dr. C. Paris (Claudia)

Engineering & Materials Science

# Long Short-Term Memory
# Remote Sensing
# Satellites
# Time Series

Earth & Environmental Sciences

# Detection
# Land Cover
# Learning
# Remote Sensing

Faculty of Geo-Information Science and Earth Observation (ITC), Department of Natural Resources (NRS)

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. 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

Marinelli, D. , Paris, C., & Bruzzone, L. (2022). An approach based on Deep Learning for tree species classification in LiDAR data acquired in mixed forest. IEEE geoscience and remote sensing letters, 19, [7004305]. https://doi.org/10.1109/LGRS.2022.3181680

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

Paris, C., Kotowska, M. M., Erasmi, S. , & Schlund, M. (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. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 5897-5900). IEEE. https://doi.org/10.1109/igarss46834.2022.9884130

Marinelli, D. , Paris, C., & Bruzzone, L. (2022). A triangulation-based technique for tree-top detection in heterogeneous forest structures using high density LiDAR data. IEEE geoscience and remote sensing letters, 19. https://doi.org/10.1109/LGRS.2021.3115470

Paris, C., Orlandi, L., & Bruzzone, L. (2022). An interactive strategy for the training set definition based on active self-paced learning implemented on a cloud-computing platform. IEEE geoscience and remote sensing letters, 19, 1-5. https://doi.org/10.1109/LGRS.2021.3114611

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). Institute of Physics (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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Naar mijn profiel

Master

Geo-Information Science and Earth Observation

Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.