Dimitra Andritsou

This chapter presents a low-cost, law-compliant methodology for constructing high-fidelity 3D digital twins (DTs) to facilitate data-driven decision-making in urban environments. The economic and technical barriers caused by the “Building Information Modeling – Geographic Information Systems (BIM-GIS)” silo are addressed by utilizing open and available data from sources such as Google Earth Pro, Streetview, and authoritative open data from National Cadastral Portals. This allows for the integration of 3D data into high-fidelity DT by using game engines, such as UE5, and high-performance, real-time visualization within a global geospatial context. Furthermore, with the integration of the Plegma dataset, which features high-frequency energy sampling and appliance-level disaggregation, the developed DT can serve as a run-time engine for a positive energy district (PED). Fundamental to our design principle is strict adherence to established laws and regulations. By using the methodology developed in the CadaSPACE platform, we visualize 3D volumetric property rights while remaining compliant with General Data Protection Regulation (GDPR), local building codes, and national legal frameworks. Normally, BIMs are created by utilizing 2D floor and subdivision plans, as is the case with developed national geospatial services. Our proposal introduces a cost-effective and feasible DT approach by utilizing a fit-for-purpose methodology in BIM creation. This approach enhances the capabilities of underdeveloped national geospatial infrastructures, ultimately aiming to facilitate access to sophisticated urban simulation and cadastral guidelines through data-driven urban decision-making.

The objective of this paper is to develop an adaptable and affordable technical tool for managing small urban areas. It demonstrates a low-cost, reliable, and fast method for integrating BIMs, IFC data, and GIS to support fit-for-purpose, crowdsourcing, and participatory applications through an online dashboard. Open data and existing geoportals are used to create the necessary geospatial infrastructure. Geometric information such as building area size and volume is combined with other data from multiple sources such as market values and CO₂ emissions, which can be updated dynamically through real-time interactions. A case study is presented for a small urban neighborhood in Athens.

This paper is part of an on-going research study on developing a low-cost and approachable methodology for f-f-p, transparent and future inclusive 3D land use management for an urban neighbourhood by constructing a homogenous, open, cloud-based and free-to-use virtual BIM-GIS geospatial infrastructure. The proposed platform is structured for registering, classifying and visualizing volumetric land use prisms, distributing conceptual and geometric information, conducting statistical analyses and data exchange aiming for optimal decision making, urban neighbourhood management and land use tracking. The proposal aspires to aid the successful and timely achievement of the UN Sustainable Agenda 2030, in particular SDGs 1,9 and 11. The methodology includes: 1) the creation of approximate BIMs by utilizing open and available data and platforms, 2) modelling of land uses as 3D volumetric prisms, 3) registering semantic and geometrical land use information and 4) crafting statistical graphs, emphasising on data compilation and BIM-GIS standards implementation.

This paper is part of an ongoing research study on developing a methodology for the low-cost creation of the Digital Twin of an urban neighborhood for sustainable, transparent, and participatory urban management to enable low-and middle-income economies to meet the UN Sustainable Development Agenda 2030 successfully and timely, in particular SDGs 1, 7, 9, 10, 11, and 12. The methodology includes: (1) the creation of a geospatial data infrastructure by merging Building Information Models (BIMs) and 3D cadastral data that may support a number of applications (i.e., visualization of 3D volumetric legal entities), and (2) the use of Artificial Intelligence (AI) platforms, Machine Learning (ML), and sensors that are interconnected with devices located in the various property units to test and predict future scenarios and support energy efficiency applications. Two modular platforms are created: (1) to interact with the AI sensors for building tracking and management purposes (i.e., alarms, security cameras, control panels, etc.) and (2) to analyze the energy consumption data such as future predictions, anomaly detection, and scenario making. A case study is made for an urban neighborhood in Athens. It includes a dynamic weather simulation and visualization of different seasons and times of day in combination with internal energy consumption.