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AI4CH - Artificial Intelligence for Cultural Heritage
The AI4CH project concerns the creation of a joint Italian-Israeli laboratory operating in the field of Artificial Intelligence applied to the Cultural Heritage.
On the Italian side, the laboratory brings together knowledge and experiences of the Bruno Kessler Foundation (FBK), the University of Trento (UniTN) and the University of Modena and Reggio Emilia (UniMoRe), under the coordination of the National Interuniversity Consortium for Informatics - CINI.
On the Israeli side, the AI4CH joint lab involves the University of Haifa and the Technion Institute of Technology.
AI4CH is funded by the Minister of Foreign Affairs and International Cooperation (MAECI) under the Call for the institution of a Joint Italian - Israeli Laboratories - year 2018.
The joint laboratory will investigate new methods and solutions in the fields of (i) intelligent extraction of information from 2D & 3D data and (ii) intelligent support to knowledge exploration. The joint lab will have particular attention to the fruition of the achieved results, the use and involvement of visitors, and the support to experts in the Cultural Heritage field.
The three-year project includes also the organization of events like Workshops, Summer Schools and exchanges of researchers/professors between Italy and Israel in order to make the research activities more integrated and, therefore, the scientific collaboration more solid and capable to continue beyond the lifetime of the project.
- Matrone, F., Grilli, E., Martini, M., Paolanti, M., Pierdicca, R., Remondino, F., 2020: Comparing Machine and Deep Learning methods for large 3D heritage semantic segmentation. MDPI International Journal of Geo-Information, 9, 535
- Teruggi, S., Grilli, E., Russo, M., Fassi, F., Remondino, F., 2020: A hierarchical machine learning approach for multi-level and multi-resolution 3D point cloud classification. MDPI Remote Sensing, 12(16), 2598
- Matrone, F., Lingua, A., Pierdicca, R., Malinverni, E. S., Paolanti, M., Grilli, E., Remondino, F., Murtiyoso, A., and Landes, T., 2020: A benchmark for large-scale heritage point cloud semantic segmentation. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1419–1426
- Grilli, E., Remondino, F., 2020: Machine learning generalisation across different 3D architectural. ISPRS International Journal of Geo-Information, 9(6), 379
- E. Grilli, E. Özdemir and F. Remondino, 2019: Application of machine and deep learning strategies for the classification of heritage point clouds. ISPRS Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., Vol. XLII-4/W18, pp. 447–454
- Grilli, E., Menna, F., Remondino, F., 2017: A review of point clouds segmentation and classification algorithms. ISPRS Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., Vol. XLII-2-W3, pp. 339-344