Conference Agenda

To meet the growing demand for 3D digital map applications and to better understand the multi-level spatial structure of cities, some cities have implemented citywide 3D digital map programs. In 3D digital map production, vehicle-mounted mobile surveying is a key component. Drawing with a practical project, this paper proposes a technical scheme for road data acquisition and processing based on the SSW VMMS (Vehicle-mounted Mobile Mapping System). Through integrated processing steps, including combined navigation solution, point cloud correction, image coordinate calculation, image deblurring, point cloud coloring, point cloud denoising, and Orbit GT data preparation, the rapid production of colored point cloud data with georeferenced coordinates, 360° panoramic image data, and individual image data is achieved. A technical scheme suitable for 3D digital map production along urban roads was developed and validated. The results produced by this scheme have passed inspection and acceptance, and were released to the public free of charge as the first batch of visualized 3D map data on the Common Spatial Data Infrastructure Portal (portal.csdi.gov.hk), receiving widespread attention and positive recognition from various sectors of society. This scheme not only promotes the broader application of the SSW VMMS but also provides effective reference for similar urban vehicle-mounted mobile mapping projects.

We present an automatic algorithm for graph-based road network extraction from remote sensing images. While existing works mostly focus on improving accuracy, we address the problem of the geometric quality of the output graphs. The state-of-the-art methods largely overlook this aspect by generating graphs without strong geometric guarantees, regularity preservation and low-complexity, which, ultimately, reduces their impact in many application scenarios. Our algorithm relies upon foundation models that analyze road networks with pixel-based representations, as well as geometric algorithms and data structures in charge of connecting geometric primitives into planar graphs. This hybrid strategy allows us to strongly enforce the geometric quality of the output graphs while bringing a high level of generalization. We show the potential of our algorithm and its advantages over existing methods on two datasets commonly-used in the field using both the conventional accuracy metrics and new metrics introduced to measure the geometric quality of the output graphs.

High-resolution monitoring of road infrastructure is essential for the early detection of geomorphological instabilities such as landslides and erosion. This study evaluates the performance of handled MMS under different vehicle-mounted configurations: a 2-meter survey pole versus a suction-cup mount, and varying acquisition speeds (10 and 20 km/h). Furthermore, a GNSS-denied scenario was simulated to test the robustness of SLAM-based processing. Initial results revealed significant geometric discrepancies (double-points artifacts and drift), particularly in the SLAM-only and high-speed datasets. To address this, an automated segment-based refinement workflow was developed using a ICP algorithm. The refinement successfully reduced the standard deviation to the level of the point cloud´s mean point spacing (5 cm). Comparative multitemporal analysis against UAV-LiDAR reference data confirms that the proposed refinement renders even SLAM-processed data viable for detecting centimetric terrain displacements. The findings demonstrate that while suction-cup mounting at 10 km/h is optimal, algorithmic refinement allows for reliable road slopes monitoring and change detection across all tested configurations

Underground utilities such as water pipelines and sewers are critical for urban systems, yet their management is challenging due to limited accessibility and uncertain positional data. Current inspection practices rely on robotic crawlers equipped with CCTV cameras or man-entry inspections, enabling visual documentation of structural conditions but lacking accurate georeferencing of internal points. Advanced solutions relying on panoramic imaging and IMUs offer partial 3D measurements and trajectory estimation, though accuracy remains limited by drift and environmental variability.

This study investigates the feasibility of multi-camera photogrammetry for mapping pipelines and confined underground environments and improving positional accuracy. Preliminary experiments were conducted using the Atom-Ant3D system on two test sets: (i) five pipelines of varying materials (concrete, PVC, fiberglass) and diameters (60–110 cm); and (ii) a 1.3 km water-distribution tunnel (~2 m diameter) prepared with 28 fixed targets measured via total station for accuracy evaluation. Data were acquired using robotic and handheld configurations and processed through two workflows: Structure-from-Motion (SfM) and multi-view V-SLAM.

Accuracy assessment focused on the tunnel test, comparing unconstrained and constrained trajectories against a reference solution. Results provide insights into the potential of photogrammetric approaches for precise pipeline reconstruction and georeferencing, supporting improved subsurface utility management and planning.

