Artificial Intelligence applied to Cultural Heritage
AI4CH
The AI4CH project concerns the creation of a joint Italian-Israeli laboratory operating in the field of Artificial Intelligence applied to 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.
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.
Official project website: http://ai4ch.fbk.eu
Videos: Generalization of 3D point cloud classification, 3D Classification of Neptun temple, 3D Classification of the Sarcophagus of the Sposes, 3D Classification of an Etruscan tomb
Related publications:
- 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
- Grilli, E., Remondino, F., 2019: Classification of 3D Digital Heritage. MDPI Remote Sensing, Vol. 11(7), 847; https://doi.org/10.3390/rs11070847
- 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
Duration: 2019-2021
Funding: Italian Ministry of Foreign Affairs and International Cooperation (MAECI)