–172. DOI: https://doi.org/10.1016/j.isprsjprs.2017.11.009
[39] Liu, J., Du, M., Mao, Z., 2017. Scale computation on high spatial resolution remotely sensed imagery multi-scale segmentation. International Journal of Remote Sensing. 38(18), 5186–5214. DOI: https://doi.org/10.1080/01431161.2017.1325536
[40] Tan, P., Steinbach, M., Kumar, V., et al., 2001. Finding spatio-temporal patterns in earth science data. In Proceedings of the 2001 ACM SIGKDD Workshop on Temporal Data Mining, San Jose, CA, USA, 26–29 August 2001.
[41] Rhif, M., Abbes, A.B., Martinez, B., et al., 2022. Detection of trend and seasonal changes in non-stationary remote sensing data: Case study of Tunisia vegetation dynamics. Ecological Informatics. 69, 101596. DOI: https://doi.org/10.1016/j.ecoinf.2022.101596
[42] Verrelst, J., Malenovský, Z., Van Der Tol, C., et al., 2019. Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods. Surveys in Geophysics. 40(3), 589–629. DOI: https://doi.org/10.1007/s10712-018-9478-y
[43] Tanase, M.A., Villard, L., Pitar, D., et al., 2019. Synthetic aperture radar sensitivity to forest changes: A simulations-based study for the Romanian forests. Science of The Total Environment. 689, 1104–1114. DOI: https://doi.org/10.1016/j.scitotenv.2019.06.494
[44] Zimble, D.A., Evans, D.L., Carlson, G.C., et al., 2003. Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote Sensing of Environment. 87(2–3), 171–182. DOI: https://doi.org/10.1016/S0034-4257(03)00139-1
[45] Min, Q., Lin, B., Li, R., 2010. Remote Sensing Vegetation Hydrological States Using Passive Microwave Measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 3(1), 124–131. DOI: https://doi.org/10.1109/JSTARS.2009.2032557
[46] Khalafaoui, Y., 2024. Advancements in Multimodal and Unsupervised Learning with Application to Recommender Systems [PhD Thesis]. CY Cergy Paris Université: Cergy-Pontoise, France. DOI: https://doi.org/10.70675/dd7b8948z0036z499dz99dez131402d47938
[47] Blaschke, T., Burnett, C., Pekkarinen, A., 2004. Image Segmentation Methods for Object-based Analysis and Classification. In: Jong, S.M.D., Meer, F.D.V.D. (eds.). Remote Sensing Image Analysis: Including The Spatial Domain. Springer: Dordrecht, The Netherlands. pp. 211–236. DOI: https://doi.org/10.1007/978-1-4020-2560-0_12
[48] Ball, J.E., Anderson, D.T., Chan, C.S., 2017. Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community. Journal of Applied Remote Sensing. 11(4), 1. DOI: https://doi.org/10.1117/1.JRS.11.042609
[49] Lee, C.A., Gasster, S.D., Plaza, A., et al., 2011. Recent Developments in High Performance Computing for Remote Sensing: A Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 4(3), 508–527. DOI: https://doi.org/10.1109/JSTARS.2011.2162643
[50] Zhang, H., Xu, J.-J., Cui, H.-W., et al., 2025. When Geoscience Meets Foundation Models: Toward a general geoscience artificial intelligence system. IEEE Geoscience and Remote Sensing Magazine. 13(4), 79–118. DOI: https://doi.org/10.1109/MGRS.2024.3496478
[51] Kazanskiy, N., Khabibullin, R., Nikonorov, A., et al., 2025. A Comprehensive Review of Remote Sensing and Artificial Intelligence Integration: Advances, Applications, and Challenges. Sensors. 25(19), 5965. DOI: https://doi.org/10.3390/s25195965
[52] Zhong, Y., Zhang, S., Zhang, L., 2013. Automatic Fuzzy Clustering Based on Adaptive Multi-Objective Differential Evolution for Remote Sensing Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6(5), 2290–2301. DOI: https://doi.org/10.1109/JSTARS.2013.2240655
[53] Zhai, H., Zhang, H., Li, P., et al., 2021. Hyperspectral Image Clustering: Current achievements and future lines. IEEE Geoscience and Remote Sensing Magazine. 9(4), 35–67. DOI: https://doi.org/10.1109/MGRS.2020.3032575
