Author

S. Zlatanova

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Review Open access Jul 2026

Open Source 3D Cadastre Visualisation Pipeline

Abstract. Interpreting multi-storey property rights is difficult when information is scattered across 2D plans and text or locked inside desktop projects. We present a web-based pathway that communicates strata lots and common property consistently across levels in a standard browser. Aligned with the 3D Cadastral Survey Data Model and Exchange (3D CSDM) of Australia, we propose an open-source, web-first approach. The method couples a lightweight browser viewer (level/tenure filters, plan overlay, search, readable legend) with an explicit conversion step that standardises common GIS inputs into a fixed core JSON profile, with limited official CSDM-aligned JSON-LD hooks applied only to selected keys that have exact matches in the published vocabularies, while all remaining source attributes are preserved as standard JSON fields. Using a New South Wales case study, we evaluated the viewer against ISO 9241-11 criteria (effectiveness, efficiency). Across repeated trials (cache disabled/enabled), mean page-open times were 0.60 s (Chrome) and 1.48 s (Edge); interaction averaged 50–60 FPS; level filters applied in 40–55 ms; all five tasks succeeded. Practically, this delivers fast, consistent 3D communication of lots and common property without installs, lowering access barriers for agencies and owners while aligning with 3D CSDM’s web-first direction. Next, we will finalise viewer parity between Upload-and-View and the Reference Viewer and add a light in-viewer validation panel.

Pavan Sai Goud Goddu, S. Zlatanova, M. Kalantari · ISPRS Annals of the Photogra... · 0 citations

Open access Jul 2026

Query2Property: Semantic retrieval of IFC properties for natural language BIM queries

Abstract. IFC models store detailed building information, but their complex schema and deeply nested property sets make querying difficult for non-expert users and challenging for large language models (LLMs) to handle directly. Current LLM-based approaches are inefficient because prompts often include entire IFC schemas, many properties of which are irrelevant to the user’s query, leading to higher inference costs and potential errors. This paper presents Query2Property, a semantic retrieval system that maps natural language queries to the most relevant IFC properties. By embedding both property descriptions and user queries in a shared vector space, the system retrieves contextually relevant properties for dynamic and concise prompt construction in LLM-driven workflows. Evaluation on 55 representative BIM queries achieves a top-1 accuracy of 87.3% and top-3 accuracy of 100%, demonstrating effective alignment with user intent. Query2Property simplifies LLM-based workflows over BIM data, supporting semantic search and natural language exploration of complex building information.

Rabindra Lamsal, S. Zlatanova · ISPRS Annals of the Photogra... · 0 citations