A key action of the proposal is “to promote and encourage the development of transnationally designed innovations (technical and non-technical innovation, including services) through pilot and joint actions contributing to facing societal and environmental challenges like energy efficiency or climate change”, through effective monitoring of forest sites located in the project areas. By effectively monitoring forest sites, the ARCA infrastructure aims to achieve the following outcomes: 1. Developing AI and machine learning algorithms for continuous monitoring of forest ecosystems and the formulation of climate adaptation scenarios. 2. Implementing a Decision Support System (DSS) for preventing natural hazards, enhancing resilience, and monitoring air quality. 3. Creating two mobile applications for real-time monitoring of tree and forest health, including fleet management for Forestry Corps. 4. Fostering cross-border collaboration on forest ecosystems, facilitating knowledge sharing among researchers and stakeholders. 5. Organizing joint training and dissemination activities in undergraduate schools to raise awareness among young people about the protection of ecosystems. In summary, ARCA represents a pioneering initiative to leverage technological advancements for the sustainable management and protection of forest ecosystems, contributing significantly to the broader goals of climate change mitigation and environmental conservation. The project outputs will be instrumental in shaping a resilient and adaptive future for our global community.

CMCC role
CMCC Foundation contributes primarily expertise on the development of the artificial intelligence platform to prevent climate change induced natural hazards and their impact on the forests, specifically on (i) AI algorithms for the study of forest natural hazards including tree and forest modeling; (ii) processing of near real time data acquired by wireless sensors networks, drones, video cameras and satellite images. Moreover, CMCC is responsible for the operational implementation, setup and run of the proposed digital platform. CMCC contributes to the design and implementation of the data lake, integrating different data sources and the adoption of AI and machine learning to monitor forest ecosystems and to forecast climate adaptation scenarios. Cmcc will also contribute to the development of the mobile Apps for real time monitoring of trees and forests health status, including the Decision Support System for disaster risk prevention and resilience. Finally, CMCC will be involved in educational training by setting up training and dissemination contests in undergraduate schools to raise awareness of young people on ecosystems protection. CMCC is the Lead Partner.

Activities
Work package 1 – IoT Infrastructure
The objective of WP1 is to design and develop the technological infrastructure used in the project. The infrastructure is based on a network of advanced wireless sensors installed on trees, including several drones equipped with LiDAR sensors and video cameras. This equipment will be set up in the forest sites in the cross-border area. Moreover, data acquired by the devices will be processed on the advanced computing infrastructure located at the CMCC Data Center in Lecce, Italy. The data will be used in WP2 to develop the AI algorithms for natural hazards prediction and fighting. Mobile devices will also be used for the development of two mobile Apps (WP3). WP1 leader is PP7 – MECOnet.

Work package 2 – AI algorithms for predicting and fighting natural hazards
WP2 aims to design and develop the AI tools for predicting and fighting natural hazards. The AI algorithms will be developed using the data collected by sensors, drones and video cameras located in the cross-border wooded areas. Different physical variables acquired through the IoT infrastructure developed in WP1, both at tree and forest levels, will be integrated and used for developing the AI tools. Climate variables will also be used for the definition of climate change mitigation measures able to prevent natural hazards, like wildfires, drought and windstorms. Machine learning methods will be developed for the implementation of sustainable forest management practices. WP2 leader is the LP – Fondazione CMCC.

Work package 3 – Big data platform & Decision Support System
WP3 aims to design and develop an operational big data platform for real-time collection, analysis and visualization of environmental data. A set of end-users services will be defined and implemented in the Decision Support System for the identification of the technologies required to implement the system functionalities and develop the AI based software components. Moreover, historical data from the wooded areas will be used for training the Machine Learning algorithms and identifying the necessary climate change mitigation scenarios. The system will be developed with customized visualization and real-time analysis capabilities, including features like access to data from video cameras/drones. WP3 will address the design and implementation of the 2 mobile Apps that will be used by Forestry Corps to monitor the health status of the trees and forests while allowing efficient fleet management. WP3 leader is PP9 – CAD Solutions.

Expected results
• Deployment of an IoT infrastructure consisting of a network of a wireless sensors, and drones, video cameras and mobile devices for real time monitoring operations. The IoT infrastructure will be deployed in Puglia (IT), Dubrovnik-Neretva (HR), Istria (HR), Municipality of Thermi (GR), Kitka area (MDK), Podgorica region (ME), Korce region (AL).
• Design and deployment of a cross-border operational platform (Big data platform and Decision Support System) based on AI technologies for monitoring the wooded areas and predicting and fighting natural hazards .
• AI models for predicting and fighting natural hazards. Historical data acquired by the IoT infrastructure deployed in the cross border wooded areas will be used for training the Machine Learning algorithms that will be developed during the project
• Educational training
• Mitigation strategies and action plans jointly developed

Partners
DHITECH – High-Tech Technological District Scarl – Italia (IT)
UNIDU – UNIVERSITY OF DUBROVNIK – Hrvatska (HR)
IRENA – Istrian Regional Energy Agency Ltd. – Hrvatska (HR)
ANATOLIKI SA – ORGANISATION FOR LOCAL DEVELOPMENT – Elláda (EL)
MKDS – PE “National Forests” – Republic of North Macedonia (MK)
MECONET – Mediterranean Excellence in Computing and Ontology – Crna Gora (ME)
UBT (University for Business and Technology) – Center for Modelling and Simulations – Tirana (AL)
CAD Solutions – Serbia (RS)