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.

This research addresses the need for accurate updates to the seabed datasets in coastal areas under environmental and human pressure. It uses Google Earth Engine (GEE) to develop a cloud-based application for Satellite-Derived Bathymetry (SDB) of the Irish bays using Sentinel-2 and Landsat-8 imagery. For the validation, the OPW Pilot Coastal Monitoring and INFOMAR datasets were used. The research refines semi-empirical algorithms and introduces an Earth Engine App (EEA) using the JavaScript API specifically tailored for and non-specialist public use. The methodology employed included pre-selecting high-quality satellite images based on the higher R-squared and lower RMSE to ensure reliability and better performance. In the initial phase, 18 bays were assessed, and the results showed that five bays (Dublin, Dungarvan, Portrane, Rosses, and Tramore) performed better across the evaluated metrics. he development and use of this application support a wide range of marine applications, especially for capacity building, as part of the pilot research led by Maynooth University and Geological Survey Ireland (GSI).

Accurate estimation of methane fluxes across Arctic wetlands is essential for understanding carbon–climate feedbacks, yet remains difficult due to sparse ground measurements, strong spatial heterogeneity, and the coarse resolution of most existing bottom-up inventories. To address these limitations, we develop a high-resolution methane flux modeling framework that integrates multisensor Earth observation data with a geofoundational deep-learning approach. The study leverages 30 m Harmonized Landsat–Sentinel (HLS) imagery, together with environmental predictors from SMAP and ERA5, and daily eddy-covariance methane flux measurements from Arctic sites after 2015. Following data filtering and quality control, the dataset comprises more than 7,600 daily observations from 45 wetland sites across northern high latitudes. A hybrid model architecture is constructed by combining the Prithvi geospatial foundation model for HLS feature extraction with a lightweight feature-wise attention encoder processing 48 auxiliary environmental variables. Fused latent representations are used to predict daily methane flux at 30 m resolution. The model demonstrates strong performance on an independent test set, capturing key spatial and temporal patterns of methane emissions. By enabling fine-scale flux estimation far beyond the resolution of conventional 0.1°–0.5° inventories, the framework offers new opportunities for detailed Arctic methane monitoring and improved characterization of wetland-driven emissions.

To plan nature restoration of fluvial corridors on a national level an inventory of existing man-made levees is mandatory. We suggest an automatic method for a river-wise extraction of levees from a high resolution terrain model based on profiles perpendicular to the river axis. In this course we present a method to cover corridors with non overlapping profiles with a given maximum distance. Levee detection is based on a mathematical formulation of the protective function of levees. In an evaluation of 150 km river length distributed over nine different rivers in Austria the method detected 98% of manually extracted levees, and 68% of their length.

Temperatures in urban areas are rising due to the climate change. Together with increasing urbanization and densification reducing cooling green spaces in cities this leads to so called urban heat islands (UHI) with increased surface- and air-temperatures in urban areas relatively to the surrounding areas. Since high temperatures are the reason for many exceed deaths municipalities are forced to protect their citizens. Satellite earth observation allows to monitor the development of urban heat islands to warn inhabitants early from dangerous heat. An other important way is increasing the resilience of cities to heat waves. For this we developed a simple but efficient method for the simulation of urban surface- and air-temperatures from single very high resolution stereo satellite images. In this paper we present the improved workflow for the simulation of urban temperatures together with the calibration and validation. Further we compare the results to in-situ-measurements in the city of Memmingen in southern Germany, to LandSat thermal mapper imagery and existing works on urban heat islands. Additionally we show how modifying the digital twin e.g. by adding trees or water areas allow the simulation of different scenarios to support decision-makers on their path towards resilient cities.

Scandinavia is facing climate change, with mean temperatures projected to rise by 2-4°C. To prepare Swedish forests for this challenge, the Swedish tree breeding program aims to selects trees adapted to a range of biotic and abiotic conditions. Key variables in this selection process include spring phenology, damage, and overall tree vitality.

Traditionally, these data have been collected through manual field assessments, a resource-intensive approach that constrains both the number of trees that can be evaluated and the frequency of measurements. Remote sensing offers an alternative: high-resolution multispectral drone imaging enables the scoring of greater numbers of trees in less time, captures multiple data points across the growing season, and reduces the risk of human error through algorithmic measurement.

This project aims to develop methods suitable for integration into the Swedish tree breeding program by using multispectral drone imagery to assess spring phenology, shoot damage, and vitality in young Norway Spruce. Field campaigns were conducted during spring 2023 and 2024. Bud flush is modeled from spectral values of tree crowns, using manual assessments of a subset of trees as training data. To capture the full progression of bud flush at high temporal resolution, images were acquired before the vegetation season and up to twice weekly during the period of most rapid development. The same modeling framework is applied to assess damage and vitality.

Antarctic ice shelves regulate ice sheet mass balance through their "buttressing effect", with major implications for global sea level rise. This study focuses on the Nansen Ice Shelf in Victoria Land, East Antarctica, which exhibits complex topography and sensitivity to environmental changes. Previous research has primarily centered on its significant collapse event in 2016; however, systematic evolutionary patterns over longer timescales remain unclear. This study integrates multi-source remote sensing observations from 1948 to 2025 to systematically reconstruct changes in the Nansen Ice Shelf's geometric characteristics (crevasse width, area) and dynamic parameters (ice flow velocity). Findings reveal distinct activity differences between the northern and southern regions of the ice shelf, closely linked to their respective boundary conditions and structural features.

