Project description DEENESFRITPL Using computers and drones to preserve historical sites Machine learning, computer-vision and other digital technologies can help monitor and preserve World Heritage sites. This is particularly important in Europe, which boasts some 400 UNESCO sites – from the Acropolis in Athens to the historic city of Verona in Italy. The Marie Skłodowska-Curie Actions (MSCA) project MLCULT will develop techniques to diagnose damage of cultural heritage buildings. It will design a model trained from the database of earthquake-damaged cultural heritage buildings. Several typologies of damage indicators will be identified and quantified, due to weathering, moisture ingress, algae growth, and efflorescence. A prototype inspection system using drone-based real-time damage detection will be demonstrated, specifically for building damage. It can help in identifying structural anomalies that need urgent repair. Show the project objective Hide the project objective Objective Europe is home to about 400 UNESCO world heritage sites and has a growing tourism industry employing many people directly and indirectly. Hence, it is of concern to ensure the cultural heritage (CH) buildings are inspected properly and correct damage diagnosis is performed. Incorrect damage diagnosis will lead to loss of cultural value and may lead to the closing of the monument, thus affecting society in general and the livelihood of people associated with it. The proposed MLCULT project is motivated by the need to perform damage diagnosis of CH using image-based machine learning (ML) techniques, thus helping to preserve them. The popularity of ML approaches and deep learning algorithms has increased considerably over the last two decades. Computer-vision-based damage detection employing convolutional neural networks will be integrated with laser scan data, nondestructive testing, and other condition assessment data to provide a better estimate of existing areas of damage. The model will be trained from the database of earthquake-damaged CH collected by the host institutions UMinho and Polimi. Several typologies of damage indicators will be identified and quantified, due to weathering, moisture ingress, algae growth, and efflorescence. The project will be supervised by Prof. Lourenço at the University of Minho, Portugal, who is an international expert on CH, and Prof. Luigi Barazzetti at Politecnico di Milano, Italy whose has expertise in computer-vision, drone and image-based damage detection. Finally, a prototype inspection system (first of its kind in CH field) using drones-based real-time damage detection will be demonstrated, specifically for CH damage pathologies. The proposed method can help in identifying structural anomalies in CH that must be urgently repaired and thus can be used in high-quality civil infrastructure monitoring systems. This method would also enable fast screening of CH buildings after a disaster such as an earthquake. Fields of science engineering and technologycivil engineeringstructural engineeringstructural health monitoringnatural sciencesbiological sciencesmicrobiologyphycologyengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringroboticsautonomous robotsdronesnatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Programme(s) HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme Topic(s) HORIZON-MSCA-2021-PF-01-01 - MSCA Postdoctoral Fellowships 2021 Call for proposal HORIZON-MSCA-2021-PF-01 See other projects for this call Funding Scheme HORIZON-AG-UN - HORIZON Unit Grant Coordinator UNIVERSIDADE DO MINHO Net EU contribution € 172 618,56 Address Largo do paco 4704 553 Braga Portugal See on map Region Continente Norte Cávado Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00 Partners (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all POLITECNICO DI MILANO Italy Net EU contribution € 0,00 Address Piazza leonardo da vinci 32 20133 Milano See on map Region Nord-Ovest Lombardia Milano Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