3D multi-view, multi-object tracking (3D MV-MOT) makes use of multiple cameras to reduce the number of missed detections and to mitigate occlusions. Most current 3D MV-MOT methods suffer from information loss when associating 3D locations with 2D image features via a 3D-to-2D projection, as they use a discrete grid in 3D and sample image features only at the projected centers of each grid cell. Thus, all other feature information is lost. An additional information loss commonly arises during cross-view aggregation when applying max or average pooling: these methods either overemphasize a single view or treat conflicting views, that depict different entities, e.g., due to occlusions, equally.

In this work, we introduce two novel modules for 3D MV-MOT, employed to pedestrian tracking, that target these limitations:

(i) VoxROI aggregates all image features that fall within the bounding box around a voxel's projection into each respective image, instead of only sampling features at the projected voxel center.

(ii) SimFuse aggregates per-view voxel features into one coherent feature representation per voxel, using similarity weights computed from re-identification (Re-ID) features.

Subsequently, they are used to measure cross-view identity similarity. Views with higher Re-ID feature similarity receive larger weights, while inconsistent views are suppressed.

Experimental results on the WildTrack dataset confirm our method's effectiveness for multi-view pedestrian detection and tracking, reaching, and in particular in cross-view scenarios improving, the general state-of-the-art. The approach maintains strong performance across different camera configurations, demonstrating its generalization capability when training and testing on different camera setups.

With the rapid growth of urban low-altitude applications, traditional airspace management approaches based on simple altitude limits and static no-fly zones can no longer meet the demands of high-density and highly dynamic operations. To address this issue, this study proposes a fine-grained gridding method for urban low-altitude airspace with dynamic event response and vertical flight corridor constraints. First, a unified three-dimensional grid model is constructed on the basis of an urban 3D digital twin platform, and the grid scale and update cycle are determined by jointly considering clearance requirements and safety separation. Second, a method for injecting static and dynamic attributes is established to achieve the unified representation and continuous updating of terrain, buildings, no-fly and restricted zones, wind fields, temporary restrictions, as well as occupancy and release information within the grid. Third, fixed-geometry and dynamically open vertical flight corridors are designed to support controlled cross-layer flight transitions and reduce the risk of vertical conflict propagation. An experimental system is developed using a typical high-density urban area in Yuehai Subdistrict, Nanshan District, Shenzhen, as the case study. The results show that the proposed method can achieve stable spatial discretization, accurate attribute loading and updating, and clear organization of cross-layer flight. The proposed method provides a unified technical framework for low-altitude airspace representation, state management, and operational governance in complex urban environments.

Automatic extraction of vectorized roof structures above LOD2.0 remains challenging due to their geometric complexity and the presence of small and occluded elements over the roofs. Detecting fine-scale roof objects such as chimneys and dormer windows in very high resolution aerial imagery is still an active research topic. This study presents a workflow for automated detection and vectorization roof structures at city scale using Line Segment Detection (LSD) networks. Compared to model-based building reconstruction approaches, LSD networks do not rely on pre-defined roof typologies and are able to extract complex roof structures and small objects over the building roofs. For this purpose, a dataset comprising approximately 139,000 buildings with LOD2.2 roof structures and more than 2.2 million roof segments is generated using 8 cm GSD aerial imagery. An automated end-to-end workflow is developed, trained and tested from the available data. Experimental results indicate that roof structures suitable for LOD2.2 3D roofs can be extracted and vectorized with high accuracy, achieving 58.4% msAP and 73.1% mAPJ with ULSD network. Robustness is further assessed by visual inspection in areas affected by roof-blocking objects such as trees and cast shadows.

Topographic maps provide structured, polygonal representations of the Earth’s surface, delineating land-cover classes such as buildings, roads, water bodies, and vegetation.

They form the foundation of national geospatial data infrastructures and support a wide range of applications, including urban planning, environmental monitoring, and cadastral management. However, the production and maintenance of such large-scale topographic maps still rely heavily on manual photo-interpretation and vector editing.

