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Publications

  1. Bizzego, A.; Bussola, N.; Salvalai, D.; Chierici, M.; Maggio, V.; Jurman, G.; Furlanello, C.,
    Proceedings of 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB),
    2019
    , pp. 1-
    8
    , (2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB),
    Siena, Italy,
    9-11 July 2019)
  2. Rinnert, Kurt; Cristoforetti, Marco,
    in «EPJ WEB OF CONFERENCES»
    Proceedings of the 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018),
    vol.214,
    2019
    , pp. 06038-
    , (23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018),
    Sofia, Bulgaria,
    9-13 July 2018)
  3. Coviello, Luca; Cristoforetti, Marco; Furlanello, Cesare,
    GARR 2019 Conference - Connecting the future - Selected Papers,
    2019
    , pp. 34-
    37
    , (GARR 2019 Conference “Connecting the future”,
    Torino, Italy,
    4-6 giugno 2019)
  4. Furlanello, Cesare; Jurman, Giuseppe; Dolci, Claudia; Villani, Rachele; Gadotti, Erik; Venuti, Paola,
    Talents and technology: training the artificial intelligence generation,
    Talent Education 2019,
    Ljubljana,
    MIB,
    2019
    , pp. 31-
    35
    , (IV Talent Education 2019,
    Portoroz,
    24-26 Oct 2019)
  5. Franch, Gabriele; Maggio, Valerio; Coviello, Luca; Jurman, Giuseppe; Furlanello, Cesare; Pendesini, Marta,
    2019,
  6. Franch, Gabriele; Maggio, Valerio; Coviello, Luca; Jurman, Giuseppe; Furlanello, Cesare; Pendesini, Marta,
    2019,
    TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility..
  7. Franch, Gabriele; Maggio, Valerio; Coviello, Luca; Jurman, Giuseppe; Furlanello, Cesare; Pendesini, Marta,
    2019,
    TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility..
  8. Mohammadian Rad, Nastaran; Kia, Seyed Mostafa; Zarbo, Calogero; van Laarhoven, Twan; Jurman, Giuseppe; Venuti, Paola; Marchiori, Elena; Furlanello, Cesare,
    in «SIGNAL PROCESSING»,
    vol. 144,
    2018
    , pp. 180 -
    191
  9. Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare,
    in «BMC BIOINFORMATICS»,
    vol. 19,
    n. S2,
    2018
  10. Rossi, C.; Acerbo, F. S.; Ylinen, K.; Juga, I.; Nurmi, P.; Bosca, A.; Tarasconi, F.; Cristoforetti, M.; Alikadic, A.,
    in «INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION»,
    vol. 30,
    2018
    , pp. 145 -
    157

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