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.
Expertise
Earth and Planetary Sciences
- Datum
- Image
- Map
- Time Series
- Land Cover
- Detection
- Rangefinding
- Sentinel-2
Organisations
Publications
2023
Enhancing training set through multi-temporal attention analysis in transformers for multi-year land cover mappingIn IGARSS 2023: 2023 IEEE International Geoscience and Remote Sensing Symposium, Article 10283284 (pp. 5411-5414). IEEE. Sedona, R., Ebert, J., Paris, C., Riedel, M. & Cavallaro, G.https://doi.org/10.1109/IGARSS52108.2023.10283284End-to-end process orchestration of Earth Observation data workflows with apache airflow on high performance computingIn IGARSS 2023: 2023 IEEE International Geoscience and Remote Sensing Symposium, Article 10283416 (pp. 711-714). IEEE. Tian, L., Sedona, R., Mozaffari, A., Kreshpa, E., Paris, C., Riedel, M., Schultz, M. G. & Cavallaro, G.https://doi.org/10.1109/IGARSS52108.2023.10283416Accuracy assessment of land-use-land-cover maps: the semantic gap between in situ and satellite dataIn 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/10.1117/12.2679433A study on the impact of the spatial and spectral resolution on plant species richness in Mediterranean regions using optical remote sensing dataIn Image and Signal Processing for Remote Sensing XXIX, Article 127330W. SPIE. Boakye, A. S., Huesca Martinez, M. & Paris, C.https://doi.org/10.1117/12.2679116AI4SmallFarms: A data set for crop field delineation in Southeast Asian smallholder farms, Article 2505705, 1-5. 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 SeriesIn IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Article 10283125 (pp. 1088-1091). 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 learningIn Proceedings 26th International Conference on Extending Database Technology ( EDBT 2023 ) (pp. 749-756). 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, 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, Article 2505805, 1-5. 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 frameworkIn 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 MemoryIn IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Article 9883655 (pp. 203-206). 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, 9146-9159. Paris, C., Gasparella, L. & Bruzzone, L.https://doi.org/10.1109/JSTARS.2022.3209902Crop Water Availability Mapping in the Danube Basin Based on Deep Learning, Hydrological and Crop Growth Modelling, Article 42. Migdall, S., Dotzler, S., Gleisberg, E., Appel, F., Muerth, M., Bach, H., Weikmann, G., Paris, C., Marinelli, D. & Bruzzone, L.https://doi.org/10.3390/engproc2021009042An interactive strategy for the training set definition based on active self-paced learning implemented on a cloud-computing platform, 1-5. Paris, C., Orlandi, L. & Bruzzone, L.https://doi.org/10.1109/LGRS.2021.3114611A triangulation-based technique for tree-top detection in heterogeneous forest structures using high density LiDAR data. Marinelli, D., Paris, C. & Bruzzone, L.https://doi.org/10.1109/LGRS.2021.3115470An approach based on Deep Learning for tree species classification in LiDAR data acquired in mixed forest, Article 7004305. Marinelli, D., Paris, C. & Bruzzone, L.https://doi.org/10.1109/LGRS.2022.3181680ESA CCI High Resolution Land Cover: Methodology and EO Data Processing Chain. Paris, C., Bruzzone, L., Bovolo, F., Maggiolo, L., Gamba, P., Moser, G., Pierantoni, G., Podsiadlo, I., Solarna, D., Sorriso, T., Zanetti, M. & Meshkini, K.
2021
A high-performance multispectral adaptation GAN for harmonizing dense time series of Landsat-8 and Sentinel-2 images, 10134-10146. Sedona, R., Paris, C., Cavallaro, G., Bruzzone, L. & Riedel, M.https://doi.org/10.1109/jstars.2021.3115604ExtremeEarth meets satellite data from space, 9038-9063. 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.https://doi.org/10.1109/JSTARS.2021.3107982TimeSen2Crop: A million labeled samples dataset of Sentinel 2 image time series for crop-type classification, Article 9408357, 4699-4708. Weikmann, G., Paris, C. & Bruzzone, L.https://doi.org/10.1109/JSTARS.2021.3073965An Approach Based on Low Resolution Land-Cover-Maps and Domain Adaptation to Define Representative Training Sets at Large ScaleIn IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 313-316). IEEE. Podsiadlo, I., Paris, C. & Bruzzone, L.https://doi.org/10.1109/IGARSS47720.2021.9553498Multi-year crop type mapping using pre-Trained deep long-short term memory and Sentinel 2 image time seriesIn Image and Signal Processing for Remote Sensing XXVII, Article 118620O. SPIE. Weikmann, G., Paris, C. & Bruzzone, L.https://doi.org/10.1117/12.2600559Water Stress Assessment in Austria based on Deep Learning and Crop Growth ModellingIn Proceedings of the 2021 conference on Big Data from Space (pp. 69-72). Publications Office of the European Union. Migdall, S., Dotzler, S., Miesgang, C., Appel, F., Muerth, M., Bach, H., Weikmann, G., Paris, C., Marinelli, D. & Bruzzone, L.https://iris.unitn.it/handle/11572/330202Artificial Intelligence and big data technologies for Copernicus data: The EXTREMEEARTH projectIn Proceedings of the 2021 conference on Big Data from Space (pp. 9-12). Publications Office of the European Union. Koubarakis, M., Stamoulis, G., Bilidas, D., Ioannidis, T., Mandilaras, G., Pantazi, D.-A., Papadakis, G., Vlassov, V., Payberah, A. H., Wang, T., Sheikholeslami, S., Hagos, D. H., Bruzzone, L., Paris, C., Weikmann, G., Marinelli, D., Eltoft, T., Marinoni, A., Kraemer, T., … Cziferszky, A.https://iris.unitn.it/handle/11572/330197
2020
Integrating models and remote sensing data for distributed glacier mass balance estimation, 6177-6194. Podsialdo, I., Paris, C., Callegari, M., Marin, C., Gunter, D., Strasser, U., Notarnicola, C. & Bruzzone, L.https://doi.org/10.1109/jstars.2020.3028653
Research profiles
Courses academic year 2023/2024
Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.
Courses academic year 2022/2023
Address
University of Twente
Langezijds (building no. 19), room 1121
Hallenweg 8
7522 NH Enschede
Netherlands
University of Twente
Langezijds 1121
P.O. Box 217
7500 AE Enschede
Netherlands
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