While such human-in-the-loop workflows ensure geometric accuracy, they are labor-intensive, costly, and non-reproducible, limiting scalability and update frequency. However, most existing polygonal outline extraction methods are restricted to single-class, which typically leads to overlaps, gaps, and inconsistent shared boundaries when extended to multi-class mapping. Moreover, few studies have demonstrated nationwide implementation or validation, leaving the scalability and generalization of current methods largely unexplored. To address these challenges, this study develops a fully automated framework for large-scale topographic mapping directly from high-resolution aerial imagery. The framework aims to produce seamless, multi-class topographic maps in a single run that remain topologically consistent across diverse urban and rural regions in the Netherlands and beyond.

Instance segmentation of individual trees from UAV-derived orthomosaics and DSMs remains challenging in dense planted forests in Japan because SfM-derived DSMs often have blurred crown boundaries and unstable quality. We propose a PFFT-based method that encodes the local DSM shape around treetop candidates and integrates it into Mask2Former to suppress unreliable candidates and improve crown separation.

Experiments on Abies sachalinensis plantation (Todo fir) data from two sites in Hokkaido showed that the method improved mAP75 from 52.18% to 55.47% and F1 at a confidence threshold of 0.5 from 89.86% to 92.08%, while reducing false positives by 41% without increasing false negatives. The results indicate that treetop-centered local shape cues are useful for instance segmentation in densely planted forests.

This study presents an integrated framework combining YOLOv9e-Seg and photogrammetric geometric analysis for detecting road-safety assets and assessing their condition using UAV imagery. Traffic cones and tubular markers, which define construction-zone boundaries, are difficult to detect due to their small size in high-resolution images. To address this, a crop-tiling strategy (512×512 pixels) was applied to enhance the representation of small objects. Polygon-based labeling was used to preserve fine object geometry, and YOLOv9e-Seg was trained to output instance masks and polygon coordinates. During testing, tiled predictions were restored to the global coordinate frame, and duplicate detections were removed by retaining only the highest-confidence results. Geometric analysis utilized segmentation-derived polygons to compute centroids and principal axes, distinguishing intact and damaged tubular markers through vector angle difference analysis. For traffic cones, convex hulls constructed from centroid positions accurately delineated construction-zone boundaries. The proposed approach achieved the highest F1 score at a 512-pixel tile size, improving detection and segmentation of small, slender objects. These results demonstrate that the framework goes beyond basic detection and segmentation by enabling quantitative geometric interpretation and reliable construction-zone reconstruction from UAV data.

Satellite imagery enables large-scale remote sensing applications by providing frequent and large-scale coverage. However, its limited spatial resolution often restricts the use of satellite images in tasks that require detailed, fine-scale information. In contrast, aerial images offer a much higher spatial resolution, allowing the extraction of fine-grained features, but typically cover smaller, more localized areas. In this work, we investigate whether super-resolution (SR) methods can bridge the gap between aerial and high-resolution satellite imagery, enabling a label-free model transfer without additional manual annotations. The idea is to enhance the spatial resolution of high-resolution satellite images, allowing models trained on aerial data to be directly applied to satellite images. Towards this goal, a state-of-the-art SR algorithm is used to upscale three high-resolution satellite images, matching the resolution of the aerial training data. Then, a segmentation network trained on an aerial image dataset is applied to segment roads and parking areas in the super-resolved satellite images. The approach is evaluated on an annotated dataset and compared to the results in the original satellite images. Additionally, we investigate its performance on a low-resolution aerial image. Our results demonstrate that SR facilitates the utilization of models trained on aerial image datasets for large-scale satellite applications without requiring new labels.

This study tackles multilingual and explicit-implicit gaps in Visual Grounding (VG) for UAV imagery, focusing on real-world UAV needs (e.g., disaster response) that require implicit reference understanding. It evaluates Qwen2.5-VL-7B’s cross-linguistic robustness via Acc@0.5% across nine languages (Chinese, English, Japanese, Russian, Korean, German, French, Spanish, Portuguese). Key results: Explicit VG (using visual attributes) outperforms implicit VG (needing context/common sense) universally. East Asian languages lead in both tasks; Indo-European languages (e.g., Portuguese, 48.63% implicit accuracy drop) lag. Attention analysis shows the model better aligns with East Asian linguistic structures. This work informs LVLM optimization for multilingual UAV applications, guiding future cross-model comparisons.

