Abstract. Air pollution is one of the most serious environmental problems in large cities, particularly in developing countries, where it has escalated into a major crisis that endangers public health. To better understand and manage this issue, Computational Fluid Dynamics (CFD) provides an effective tool for analyzing airflow and pollutant dispersion in complex urban settings. This study aims to examine how urban morphology affects ventilation efficiency and pollutant dispersion using three-dimensional (3D) modeling integrated with CFD simulation. The case study focuses on Mashhad, Iran’s second-largest metropolis, within a 300-meter radius of the Avini Air Quality Monitoring Station. First, a detailed 3D model of the study area was created using geospatial building data, including height and geometry. Then, the dispersion of PM₂.₅ and PM₁₀ particles was simulated in the CFD environment based on actual air quality data and prevailing wind conditions. The Residence Time Index (RTI) was calculated to represent the ability of the built environment to retain pollutants. Model validation was performed through comparison with results from a reference study, confirming good agreement and reliability. The findings indicate that areas with higher building density and lower wind velocity tend to trap more pollutants, leading to increased particle accumulation. Variations in building height and differences between PM₁₀ and PM₂.₅ behavior also influence dispersion patterns. Overall, integrating 3D urban modeling with CFD analysis provides a robust approach to understanding pollutant dynamics in urban areas and supports sustainable planning strategies aimed at improving air quality.
Rapid and reliable assessment of building damage after major earthquakes is essential for effective emergency response and recovery planning. This study formulates post-disaster building damage detection (BDD) as a binary image classification task (damaged vs. undamaged buildings) using multimodal satellite data and a unified ResNet-18 backbone to enable a controlled comparison of fusion strategies. The analysis focuses on the Mw 7.7 Myanmar earthquake of 28 March 2025 and integrates post-event COSMO-SkyMed Second Generation (CSG) dual-polarization (HH, HV) SAR imagery, Maxar optical data, OpenStreetMap (OSM) building footprints, and UNOSAT damage annotations. Three fusion paradigms are evaluated: Early Fusion (EF), Late Fusion (LF), and a novel Middle Fusion (MF) approach. The proposed MF framework introduces a Footprint-Guided Cross-Attention (FGCA) mechanism that uses building geometry as a spatial prior to guide feature-level interaction between SAR and optical representations. Five-fold cross-validation results show that MF consistently outperforms EF and LF, achieving higher precision, F1-score, and robustness across modality configurations. By jointly exploiting SAR structural sensitivity, optical detail, and footprint-based spatial context, the proposed Footprint-Guided Middle Fusion (FGMF) framework enables accurate and scalable building damage mapping from heterogeneous Earth Observation (EO) data.
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.
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. This includes 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, i.e. negative elevation differences in outward direction. 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.
Prompted by the rapid advancements in software and hardware, 3D building data for numerous different applications is nowadays often captured via mobile or kinematic laser scanning. However, in contrast to other laser scanning methods, there exist only a few approaches tailored for the planning of a kinematic laser scan survey, and none of them provides an optimality guarantee. Therefore, we propose a novel approach based on Mixed Integer Linear Programming (MILP) to find the optimal trajectory for such a survey. To obtain a high-quality point cloud, we account for scanner-related constraints that influence the quality of the resulting point cloud. Moreover, we enable the introduction of tie points to mitigate the effects of uncertainties in the position estimation that are propagated in the acquired data. In our problem formulation, we aim to find the best tour in a properly weighted graph. For this, we propose two different weight settings to either enable a purely length-based optimization or to increase the redundancy in the measurements by incorporating a Visibility Ratio Factor (VRF) into the objective function.To prove the applicability of our approach for offline panning, we apply our formulation to three different scenarios. In this context, the VRF-based weighting enables a significant speed-up of the solving process while resulting in only slightly prolonged routes. This approach paves the way for applying exact algorithms with an optimality guarantee in the planning process for efficient kinematic laser scanning surveys.