[54] Nigro, L., Fränti, P., 2023. Two Medoid-Based Algorithms for Clustering Sets. Algorithms. 16(7), 349. DOI: https://doi.org/10.3390/a16070349
[55] García-Escudero, L.A., Gordaliza, A., Matrán, C., et al., 2010. A review of robust clustering methods. Advances in Data Analysis and Classification. 4(2–3), 89–109. DOI: https://doi.org/10.1007/s11634-010-0064-5
[56] Aggarwal, C.C., 2018. A Survey of Uncertain Data Clustering Algorithms. In: Aggarwal, C.C., Reddy, C.K. (eds.). Data Clustering. Chapman and Hall/CRC: Boca Raton, FL, USA. pp. 457–482. DOI: https://doi.org/10.1201/9781315373515-18
[57] Zhao, B., Zhong, Y., Ma, A., et al., 2016. A Spatial Gaussian Mixture Model for Optical Remote Sensing Image Clustering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(12), 5748–5759. DOI: https://doi.org/10.1109/JSTARS.2016.2546918
[58] Mayr, S., Kuenzer, C., Gessner, U., et al., 2019. Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products. Remote Sensing. 11(22), 2616. DOI: https://doi.org/10.3390/rs11222616
[59] Hashemi, S.N., Mehdizadeh, R., 2015. Application of hierarchical clustering technique for numerical tectonic regionalization of the Zagros region (Iran). Earth Science Informatics. 8(2), 367–380. DOI: https://doi.org/10.1007/s12145-014-0163-5
[60] Sublime, J., Troya-Galvis, A., Puissant, A., 2017. Multi-Scale Analysis of Very High Resolution Satellite Images Using Unsupervised Techniques. Remote Sensing. 9(5), 495. DOI: https://doi.org/10.3390/rs9050495
[61] Tung, F., Wong, A., Clausi, D.A., 2010. Enabling scalable spectral clustering for image segmentation. Pattern Recognition. 43(12), 4069–4076. DOI: https://doi.org/10.1016/j.patcog.2010.06.015
[62] Zhang, L., Geng, X., Qin, Z., et al., 2022. Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting. Sustainability. 14(19), 12397. DOI: https://doi.org/10.3390/su141912397
[63] Li, H., Cui, J., Zhang, X., et al., 2022. Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction. Remote Sensing. 14(18), 4579. DOI: https://doi.org/10.3390/rs14184579
[64] Shahnas, P., Malathy, S., 2025. Evaluating subspace clustering techniques for effective and efficient dimensionality reduction in hyperspectral imaging. Chemometrics and Intelligent Laboratory Systems. 264, 105463. DOI: https://doi.org/10.1016/j.chemolab.2025.105463
[65] Ghosh, A., Mishra, N.S., Ghosh, S., 2011. Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences. 181(4), 699–715. DOI: https://doi.org/10.1016/j.ins.2010.10.016
[66] Zhang, R., Li, X., 2021. Regularized Regression with Fuzzy Membership Embedding for Unsupervised Feature Selection. IEEE Transactions on Fuzzy Systems. 29(12), 3743–3753. DOI: https://doi.org/10.1109/TFUZZ.2020.3026834
[67] Shahi, K.R., Ghamisi, P., Rasti, B., et al., 2022. Unsupervised Data Fusion With Deeper Perspective: A Novel Multisensor Deep Clustering Algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15, 284–296. DOI: https://doi.org/10.1109/JSTARS.2021.3132856
[68] Wang, Y., Albrecht, C.M., Braham, N.A.A., et al., 2022. Self-Supervised Learning in Remote Sensing: A review. IEEE Geoscience and Remote Sensing Magazine. 10(4), 213–247. DOI: https://doi.org/10.1109/MGRS.2022.3198244
[69] Taşdemir, K., Moazzen, Y., Yildirim, I., 2015. An Approximate Spectral Clustering Ensemble for High Spatial Resolution Remote-Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(5), 1996–2004. DOI: https://doi.org/10.1109/JSTARS.2015.2424292
[70] Wickramasinghe, C.S., Amarasinghe, K., Marino, D.L., et al., 2021. Explainable Unsupervised Machine Learning for Cyber-Physical Systems. IEEE Access. 9, 131824–131843. DOI: https://doi.org/10.1109/ACCESS.2021.3112397
