dr. C. Paris (Claudia)

Assistant Professor

About Me

I am an Assistant Professor in the Department of Natural Resources, Faculty of Geo-information Science and Earth Observation. I received my Ph.D in Information and Communication Technology from the University of Trento, Italy, in 2016. In 2014, I was a visiting PhD student at the Rochester Institute of Technology (RIT), Rochester, New York State, USA, working on the fusion of airborne and terrestrial LiDAR data. In 2016, I was a visiting Post-Doc at the Instituto Superior Técnico, Lisbon, Portugal, working on the superresolution of multiresolution multispectral remote sensing images. My research concentrates mainly on passive and active remote sensing and, in particular, on designing novel and automatic system architecture for large-scale environmental monitoring. My main research interests include remote sensing image processing, signal processing and pattern recognition with specific reference to classification and fusion of multisource remote sensing data (LiDAR data, hyperspectral, multispectral and high resolution optical images), multi-temporal image analysis, domain-adaptation methods and deep-learning models. Moreover, my research interests are also focused on the use of remote sensing data for sustainable development.


Engineering & Materials Science
Long Short-Term Memory
Remote Sensing
Time Series
Earth & Environmental Sciences
Land Cover
Remote Sensing


Sedona, R. , Paris, C., Tian, L., Riedel, M., & Cavallaro, G. (2022). An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 203-206). [9883655] IEEE/EUCA. https://doi.org/10.1109/IGARSS46834.2022.9883655
Migdall, S., Dotzler, S., Gleisberg, E., Appel, F., Muerth, M., Bach, H., Weikmann, G. , Paris, C., Marinelli, D., & Bruzzone, L. (2022). Crop Water Availability Mapping in the Danube Basin Based on Deep Learning, Hydrological and Crop Growth Modelling. Engineering proceedings, 9(1), [42]. https://doi.org/10.3390/engproc2021009042
Paris, C., Gasparella, L., & Bruzzone, L. (2022). A Scalable High-Performance Unsupervised System for Producing Large-Scale HR Land Cover Maps: The Italian country case study. IEEE Journal of selected topics in applied earth observations and remote sensing, 15, 9146-9159. https://doi.org/10.1109/JSTARS.2022.3209902
Weikmann, G. , Paris, C., & Bruzzone, L. (2021). Multi-year crop type mapping using pre-Trained deep long-short term memory and Sentinel 2 image time series. In L. Bruzzone, F. Bovolo, & J. A. Benediktsson (Eds.), Image and Signal Processing for Remote Sensing XXVII [118620O] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11862). SPIE Press. https://doi.org/10.1117/12.2600559
Podsiadlo, I. , Paris, C., & Bruzzone, L. (2021). An Approach Based on Low Resolution Land-Cover-Maps and Domain Adaptation to Define Representative Training Sets at Large Scale. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 313-316). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553498
Sedona, R. , Paris, C., Cavallaro, G., Bruzzone, L., & Riedel, M. (2021). A high-performance multispectral adaptation GAN for harmonizing dense time series of Landsat-8 and Sentinel-2 images. IEEE Journal of selected topics in applied earth observations and remote sensing, 14, 10134 - 10146. https://doi.org/10.1109/jstars.2021.3115604
Weikmann, G. , Paris, C., & Bruzzone, L. (2021). TimeSen2Crop: A million labeled samples dataset of Sentinel 2 image time series for crop-type classification. IEEE Journal of selected topics in applied earth observations and remote sensing, 14, 4699-4708. [9408357]. https://doi.org/10.1109/JSTARS.2021.3073965
Hagos, D. H., Kakantousis, T., Vlassov, V., Sheikholeslami, S., Wang, T., Dowling, J. , Paris, C., Marinelli, D., Weikmann, G., Bruzzone, L., Khaleghian, S., Krmer, T., Eltoft, T., Marinoni, A., Pantazi, D-A., Stamoulis, G., Bilidas, D., Papadakis, G., Mandilaras, G., ... Cziferszky, A. (2021). ExtremeEarth meets satellite data from space. IEEE Journal of selected topics in applied earth observations and remote sensing, 14, 9038-9063. https://doi.org/10.1109/JSTARS.2021.3107982
Troumpoukis, A., Konstantopoulos, S., Mouchakis, G., Prokopaki-Kostopoulou, N. , Paris, C., Bruzzone, L., Pantazi, D. A., & Koubarakis, M. (2020). GeoFedBench: A benchmark for federated GeoSPARQL query processors. CEUR workshop proceedings, 2721, 229-232.
