The GAIA project aims to create a Two-Sided Digital Twin (2SDT) of the local context, designed to support both local decision-makers and emerging communities by enabling the simulation of interventions in advance to reduce risks and generate social value. The 2SDT is a cyber-physical-social system composed of a digital component—made up of models, algorithms, software, and data—and a collaborative component, aimed at facilitating access to information and the formation of interdisciplinary groups capable of devising innovative solutions. This digital twin is capable of sensing the physical and digital environment, updating its virtual counterpart, planning and simulating changes, and interacting with the urban socio-economic system. To do so, it integrates artificial intelligence technologies across all levels of the technology stack: in data management for the validation and generation of synthetic data, in data analytics with predictive and prescriptive models and MLaaS environments, and in collaborative applications with machine learning tools for profiling, recommendation, and community aggregation. In this context, EHT contributes to the design, development, and testing of the Emerging Communities Sharing Lab, aimed at accelerating the adoption of emerging technologies by communities of interest and fostering the creation of innovative applications. EHT’s activities include enhancing data collection networks, defining PoCs and engaging experts, researching models to represent the physical world and communities, designing the Lab’s explore, build, and relate modules, developing serious games, focus groups, and thematic appathons, disseminating results, and conducting sustainability analyses with a corresponding exploitation plan. The target markets for these activities are Data Science, Big Data and Analytics, IoT, and Digital Twin platforms. The assets developed include the strengthening of monitoring networks (e.g., rain gauges, video surveillance, water monitoring), the Lab’s physical and social model, a flexible Data Catalogue, ML-based profiling and recommendation systems, and prescriptive analytics systems. The expertise acquired by EHT includes the design and management of participatory activities (serious games, focus groups, appathons), the creation of Data Catalogs, and the development of ML systems for profiling, recommendation, and prescriptive analysis.