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Earth and Planetary Sciences
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2025
Enhancing above-ground biomass estimation in agroforestry systems: A scalable deep learning approach using Sentinel-2 data (2025)IEEE Journal of selected topics in applied earth observations and remote sensing, 19, 3589-3604 (E-pub ahead of print/First online). Zhu, X., Luleva, M., Gou, Y., Paris, C. & Shi, Y.https://doi.org/10.1109/JSTARS.2025.3649752Deep learning-based vegetation canopy height mapping with polarimetric SAR: Application of a Polarization Fusion U-Net in Gabon’s tropical forests (2025)Science of Remote Sensing, 12, 1-16. Article 100327. Bhuiyan, R., Paris, C., Wang, T., Khodadadzadeh, M. & Schlund, M.https://doi.org/10.1016/j.srs.2025.100327Optimizing deep learning for satellite hyperspectral data: An xAI-driven approach to hyperparameter selection (2025)In 2025 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 7602-7606). Article 11243437. IEEE (E-pub ahead of print/First online). Linardi, M., Dabo, S. & Paris, C.https://doi.org/10.1109/IGARSS55030.2025.11243437Advanced analysis of agroforestry fractional covers leveraging Sentinel-2 data and deep learning techniques (2025)[Contribution to conference › Abstract] Living Planet Symposium 2025. Farsad Layegh, N., Paris, C., Chemura, A. & Darvishzadeh, R.https://lps25.esa.int/programme/Multimodal satellite-field data integration for scalable crop type mapping with limited labels (2025)IEEE transactions on geoscience and remote sensing, 63. Article 4412612. Abbas, A., Paris, C. & Nelson, A. D.https://doi.org/10.1109/TGRS.2025.3581054Leveraging educational partnerships to integrate education for sustainable development into university geoscience curriculum (2025)International journal of sustainability in higher education, 26(9), 131-148. Maganini, M., Herndon, K. E., Loran, T. M., Griffin, R. E., Anderson, E. R., van der Meer, F. D., Darvishzadeh, R., Paris, C., Rietbroek, R., Huesca Martinez, M. & Schlund, M.https://doi.org/10.1108/IJSHE-07-2024-0445From MODIS to Sentinel-2: A regional comparative analysis of crop-yield prediction with matched spatiotemporal data (2025)IEEE Journal of selected topics in applied earth observations and remote sensing, 18, 27663-27683. Article 21. Adriko, K., Sedona, R., Seguini, L., Riedel, M., Cavallaro, G. & Paris, C.https://doi.org/10.1109/JSTARS.2025.3624046
2024
Reuse out-of-year data to enhance land cover mapping via feature disentanglement and contrastive learning (2024)IEEE Journal of selected topics in applied earth observations and remote sensing, 18, 1681-1694. Dantas, C. F., Gaetano, R., Paris, C. & Ienco, D.https://doi.org/10.1109/JSTARS.2024.3503756A data-driven approach for estimating regional food flows fusing earth observation and geospatial data (2024)In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 897-900) (International Geoscience and Remote Sensing Symposium (IGARSS)). IEEE. Paris, C., Khan, M., Cattaneo, M. & Dou, Y.https://doi.org/10.1109/IGARSS53475.2024.10640910Enhancing Land Cover Mapping: A novel automatic approach to improve mixed spectral pixel classification (2024)In IGARSS 2024: 2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 1017-1020) (International Geoscience and Remote Sensing Symposium (IGARSS)). IEEE. Sharma, S., Sedona, R., Riedel, M., Cavallaro, G. & Paris, C.https://doi.org/10.1109/IGARSS53475.2024.10641976EO-derived geospatial data for monitoring food and nutrition security: A case study of Rwanda (2024)In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 3860-3863) (International Geoscience and Remote Sensing Symposium (IGARSS)). IEEE. Dusabe, B., Dou, Y., Manners, R. & Paris, C.https://doi.org/10.1109/IGARSS53475.2024.10640900Geospatial for Planetary Health. Geoversity short course (2024)[Other contribution › Other contribution]. Geoversity (UT-ITC). Blanford, J. I., Paris, C., Augustijn, P. W. M. & Penning de Vries, M. J. M.https://www.geoversity.io/courses/geospatial-planetary-health/Sen4Map: Advancing mapping with Sentinel-2 by providing detailed semantic descriptions and customizable land-use and land-cover data (2024)IEEE Journal of selected topics in applied earth observations and remote sensing, 17, 13893-13907. Sharma, S., Sedona, R., Riedel, M., Cavallaro, G. & Paris, C.https://doi.org/10.1109/JSTARS.2024.3435081