Flood mapping is one of the most important applications of Synthetic Aperture Radar (SAR) because it can monitor the earth's surface under all-weather, day-and-night conditions. While SAR intensity has been widely used for flood mapping, the potential and added value of interferometric coherence, especially its temporal behavior in different environments, remains mostly unexplored. In this study, we assess the potential and added value of interferometric coherence from Sentinel-1 time series for flood mapping in three contrasting regions: the urban area of Valencia (Spain), the arid region of Sistan and Baluchestan (Iran), and the agricultural area of Hannover (Germany). Our analysis of multi-temporal coherence shows that coherence provides clear flood indicators in arid regions through strong temporal decorrelation, but its performance is less reliable in vegetated and urban areas. In agricultural regions, pre-flood (baseline) coherence is inherently low due to vegetation phenology and temporal decorrelation, making any additional decrease due to flood inundation often indistinguishable. In urban areas, coherence generally remains stable, with only slight decreases observed in specific cases; therefore, the detectability of flooded areas using coherence-based approaches is limited in both agricultural and urban environments. In contrast, coherence in arid regions is high before flooding and drops significantly during flood events, making floods easy to detect in such regions. These findings demonstrate that, for flood mapping, interferometric coherence is a valuable but environment-dependent indicator, with the highest benefit seen in arid regions where intensity-based methods are limited.

Arctic sea ice thickness has been declining over recent decades due to climate change, making accurate prediction increasingly critical for environmental monitoring and climate modeling. Microwave remote sensing combined with machine learning has emerged as a promising approach for estimating sea ice thickness. This study investigates the prediction of lab-grown sea ice thickness, ranging from 27 to 47 cm, using time-series backscatter data collected from surface-based Ku- and C-band scatterometers in three polarizations (VV, HH, and HV). A Random Forest model was applied to the time series, incorporating Normalized Radar Cross-Section (NRCS) values and statistical features (mean and standard deviation) across various temporal variables (lead and lag times). The model achieved high prediction accuracy, with the lowest error recorded at RMSE = 0.03 cm. Feature importance analysis using the Permutation Importance method revealed that co-polarized C-band features (C-VV and C-HH) were the most influential in predicting sea ice thickness. These findings underscore the potential of integrating microwave remote sensing with Random Forest models to enhance sea ice thickness prediction and provide valuable insights for future research and real-time monitoring in Arctic regions.

Operational flood monitoring demands both accurate extent delineation and quantitative depth estimation, yet existing research addresses these objectives separately. This study presents an integrated SAR-to-depth framework combining state space model segmentation with DEM-based geometric depth estimation to deliver comprehensive flood intelligence from Sentinel-1 SAR imagery and digital elevation models. We propose CS-Mamba, a hierarchical U-Net architecture incorporating selective state space

mechanisms, achieving 79.79% mean IoU on 10 European flood events from the KuroSiwo benchmark while surpassing CNN baselines and outperforming RSMamba by 7.37 percentage points. Test performance exceeding validation confirms robust cross-event generalization to unseen disasters. Controlled experiments establish that deep learning predictions achieve sufficient accuracy for operational depth estimation, with CS-Mamba flood masks showing ±2% agreement with reference annotations across four global DEMs despite conservative extent delineation. This agreement enables integrated pipelines without manual annotation, while systematic DEM comparison identifies Copernicus and MERIT as optimal choices. The complete framework delivers three-class flood masks and pixel-wise depth maps at operational resolution, bridging the traditional gap between extent mapping and quantitative assessment for emergency response.

This contribution introduces TGSD-Net, a temporal variation-guided self-supervised network designed to improve despeckling of polarimetric SAR (PolSAR) imagery without the need for clean reference data. The method leverages consecutive multi-temporal observations to create pseudo training pairs and incorporates a lightweight temporal change detection prior, allowing the network to exploit temporal redundancy while remaining robust to land-cover variations. TGSD-Net further integrates auxiliary polarimetric decomposition features and a spatiotemporal information fusion module to enhance structural and scattering representations. The approach is tailored for multi-temporal SAR scenarios, where speckle, temporal variation, and heterogeneous land-cover types pose significant challenges. Experiments on real PolSAR datasets show that TGSD-Net achieves strong noise suppression while preserving edges, textures, and physical scattering properties. The results demonstrate the potential of self-supervised temporal learning to advance PolSAR image restoration and support downstream remote sensing applications.

Current Remote Sensing applications demand multi-source, multi-sensor data fusion. Multi-source, multi-sensor data fusion provides useful information integrated for quick and better interpretation, understanding and effective decision-making. Data fusion of Synthetic Aperture Radar (SAR) data of Earth Observation Satellite-04 (EOS-04) and Optical Multispectral (MX) data of Sentinel-2 are current topic of interest in this paper. SAR and Optical MX which includes active and passive remote sensing technologies belong to different mechanisms of wave interaction due to widely separated and non-overlapping regions of the electromagnetic spectrum. In this paper, a novel approach to the re-implementation of Wavelet, Brovey, Fast Intensity Hue Saturation (FIHS), Frequency filtering, and Pure pixel data fusion methods is presented. The presented novel approach emphasises modulation-based fusion technique with proper normalization and scaling of both the input datasets. Fusion results of presented fusion methods are evaluated visually as well as quantitatively with quality metrics. The quality metrics demonstrate the ability of the presented novel approach to fuse optical spectral information into SAR data effectively to generate improved high-resolution SAR-coloured fused products.