[71] Sadeghi, B., 2025. Clustering in geo-data science: Navigating uncertainty to select the most reliable method. Ore Geology Reviews. 181, 106591. DOI: https://doi.org/10.1016/j.oregeorev.2025.106591
[72] Gargees, R.S., Scott, G.J., 2020. Deep Feature Clustering for Remote Sensing Imagery Land Cover Analysis. IEEE Geoscience and Remote Sensing Letters. 17(8), 1386–1390. DOI: https://doi.org/10.1109/LGRS.2019.2948799
[73] Zhang, X., Leng, C., Hong, Y., et al., 2021. Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey. Remote Sensing. 13(24), 5128. DOI: https://doi.org/10.3390/rs13245128
[74] Teh, H.Y., Kempa-Liehr, A.W., Wang, K.I.-K., 2020. Sensor data quality: a systematic review. Journal of Big Data. 7(1), 11. DOI: https://doi.org/10.1186/s40537-020-0285-1
[75] Tuia, D., Schindler, K., Demir, B., et al., 2025. Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward. IEEE Geoscience and Remote Sensing Magazine. 13(4), 119–141. DOI: https://doi.org/10.1109/MGRS.2024.3425961
[76] Shi, Z., Pun-Cheng, L.S.C., 2019. Spatiotemporal Data Clustering: A Survey of Methods. ISPRS International Journal of Geo-Information. 8(3), 112. DOI: https://doi.org/10.3390/ijgi8030112
[77] Ahmed, I., Raihan, A.S., 2024. Spatiotemporal Data Analysis: A Review of Techniques, Applications, and Emerging Challenges. In: Gaw, N., Pardalos, P.M., Gahrooei, M.R. (eds.). Multimodal and Tensor Data Analytics for Industrial Systems Improvement. Springer International Publishing: Cham, Switzerland. pp. 125–166. DOI: https://doi.org/10.1007/978-3-031-53092-0_7
[78] Goldberger, J., Gordon, S., Greenspan, H., 2006. Unsupervised image-set clustering using an information theoretic framework. IEEE Transactions on Image Processing. 15(2), 449–458. DOI: https://doi.org/10.1109/TIP.2005.860593
[79] Prabhakaran, R., Bertotti, G., Urai, J., et al., 2021. Investigating spatial heterogeneity within fracture networks using hierarchical clustering and graph distance metrics. Solid Earth. 12(10), 2159–2209. DOI: https://doi.org/10.5194/se-12-2159-2021
[80] Dalla Mura, M., Prasad, S., Pacifici, F., et al., 2015. Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing. Proceedings of the IEEE. 103(9), 1585–1601. DOI: https://doi.org/10.1109/JPROC.2015.2462751
[81] Chartsias, A., Joyce, T., Giuffrida, M.V., et al., 2018. Multimodal MR Synthesis via Modality-Invariant Latent Representation. IEEE Transactions on Medical Imaging. 37(3), 803–814. DOI: https://doi.org/10.1109/TMI.2017.2764326
[82] Yu, M., Huang, Q., Li, Z., 2024. Deep learning for spatiotemporal forecasting in Earth system science: a review. International Journal of Digital Earth. 17(1), 2391952. DOI: https://doi.org/10.1080/17538947.2024.2391952
[83] Shi, M., Janowicz, K., Liu, Z., et al., 2025. Defining Concept Drift and Its Variants in Research Data Management: A Scientometric Case Study on Geographic Information Science. Transactions in GIS. 29(3), e70058. DOI: https://doi.org/10.1111/tgis.70058
[84] Baumann, P., 2021. A general conceptual framework for multi-dimensional spatio-temporal data sets. Environmental Modelling & Software. 143, 105096. DOI: https://doi.org/10.1016/j.envsoft.2021.105096
[85] Moujahid, A., Dornaika, F., 2025. Advanced unsupervised learning: a comprehensive overview of multi-view clustering techniques. Artificial Intelligence Review. 58(8), 234. DOI: https://doi.org/10.1007/s10462-025-11240-8
[86] Chai, C., Li, G., 2020. Human-in-the-loop Techniques in Machine Learning. IEEE Data Engineering Bulletin. 43(3), 37–52.
[87] Tatman, R., VanderPlas, J., Dane, S., 2018. A practical taxonomy of reproducibility for machine learning research. In Proceedings of the 35th International Conference on Machine Learning, Stockholm, Swede, 10–15 July 2018.
