## 1 Introduction  
In the European Union (EU), member states (MSs) have committed to achieving climate neutrality by 2050 [1]. Achieving this ambitious target requires addressing the energy and emissions impact of the building sector, which accounts for 40% of energy consumption and 36% of GHG emissions [2]. To this end, the EU has launched initiativessuch as the Renovation Wave [3] and the 2024 Energy Performance of Buildings Directive (EPBD) recast [2], aiming to accelerate the renovation of existing buildings across Europe. However, achieving these goals is not without challenges. Monitoring the progress of national strategies and ensuring their effectiveness requires robust, reliable, and comparable data across MSs. To address this need, the 2018 EPBD recast Directive [4] introduced a set of progress indicators to measure decarbonization advancements of the national building stocks. The list of indicators is extensive, covering a wide range of aspects. These include the general characteristics and energy performance of the national building stock, deep renovation status, the worst-performing segments, rented properties, energy poverty, the capacities in the building industry, actual energy savings, the broader benefits of building renovations, estimated energy savings, and reduced healthcare costs due to renovations, as well as policies and measures for mobilizing investments in building renovations, among other things [5]. Measuring these indicators is crucial for optimizing national renovation strategies based on data and demonstrating to the EU the effective use of European funds for decarbonizing the built environment. Despite the importance of these indicators, many remain difficult to measure [6]. This challenge stems from the following:  
1. Data fragmentation and lack of interoperability: Data are often collected at regional or municipal levels without a common format, hindering their combination to create larger-scale information [7]. 2. Disparate data structures: Data collected from various sources are in incompatible formats, lacking application programming interfaces (APIs) for direct access, complicating large-scale analysis. Some data are provided in non-machine-readable formats, impeding automation [8]. 3. Difficulty accessing data: Much of the information is not open and requires a request, blocking constant automation and updating [9]. 4. Labeling errors and missing data: Information is sometimes incomplete or contains errors that need filtering or correction [10]. As a result, these barriers significantly limit the ability of MSs to collect and utilize essential data for effective decision-making [11]. Emerging technologies such as georeferencing, big data analysis, and machine learning (ML) present an excellent opportunity to overcome these barriers and improve data collection [7]. For these technologies to deliver maximum utility, the data they rely on must:  
* Be standardized across all MSs to facilitate cooperation and comparability between European countries. * Be open to enable automation for constant updates. To address these needs, the EU has launched several directives and proposals such as the Infrastructure for Spatial Information in Europe (INSPIRE) Directive [12], Directive (EU) 2019/1024 [13] and the Data Act [14]. These directives aim to enhance access to and use of data within the EU, creating a common and secure interoperable space in the European Union. As discussed in [15], the use of open big data from public information sources within interoperable frameworks established by these directives allows for large-scale analysis and benefits from georeferencing and data cross-referencing to monitor decarbonization progress and building conditions, among other advantages. One promising approach to leverage these technologies is the use of urban building energy models (UBEMs). Historically, building energy models (BEMs) have been key tools for understanding a building's energy performance and proposing improvements for energy efficiency.