BIM4SmartRegions – Digital Twins for Buildings and Cities – presented by LOC and Runder Tisch GIS e.V.

BIM4SmartRegions – Digital Twins for Buildings and Cities
presented by LOC and Runder Tisch GIS e.V.

Esri Deutschland GmbH: Geodaten und KI für räumliche Intelligenz – Analyse, Vernetzung und Automatisierung von Prozessen im gesamten Infrastruktur- und Baulebenszyklus.

Geodaten und Künstliche Intelligenz ermöglichen räumliche Intelligenz entlang des gesamten Infrastruktur- und Baulebenszyklus. Der Beitrag zeigt, wie GIS als „System of Record“ Daten aus Planung, Bau und Betrieb konsistent zusammenführt und mit BIM sowie KI vernetzt. Im Fokus stehen die intelligente Standortwahl und Resilienzbewertung, die effiziente Datenerfassung und Bestandsanalyse sowie die Automatisierung durchgängiger Workflows. Durch die Integration heterogener Datenquellen und deren KI-gestützte Auswertung entstehen fundierte Entscheidungsgrundlagen, die Prozesse beschleunigen, Risiken minimieren und nachhaltige, resiliente Infrastrukturen ermöglichen.

Jaud IT GmbH: Applying MVD Methodology to CityGML Exchanges: Energy Analysis Test Case 

In openBIM, model exchanges run through a loop of requirements, checks and issues: Model View Definitions (MVD) specify what an IFC model must contain, automated checkers verify it, and findings come back as BCF issues. City models in CityGML have no equivalent workflow, even though use cases such as energy analysis depend on data being complete and fit for purpose. This talk presents a feasibility test of transplanting the openBIM MVD methodology to CityGML exchanges, using energy analysis as the test case. Requirements from this use case are expressed as machine-readable rules and CityGML building models are checked against them; failures are reported back to authors as BCF issues with 3D viewpoints, closing the loop. We show what carries over directly from IFC, where the existing standards need adaptation, and what this means for quality-assured, requirement-driven exchange of semantic city models.

SiloLink/Technical University of Munich: From clash overload to agent-based issue management 

BIM coordination teams spend countless hours reviewing clash reports, yet most detected clashes are repetitive, low-priority, or irrelevant to real construction risks. As projects grow, issue management quickly becomes out of control: comments are scattered across tools, responsibilities are unclear, and critical problems are easily delayed or missed.
SiloLink addresses this challenge through AI clash detection and agent-based issue management. Our platform helps teams detect, group, filter, and prioritize clashes based on model context and project rules. Instead of manually checking thousands of clashes, BIM managers can focus on a small number of meaningful issues that truly require coordination. AI agents further support the full issue workflow by creating issue summaries, assigning responsibilities, tracking status, and preparing clear coordination reports. This reduces review time, improves coordination quality, and helps design and construction teams resolve risks.