Marco Fiorucci is a Researcher in Machine Learning at the Centre for Cultural Heritage Technology (CCHT) of the Istituto Italiano di Tecnologia (IIT). He is working mainly in the field of Machine Learning, with particular emphasis on optimal transport and generative models. Marco has won a MSCA-IF for his OPTIMAL project that aims to develop an efficient Machine Learning approach based on Optimal Transport to automatically detect looting (past and present) directly on airborne LiDAR point cloud time-series. He is a computer scientist with a solid broad background, spanning from Physics and Machine Learning to Graph Theory and Statistics. He has successfully worked on several different interdisciplinary projects in academia and in industry. He received his Master's degree (summa cum laude) and his PhD in Computer Science from Ca’ Foscari University of Venice respectively in 2015 and 2019. He held a visiting research position at the University of Alicante and at VTT (Finland). More recently, he has shifted his attention to analysing EO data (multispectral and hyperspectral data) and airborne LiDAR for detecting sub-surface archaeological sites (ESA-CLS).
IIT People Search
Naylor P., Di Carlo D., Traviglia A., Yamada M., Fiorucci M.
Implicit Neural Representation for Change Detection
2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024, pp. 935-945
Conference Paper Conference
Sech G., Soleni P., Verschoof-van der Vaart B., Kokalj Z., Traviglia A., Fiorucci M.
Transfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures On LiDAR Data
Digest - International Geoscience and Remote Sensing Symposium (IGARSS), pp. 6987-6990
Traviglia A., Artesani A., Fiorucci M., Ljubenovic M.
Advances in Hyperspectral Data Processing for Cultural Heritage
European Physical Journal Plus
L'intelligenza artificiale a servizio dell'arte
Implicit Learning for Unsupervised Change Detection
OPtimal Transport for Identifying Marauder Activities on Lidar
Okinawa Institute of Technology (Okinawa, Japan)
Scalable Unbalanced Optimal Transport for Change Detection
The Institute of Statistical Mathematics, Tokyo
Unbalanced Optimal Transport for Change Detection
Graph Summarization Using Regular Partition and Its Use in Graph Search
Department of Mathematics and Systems Analysis, Aalto University
Fiorucci M., Naylor P., Yamada M.
Optimal Transport for Change Detection on LiDAR Point Clouds
Digest - International Geoscience and Remote Sensing Symposium (IGARSS), pp. 982-985
Traviglia A., Fiorucci M.
Graph Convolutional Neural Networks for Cultural Heritage: Applications in RS recognition, numismatics and epigraphy
Machine Learning in Archaeology, Rome
Pelosin F., Fiorucci M., and Pelillo M.
Graph Summarization Using Regular Partitions
The 8th International Conference on Network Analysis, Moscow
Colleagues of Cultural Heritage Technologies
Lilith Casanova Administrative Assistant Cultural Heritage Technologies Procurement Directorate Res. Line Admin. Support Robotics & Comp