Self-Supervised Learning Framework for Multimodal Environmental Pollution Assessment
DOI:
https://doi.org/10.64137/31079377/IJMSD-V2I2P105Keywords:
Self-Supervised Learning, Multimodal Data Fusion, Environmental Pollution Assessment, Deep Learning, Remote Sensing, Air Quality MonitoringAbstract
Environmental pollution assessment traditionally relies on separate monitoring networks that capture heterogeneous data streams, including satellite imagery, ground-based sensor arrays, and meteorological logs. Integrating these multimodal data sources remains a significant challenge due to the scarcity of annotated labels, high dimensionality, and spatiotemporal misalignments. This paper introduces an advanced Self-Supervised Learning (SSL) framework designed specifically for multimodal environmental pollution assessment. By leveraging contrastive learning paradigms alongside masked autoencoder architectures, our framework extracts robust, joint representations from unlabeled multi-spectral satellite imagery, continuous gaseous sensor streams, and localized weather data. We validate our model across diverse urban and industrial zones utilizing a comprehensive dataset spanning three distinct geographical regions. The results demonstrate that the self-supervised pre-trained representations significantly outperform conventional supervised and unsupervised baselines in downstream tasks, including fine-grained particulate matter (PM2.5 and PM10) estimation and industrial anomaly detection. The framework achieves superior performance even when labeled data is extremely scarce, establishing a highly scalable and cost-effective paradigm for global environmental monitoring and policy formulation.
References
[1] C. Mathilde, M. Ishan, M. Julien, G. Priya, B. Piotr, and J. Armand, “Unsupervised Learning of Visual Features by Contrasting Cluster Assignments,” Advances in Neural Information Processing Systems, vol. 33, 2020, Available: https://proceedings.neurips.cc/paper/2020/hash/70feb62b69f16e0238f741fab228fec2-Abstract.html
[2] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A Simple Framework for Contrastive Learning of Visual Representations,” proceedings.mlr.press, Nov. 21, 2020. https://proceedings.mlr.press/v119/chen20j.html
[3] K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked Autoencoders Are Scalable Vision Learners,” Thecvf.com, pp. 16000–16009, 2022, Available: https://openaccess.thecvf.com/content/CVPR2022/html/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper
[4] C.-Y. Hsu, C.-D. Wu, Y.-P. Hsiao, Y.-C. Chen, M.-J. Chen, and S.-C. Lung, “Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations,” Remote Sensing, vol. 10, no. 12, p. 1971, Dec. 2018, doi: https://doi.org/10.3390/rs10121971.
[5] W.-H. Tseng, H.-A. Lê, A. Boulch, Sébastien Lefèvre, and D. Tiede, “CROCO: CROSS-MODAL CONTRASTIVE LEARNING FOR LOCALIZATION OF EARTH OBSERVATION DATA,” ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, vol. V–22022, pp. 415–421, May 2022, doi: https://doi.org/10.5194/isprs-annals-v-2-2022-415-2022.
[6] F. Radenović, G. Tolias, and O. Chum, “Fine-Tuning CNN Image Retrieval with No Human Annotation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 7, pp. 1655–1668, Jul. 2019, doi: https://doi.org/10.1109/TPAMI.2018.2846566.
[7] A. Radford et al., “Learning Transferable Visual Models From Natural Language Supervision,” proceedings.mlr.press, Jul. 01, 2021. https://proceedings.mlr.press/v139/radford21a
[8] P. Lara-Benítez, M. Carranza-García, J. M. Luna-Romera, and J. C. Riquelme, “Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting,” LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas), Mar. 2020, doi: https://doi.org/10.20944/preprints202003.0096.v1.
[9] “Vaswani, A., Shazeer, N., Parmar, N., et al. (2017) Attention Is All You Need. Advances in Neural Information Processing Systems 30 Annual Conference on Neural Information Processing Systems 2017, Long Beach, 4-9 December 2017, 5998-6008 - References - Scientific Research Publishing,” Scirp.org, 2017. https://www.scirp.org/reference/referencespapers?referenceid=3381180
[10] Y. Wang, C. M. Albrecht, A. Braham, L. Mou, and Xiao Xiang Zhu, “Self-Supervised Learning in Remote Sensing: A review,” IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 4, pp. 213–247, Dec. 2022, doi: https://doi.org/10.1109/mgrs.2022.3198244.
[11] A. de Villeroché et al., “Physics-informed neural networks for atmospheric flow modeling of pollutant dispersion in industrial sites,” Air Quality, Atmosphere & Health, vol. 19, no. 3, Feb. 2026, doi: https://doi.org/10.1007/s11869-026-01934-5.