[88] Chander, B., Gopalakrishnan, K., 2023. Data clustering using unsupervised machine learning. In Statistical Modeling in Machine Learning. Elsevier: Amsterdam, The Netherlands. pp. 179–204. DOI: https://doi.org/10.1016/B978-0-323-91776-6.00015-4
[89] Nagar, S., Farahbakhsh, E., Awange, J., et al., 2024. Remote sensing framework for geological mapping via stacked autoencoders and clustering. Advances in Space Research. 74(10), 4502–4516. DOI: https://doi.org/10.1016/j.asr.2024.09.013
[90] Frias-Martinez, V., Frias-Martinez, E., 2014. Spectral clustering for sensing urban land use using Twitter activity. Engineering Applications of Artificial Intelligence. 35, 237–245. DOI: https://doi.org/10.1016/j.engappai.2014.06.019
[91] Potgieter, A.B., Zhao, Y., Zarco-Tejada, P.J., et al., 2021. Evolution and application of digital technologies to predict crop type and crop phenology in agriculture. In Silico Plants. 3(1), diab017. DOI: https://doi.org/10.1093/insilicoplants/diab017
[92] Liang, J., Zhou, J., Qian, Y., et al., 2017. On the Sampling Strategy for Evaluation of Spectral-Spatial Methods in Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 55(2), 862–880. DOI: https://doi.org/10.1109/TGRS.2016.2616489
[93] Peyghambari, S., Zhang, Y., 2021. Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review. Journal of Applied Remote Sensing. 15(3). DOI: https://doi.org/10.1117/1.JRS.15.031501
[94] Zhu, L., Walker, J.P., Ye, N., et al., 2019. Roughness and vegetation change detection: A pre-processing for soil moisture retrieval from multi-temporal SAR imagery. Remote Sensing of Environment. 225, 93–106. DOI: https://doi.org/10.1016/j.rse.2019.02.027
[95] Ansari, M.Y., Ahmad, A., Khan, S.S., et al., 2020. Spatiotemporal clustering: a review. Artificial Intelligence Review. 53(4), 2381–2423. DOI: https://doi.org/10.1007/s10462-019-09736-1
[96] Kurian, A., Rihan, M., Mano Paul, P., 2025. Anomaly detection for natural disaster preparedness. In Data Science & Exploration in Artificial Intelligence. CRC Press: London, UK. pp. 147–152. DOI: https://doi.org/10.1201/9781003589273-23
[97] Gao, X., Wang, Z., Feng, Y., et al., 2023. Benchmarking Robustness of AI-Enabled Multi-sensor Fusion Systems: Challenges and Opportunities. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, San Francisco, CA, USA, 3–9 December 2023; pp. 871–882. DOI: https://doi.org/10.1145/3611643.3616278
[98] Wu, M., Li, Q., Yang, F., et al., 2023. Learning from biased crowdsourced labeling with deep clustering. Expert Systems with Applications. 211, 118608. DOI: https://doi.org/10.1016/j.eswa.2022.118608
[99] Demir, B., Bovolo, F., Bruzzone, L., 2013. Updating Land-Cover Maps by Classification of Image Time Series: A Novel Change-Detection-Driven Transfer Learning Approach. IEEE Transactions on Geoscience and Remote Sensing. 51(1), 300–312. DOI: https://doi.org/10.1109/TGRS.2012.2195727
[100] Kondylatos, S., Bountos, N.I., Michail, D., et al., 2025. On the Generalization of Representation Uncertainty in Earth Observation. In Proceedings of the 2025 IEEE/CVF International Conference on Computer Vision (ICCV), Honolulu, HI, USA, 19–23 October 2025; pp. 6552–6562. DOI: https://doi.org/10.1109/ICCV51701.2025.00618
[101] Tso, C.-H.M., Henrys, P., Rennie, S., et al., 2020. State Tagging for Improved Earth and Environmental Data Quality Assurance. Frontiers in Environmental Science. 8, 46. DOI: https://doi.org/10.3389/fenvs.2020.00046
[102] Olaya, P., Kennedy, D., Llamas, R., et al., 2023. Building Trust in Earth Science Findings through Data Traceability and Results Explainability. IEEE Transactions on Parallel and Distributed Systems. 34(2), 704–717. DOI: https://doi.org/10.1109/TPDS.2022.3220539
Downloads
How to Cite
Zhang, H., & Zhao, L. (2026). Unsupervised Intelligent Framework for Earth Science Remote Sensing Applications Based on Clustering Algorithms. Journal of Environmental & Earth Sciences, 8(8), 355–381. https://doi.org/10.30564/jees.v8i8.13389
Issue
Article Type
Review
License
Copyright © 2026 Hongyan Zhang, Li Zhao
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