2023
End-to-end process orchestration of Earth Observation data workflows with apache airflow on high performance computing (2023)In IGARSS 2023: 2023 IEEE International Geoscience and Remote Sensing Symposium (pp. 711-714). Article 10283416. IEEE. Tian, L., Sedona, R., Mozaffari, A., Kreshpa, E., Paris, C., Riedel, M., Schultz, M. G. & Cavallaro, G.https://doi.org/10.1109/IGARSS52108.2023.10283416Enhancing training set through multi-temporal attention analysis in transformers for multi-year land cover mapping (2023)In IGARSS 2023: 2023 IEEE International Geoscience and Remote Sensing Symposium (pp. 5411-5414). Article 10283284. IEEE. Sedona, R., Ebert, J., Paris, C., Riedel, M. & Cavallaro, G.https://doi.org/10.1109/IGARSS52108.2023.10283284A study on the impact of the spatial and spectral resolution on plant species richness in Mediterranean regions using optical remote sensing data (2023)In Image and Signal Processing for Remote Sensing XXIX. Article 127330W. SPIE. Boakye, A. S., Huesca Martinez, M. & Paris, C.https://doi.org/10.1117/12.2679116Accuracy assessment of land-use-land-cover maps: the semantic gap between in situ and satellite data (2023)In Image and Signal Processing for Remote Sensing XXIX. Article 127330M. SPIE. Paris, C., Martinez-Sanchez, L., Velde, M. v. d., Sharma, S., Sedona, R. & Cavallaro, G.https://doi.org/10.1117/12.2679433AI4SmallFarms: A data set for crop field delineation in Southeast Asian smallholder farms (2023)IEEE geoscience and remote sensing letters, 20, 1-5. Article 2505705. Persello, C., Grift, J., Fan, X., Paris, C., Hänsch, R., Koeva, M. & Nelson, A.https://doi.org/10.1109/LGRS.2023.3323095Towards Explainable AI4EO: An Explainable Deep Learning Approach for Crop Type Mapping using Satellite Images Time Series (2023)In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium (pp. 1088-1091). Article 10283125. IEEE. Abbas, A., Linardi, M., Vareille, E., Christophides, V. & Paris, C.https://doi.org/10.1109/IGARSS52108.2023.10283125ExtremeEarth: Managing Water Availability for Crops Using Earth Observation and Machine Learning (2023)In Proceedings 26th International Conference on Extending Database Technology (EDBT 2023), Ioannina, Greece, March 28 - March 31 (pp. 749-756) (Advances in Database Technology; Vol. 26). OpenProceedings.org. Appel, F., Bach, H., Migdall, S., Koubarakis, M., Stamoulis, G., Bilidas, D., Pantazi, D. A., Bruzzone, L., Paris, C. & Weikmann, G.https://doi.org/10.48786/edbt.2023.62Multi-year mapping of water demand at crop level: An end-to-end workflow based on high-resolution crop type maps and meteorological data (2023)IEEE Journal of selected topics in applied earth observations and remote sensing, 16, 6758-6775. Weikmann, G., Marinelli, D., Paris, C., Migdall, S., Gleisberg, E., Appel, F., Bach, H., Dowling, J. & Bruzzone, L.https://doi.org/10.1109/JSTARS.2023.3294107Toward the production of spatiotemporally consistent annual land cover maps using Sentinel-2 time series (2023)IEEE geoscience and remote sensing letters, 20, 1-5. Article 2505805. Sedona, R., Paris, C., Ebert, J., Riedel, M. & Cavallaro, G.https://doi.org/10.1109/LGRS.2023.3329428
2022
A novel approach for environmental monitoring based on the integration of multi-temporal multi-source Earth Observation data and field surveys in a spatio-temporal framework (2022)In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 5897-5900). IEEE. Paris, C., Kotowska, M. M., Erasmi, S. & Schlund, M.https://doi.org/10.1109/igarss46834.2022.9884130An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory (2022)In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 203-206). Article 9883655. IEEE. Sedona, R., Paris, C., Tian, L., Riedel, M. & Cavallaro, G.https://doi.org/10.1109/IGARSS46834.2022.9883655A Scalable High-Performance Unsupervised System for Producing Large-Scale HR Land Cover Maps: The Italian country case study (2022)IEEE Journal of selected topics in applied earth observations and remote sensing, 15, 9146-9159. Paris, C., Gasparella, L. & Bruzzone, L.https://doi.org/10.1109/JSTARS.2022.3209902
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