Paris, C., Weikmann, G., & Bruzzone, L. (2020). A study of the robustness of the long short-term memory classifier to cloudy time series of multispectral images. In SPIE Remote Sensing 2020 (pp. 1-10). (Proceedings of SPIE - the international society for optical engineering; Vol. 11533). https://doi.org/10.1117/12.2574383
Podsialdo, I. , Paris, C., Callegari, M., Marin, C., Gunter, D., Strasser, U., Notarnicola, C., & Bruzzone, L. (2020). Integrating models and remote sensing data for distributed glacier mass balance estimation. IEEE Journal of selected topics in applied earth observations and remote sensing, 6177-6194. https://doi.org/10.1109/jstars.2020.3028653
Paris, C., & Bruzzone, L. (2020). A novel approach to the unsupervised extraction of reliable training samples from thematic products. IEEE transactions on geoscience and remote sensing, 59(3), 1930-1948. [9121728]. https://doi.org/10.1109/tgrs.2020.3001004
Harikumar, A. , Paris, C., Bovolo, F., & Bruzzone, L. (2020). A crown quantization-based approach to tree-species classification using high-density airborne laser scanning data. IEEE transactions on geoscience and remote sensing, 59(5), 4444-4453. https://doi.org/10.1109/tgrs.2020.3012343
Gregorio, L. D., Bovolo, F., Callegari, M., Günther, D., Marin, C., Niroumand-Jadidi, M. , Paris, C., Podsiadlo, I., Strasser, U., Zebisch, M., Bruzzone, L., & Notarnicola, C. (2020). Snow Parameters Estimation Through New Data Fusion Approaches Involving a Hydrological Model and Remote Sensing Products. 1. Abstract from International Conference on Snow Hydrology., Bolzano, Italy. https://snowhydro.eurac.edu/
Paris, C., Bioucas-Dias, J., & Bruzzone, L. (2019). A Novel Sharpening Approach for Superresolving Multiresolution Optical Images. IEEE transactions on geoscience and remote sensing, 57(3), 1545-1560. [8472286]. https://doi.org/10.1109/TGRS.2018.2867284
Paris, C., & Bruzzone, L. (2019). A Growth-Model-Driven Technique for Tree Stem Diameter Estimation by Using Airborne LiDAR Data. IEEE transactions on geoscience and remote sensing, 57(1), 76-92. [8428490]. https://doi.org/10.1109/TGRS.2018.2852364
Podsiadlo, I. , Paris, C., Bovolo, F., Callegari, M., De Gregorio, L., Günther, D., Marin, C., Marke, T., Niroumand-Jadidi, M., Notarnicola, C., Strasser, U., Zebisch, M., & Bruzzone, L. (2019). Integration of hydro-climatological model and remote sensing for glacier mass balance estimation. In SPIE Remote Sensing 2019 (Proceedings of SPIE - the international society for optical engineering; Vol. 11155). https://doi.org/10.1117/12.2533232
Koubarakis, M., Bereta, K., Bilidas, D., Giannousis, K., Ioannidis, T., Pantazi, D-A., Stamoulis, G., Haridi, S., Vlassov, V., Bruzzone, L. , Paris, C., Eltoft, T., Krämer, T., Charalabidis, A., Karkaletsis, V., Konstantopoulos, S., Dowling, J., Kakantousis, T., Datcu, M., ... Fleming, A. (2019). From copernicus big data to extreme earth analytics. In EDBT/ICDT 2019 Joint Conference (pp. 690-693). [321] https://doi.org/10.5441/002/edbt.2019.88
Bovolo, F., Bruzzone, L., Fernández-Prieto, D. , Paris, C., Solano-Correa, Y. T., Volden, E., & Zanetti, M. (2019). Big Data from Space for Precision Agriculture Applications. In S. Nativi, C. Wang, G. Landgraf, M. A. Liberti, P. Mazzetti, & Z. S. Mohamed-Ghouse (Eds.), 11th International Symposium on Digital Earth (ISDE 11): 24-27 September 2019, Florence, Italy (pp. 1-3). [012004] (IOP Conference Series: Earth and Environmental Science; Vol. 509). IOP. https://doi.org/10.1088/1755-1315/509/1/012004
Paris, C., & Bruzzone, L. (2019). Automatic Extraction of Weak Labeled Samples From Existing Thematic Products For Training Convolutional Neural Networks. In 2019 IEEE International Geoscience & Remote Sensing Symposium: Proceedings (pp. 5722-5725). [8900649] IEEE. https://doi.org/10.1109/IGARSS.2019.8900649
Marinelli, D. , Paris, C., & Bruzzone, L. (2019). An Automatic Technique for Deciduous Trees Detection in High Density Lidar Data Based on Delaunay Triangulation. In 2019 IEEE International Geoscience & Remote Sensing Symposium: Proceedings (pp. 94-97). [8899772] IEEE. https://doi.org/10.1109/IGARSS.2019.8899772

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Courses Academic Year  2022/2023

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University of Twente
Drienerlolaan 5
7522 NB Enschede
The Netherlands

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University of Twente
P.O. Box 217
7500 AE Enschede
The Netherlands