Conference Agenda

C-band scatterometers have been collecting radar backscatter data since 1991, providing valuable long-term records for environmental monitoring applications such as soil moisture and vegetation dynamics. However, differences in sensor calibration between missions introduce biases that compromise the continuity of these data records. This paper presents the practical implementation and adaptation of Reimer's (2014) rainforest-based inter-calibration approach for ESA's ERS satellites (ESCAT) and MetOp/ASCAT instruments. We implement the method as a modern, open-source Python framework and apply it to the newly complete ERS data record (including ERS-1 data not available in the original study). The resulting calibrated backscatter data record will enable improved long-term monitoring of land surface dynamics with reduced mission-to-mission variability in bias and slope response over incidence angle.

Improved availability and quality of high-resolution satellite imagery allow for reliable airplane detection. Yet, fine-grained classification, especially of commercial airliners, remains a formidable challenge. Besides common difficulties, such as varying image

artifacts and occlusions, the main challenge lies in the strong visual similarity between airliner families. This paper presents a geometry-aware classification that enhances oriented object detectors by integrating absolute measures and geometric features – fuselage length, wingspan, wing sweep angle, engine count, and fuselage width – in the form of priors into a Bayesian maximum a posteriori (MAP) estimation. The proposed pipeline is detector-agnostic by updating class posteriors without retraining the main detector. On the Gaofen Challenge dataset, it results in consistent improvements based on untuned baseline detectors, which outperform the top scores of the sophisticated fine-tuned models. An oracle experiment reveals the potential of the approach with an upper limit of the overall mean Average Precision of up to 0.96 and 0.98 for Gaofen and SuperView data, respectively. Furthermore, the impact of the employed geometric attributes is quantitatively evaluated.

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.

Semantically rich 3D city models play a vital role in a variety of applications, such as urban planning. Enhancing these models with currently unavailable attributes, such as building storey numbers, can unlock new opportunities to address pressing challenges, including sustainable urban development. In this work, we present an end-to-end pipeline for the automatic estimation of the number of storeys to semantically enrich 3D city models. We employ volunteered geographic information street-view imagery

from Mapillary, using a COCO-pretrained object detection model to identify windows in façade images as key visual indicators for inferring building storey counts. Our detection pipeline, based on the YOLOv3 architecture, estimates storey numbers using an ensemble of clustering methods including Gaussian Mixtures and DBSCAN and enables the automatic augmentation of CityGML-based 3D city models by filling in missing attributes. This enrichment supports advanced applications, such as assessing building-scale energy demand, evaluating vertical urban growth patterns or population density estimations. We validated the feasibility of our approach with unfiltered Mapillary and applied it to a district in the city of Heidelberg, Germany. The paper also includes a detailed discussion of learning process quality, integration workflows, and visualization of the enriched 3D city model. The developed code is available at: https://github.com/hcu-cml/citydb-buildingstoreys-ai.