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Sustainable Historic Environments hoListic reconstruction through Technological Enhancement and community based Resilience

Periodic Reporting for period 2 - SHELTER (Sustainable Historic Environments hoListic reconstruction through Technological Enhancement and community based Resilience)

Reporting period: 2020-12-01 to 2022-05-31

Historic Areas are one of our most valuable achievement as a society due to their cultural and natural values and their impact on peoples well-being. But current disaster statistics and studies usually do not consider heritage as a sensitive and valuable element. The higher vulnerability of materials and structures, difficult accessibility and density of the urban fabric makes the improvement of the resilience of historic areas a challenge. The overall objective of SHELTER is to develop data-driven and community-based approaches, tools and solutions to improve resilience in the historic areas. SHELTER proposes a change of paradigm, where the disturbance is not an unexpected event anymore, instead it is foreseen, accepted and addressed for transformation. SHELTER aims to generate and demonstrate a way to improve resilience that is the result of the interplay of two processes collaborating to feature individual solutions for each case: a data-driven platform that supports the diagnosis, decision making, implementation and monitoring based on existing knowledge and heterogeneous data, and an Open Lab approach that provides a continuous framework for local knowledge extraction, citizen´s engagement, co-creation, capacity building and innovation. SHELTER is organized to develop and demonstrate a highly adaptable and replicable systemic approach based on the identification and activation of the inherent resilient characteristics of historic environments (heritage-led resilience) and the preservation of its values (conservation-friendly resilience). SHELTER will adopt a case studies based approach with two objectives: to generate the required knowledge regarding the impact of different impacts in diverse typologies of Natural and Cultural Heritage and to co-validate the suitability, adaptability and replicability of the SHELTER framework, methodologies and ICT tools to different contexts. The five case studies (Ravenna-Italy, Seferihizar-Turkey, Dordrecht-Netherlands, Natural Park of Baixa Limia-Serra Do Xurés – Spain and Sava River Basin) have been selected considering their natural and cultural value and diversity, their especial exposure to diverse hazards, their geographical representability and climate conditions and their scale and typology.
In the first period of SHELTER, the general framework and the knowledge and data management base were established. This allowed the successful finalisation in this second period of the generation of the non-existing knowledge for assessment and monitoring of risk and resilience in Historic Areas and development of tools and technologies for all Disaster Risk Management phases. In this period, also the definition of the actionable plans within the collaborative planning of the low carbon resilience in Historic Areas has started with the development of early-recovery roadmaps for post-disaster phase.

In this period the design and implementation of the common components of the data-driven platform that supports the diagnosis, decision making, implementation and monitoring based on existing knowledge and heterogeneous data have been successfully carried out and the Resilience Dashboard and the tools for strategic decision-making are in an advanced stage. The five Open Labs established across Europe have continued their co-creation activities regardless of the challenging conditions, providing continuous inputs for co-creation, validation, and replication of all the results. The Open Labs have been supported theoretically and methodologically by the establishment of the framework for the community approach and the tools for resilience financing and adaptive governance have been almost finished. Transversal actions of exploitation, dissemination and communication have been performed to ensure the expected GLOCAL impact of project results.
The foundation of the SHELTER framework has been defined, setting natural, tangible and intangible heritage as a central piece of the complex interactions between the resilience and the social, technical, ecological and economic forces. The definition of a multiscale data model will allow the spatial identification and analysis of the dynamics among these components and the designing of innovative strategies for resilience enhancement. The challenge lies in how to transform a large amount of existing information in knowledge. SHELTER has classified and evaluated data sources according to their compatibility with standards and the use of metadata (INSPIRE). Besides, an in-cloud Big Data storage solution is proposed with a data catalog for the efficient search of stored information and the multiscale data model is defined based on international standards, that facilitate the compatibility and durability of the proposed solution. SHELTER has developed a method for collaborative and multiscale characterisation of CH assets. The definition of the impact of natural hazards, the establishment of the constraints for the strategies applicability and identification of the assets contributing to inherent resilience is ongoing. A cross-scale, multidimensional assessment methodology based on indicators has been established. The innovation of this approach lies in linking physical vulnerability and risk management concepts and approaches with a more general and multidimensional resilience approach focused on the singularities of historic environments. The most common approach for rapid damage assessment in general and flood delineation is based on the use satellite imagery combined with supervised machine learning classifiers. The proposal in SHELTER is based on applying a traditional Machine Learning algorithm (Random Forest) and an adapted Deep Learning model (U-Net). Landsat data is mostly used for delineating burned areas, and the approaches combine expertly defined thresholds with classic machine learning algorithms. SHELTER exploits a subset of deep neural networks - convolutional neural networks (CNNs) - to find burned regions. Social networks and the analysis of the information generated allow obtaining useful information on natural disasters. Existing solutions are usually specific and tend not to generalize well and perform poorly when applied to data coming from different sources. SHELTER aims to study the performance of recent deep learning models for automatic analysis of images from multiple sources and classification applicable to multiple languages. SHELTER is implementing an innovative Open Labs approach based on a more horizontal, cross-scale resilience model through adaptive governance and collaborative thinking that places stakeholders in the centre of the process. The designed process allows the Open Labs the co-identification of problems and priorities, co-creation, test and pilot of solutions, co-monitoring and awareness-raising.
SHELTER ummersite
SHELTER framework
Crowdsourcing solutions for citizens engagement in preparedness and response
SHELTER portfolio of solutions
SHELTER Dashboard
SHELTER cultural and natural macrocategories