Grants and Funded Projects

The CCHT finances its research activities on the base of competitive funding from European, national and international institutions. The Center participates in collaborative projects with private and public companies to develop technologies and push the innovation for the safeguard of cultural assets. Here a list of the funded projects:


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Cultural Landscapes Scanner (CLS): Earth Observation and automated detection of subsoil undiscovered cultural heritage sites via AI approaches 

Sub-surface, yet-undiscovered Cultural Heritage sites (CH) can be identified on Remote Sensing (RS) data from a variety of sensors (multispectral, hyperspectral and radar satellite platforms, etc.) in the form of anomalies or traces detectable on bare soils, crops or vegetation. The current extraordinary availability of free RS data through platforms like Copernicus poses severe hindrances in terms of processing and interpreting them to the point that the quantity of data is not manageable by traditional ‘human’ visual interpretation. This entails developing Artificial Intelligence (AI) methods to automatically process the data in order to identify buried CH sites.
The CLS project takes charge of it by supporting the development of specific methods that look at automatically identify specific CH objects and patterns related to anthropogenic interference on landscapes in the past using latest breakthroughs in Machine Learning and Computer Vision. In particular, this project seeks to kickstart the application of cutting-edge computational methods in order to define a broad-spectrum, adaptable and robust automated recognition procedure customised for CH objects in remotely sensed data available from Copernicus platform.
Automating remote sensing analytics via Artificial Intelligence will produce large benefits in terms of archaeological object detection in satellite imagery and represents a significant breakthrough in the discipline as it will replace existing procedures based on subjective observation.
The Cultural Landscapes Scanner pilot-project is the result of a partnership between IIT’s Centre for Cultural Heritage Technology and European Space Agency (ESA) as part of the ESA co-funded research programme Discovery & Preparation.

Partners: IIT, European Space Agency (ESA)

Period: 2020/2022

Funding body: European Space Agency (ESA)

Website: ccht.iit.it/grants/cls


 
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OPTIMAL: OPtimal Transport for Identifying Marauder Activities on LiDAR

Illegal excavation of archaeological sites aimed at collecting historical material culture (""looting"") is a pressing problem on a global scale. The global upsurge of in the illegal excavation of cultural heritage sites (e.g. in connection to turmoils in Middle East or due to the impossibility of monitoring inaccessible areas, like in South America) and the subsequent trafficking of antiquities, exacerbated by the Covid lockdown, calls for the timely development of automatic means for identifying looting activities. The OPTIMAL (OPtimal Transport for Identifying Marauder Activities on LiDAR) project aims to tackle this challenge by developing an efficient and principled Machine Learning (ML) approach based on Optimal Transport to automatically detect looting (past and present) directly on airborne Light Detection And Ranging (LiDAR) point cloud time-series. OPTIMAL proposes, for the first time, the use of LiDAR for monitoring and assessing the damages of looting based on LiDAR’s unique ability to penetrate forest canopies and enabling to see a range of looting-related features under the canopy (e.g. shape and depth of the lootings pits) that otherwise would remain hidden due to vegetation covers. OPTIMAL will create and make publicly available the first multi-temporal LiDAR dataset for illegal activities’ identification to foster the interest of MLs researchers in developing new methods to tackle challenges in landscape archaeology and to evaluate the developed ML approach. Results of this interdisciplinary research will be widely disseminated within Cultural Heritage, Remote Sensing and Machine Learning communities and to others that can exploit OPTIMAL’s results. A communication strategy will be designed to ignite enthusiasm for technological advancements for the protection of our Heritage.

 

Partners: IIT, Kyoto University, RIKEN

Period: 2022/2024

Funding body: European Commission (MSCA-IF-2020)

Website: comnig soon!


 
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RUTE: Revealing Unseen Text with THz Waves

Safely stored in archives’ storages across the world, ancient handwritten books (codices) that are too fragile to be opened, page-turned, or unfolded, are not open to consultation nor accessible, their content hidden from both experts and public. The main goal of the proposed project is to uncover their content and Reveal Unseen Text (RUTE) by reading through them without opening them. Current approaches for facilitating this task are mostly based on techniques that expose fragile manuscripts to ionizing radiation (e.g., X-ray computed tomography) potentially causing damage to ancient inks or support. To prevent damage of valuable manuscripts or written text, RUTE will exploit a non-ionizing technique, terahertz (THz) time-domain imaging, that can penetrate highly absorbing materials (e.g., paper, papyrus, or parchment) and detect paper-air gaps in the sample. Detected paper-air gaps lead to retrieving images that represent a single page of closed books often corrupted by severe system-induced blurring and noise effects. Until now, only limited attempts have been made to reduce these degradation effects (i.e., blur and noise) with none of them tailored to THz images that contain text. To fill this gap, RUTE will facilitate a unique approach to data creation and collection and introduce a highly interdisciplinary methodological process for reading through closed books. The proposed methodology will facilitate detection of paper-air gaps and pages separation, text enhancement, and document images restoration. RUTE will have a tremendous impact on analysis, preservation, and digitisation of valuable European and World cultural heritage assets by enabling the possibility to access knowledge that would otherwise remain undiscovered.

 

Partners: IIT

Period: 2022/2024

Funding body: European Commission (MSCA-IF-2020)

Website: comnig soon!


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NETwork and digital platform for Cultural Heritage Enhancing and Rebuilding (NETCHER)

The last decades have witnessed a variety of initiatives promoted by a diverse set of actors engaged in the protection of endangered Cultural Heritage (CH) and in stopping illicit trade, initiatives that have tried to bring solutions, remediation, methods and approaches to tackle looting and trafficking. 
NETCHER seeks to address the complex challenge of harmonising and bringing together these worthy, but often disconnected initiatives by using a participative approach that will result in the establishment of a structured network (defined as a Social Platform) drawing together a broad range of players such as international bodies, umbrella organizations, national governments, researchers, public policy makers, NGOs, as well as public and private foundations. 
One of the cores of the project is to establish an interoperability roadmap for existing and in development technologies, such as metadata analysis, satellite imagery, scraping packages, databases, digital scanning technologies and photogrammetry, neural networks.
Moreover, the project aims to foster cross-fertilization in technological innovations specifically aimed at fighting the illicit trade in CH.
In light of the significance of these uncoordinated efforts, the Platform will take charge of the systematizing and framing of all the emerging best practices in order to enhance and capitalize on the experiences of the partnership members at an international level for building a joint action plan with shared toolkits and a research and innovation roadmap.

Partners: Università Ca’ Foscari di Venezia (IIT), Deutsches Archäologisches Institut (DAI), CNRS, École Nationale Supérieure de la Police – ENSP, Capital High Tech, Interarts, Michael Culture Association (MCA)

Period: closed (2019/2021)

Funding body: European Commission - H2020

Websitehttps://netcher.eu

Twitter@Netcher5