Aerosol size information, typically represented by fine- and coarse-mode aerosol optical depth (fAOD and cAOD), is crucial for understanding anthropogenic emissions and radiative effects. However, satellite-based retrievals suffer from limited labeled data and high uncertainty over land. To address these challenges, we developed a novel deep pretraining framework capable of mining latent representations from unlabeled satellite pixels, thereby enhancing the accuracy and generalization of aerosol size information retrieval. The framework leverages a self-supervised pretraining stage to capture intrinsic spatiotemporal correlations in multispectral satellite data and transfers these latent features to a supervised fine-tuning model. Using MODIS data combined with AERONET observations, our pretrained model achieved a 10% improvement in correlation and a 15% enhancement in regions without ground observations compared to conventional deep-learning models. The retrieved global fAOD from 2001–2020 reveals a significant decreasing trend (−1.39 × 10⁻³ yr⁻¹), with regional differences—most notably, a threefold stronger decline over China than the global average. These results demonstrate that deep pretraining can effectively exploit unlabeled satellite information, bridging the gap between sparse ground networks and dense global observations, and offering a transformative approach for large-scale aerosol characterization and climate studies.

Atmospheric aerosols are a key factor influencing the Earth's radiation balance and climate change, and the accuracy of their retrieval is crucial for environmental monitoring and climate research. FY-4A AGRI, with its high-frequency observation capability, can provide aerosol data at high temporal resolution. Combined with deep learning technology, it enables efficient monitoring of dynamic aerosol variations. This study develops a retrieval algorithm for aerosol optical and microphysical parameters based on the Transformer deep learning model, specifically designed for the FY-4A geostationary satellite. The algorithm achieves multi-parameter collaborative retrieval of aerosol optical depth (AOD), fine/coarse-mode aerosol optical depth (FAOD/CAOD), and single scattering albedo (SSA). This research overcomes the reliance on prior assumptions inherent in traditional physical retrieval methods. By integrating multi-band spectral features, geometric observation parameters, and data from 104 AERONET sites, it significantly enhances retrieval accuracy under the complex surface conditions of East Asia. Experimental results demonstrate high accuracy in validation against AERONET sites, with correlation coefficients of R=0.915 for AOD, R=0.897 for FAOD, R=0.851 for CAOD, and R=0.536 for SSA. Comparative validation of various aerosol product spatial distributions highlights the advantages of the proposed algorithm in capturing aerosol diurnal variations (such as haze dissipation processes) and extreme events (e.g., dust storms and biomass burning). This study provides a new technical approach for regional air quality monitoring and climate effect assessment, advancing the application of China’s geostationary meteorological satellites in aerosol monitoring.

Heavy metal pollution presents a formidable challenge to global ecosystems, threatening biodiversity, soil and water quality, and human health. The atmosphere serves as both a source and long-range conveyor of bioaerosols, complex particles that include bacteria, fungal spores, and dust-bound heavy metals, profoundly influencing biosphere health and ecosystem function. In this study, we investigate atmosphere-biosphere interactions in Pin Valley National Park, a cold desert ecosystem in the Western Himalayas, by analyzing how bioaerosol-mediated deposition of heavy metals shapes vegetation stress and phytoremediation dynamics. Integrating field spectroscopy, in-situ chemical analysis (ICP-MS), and multi-temporal satellite data, we mapped heavy metal hotspots (Pb, Cd, Ni, Cr) and linked them to shifts in vegetation health and thermal indices. We observed significant spatial overlap between elevated metal concentrations likely introduced via long-range atmospheric transport and suppressed vegetation indices. Phytoremediator species such as Brassica juncea and Populus exhibited strong metal uptake, revealing natural biospheric buffering capacity against airborne contaminants. Additionally, iron oxide and hydrothermal indices indicated that soil mineral conditions, modulated by deposition, may influence microbial and root zone dynamics. This multidisciplinary assessment underscores the role of the atmosphere not merely as a depositor but as a dynamic bioreactor influencing terrestrial microbiomes and plant stress responses. By offering a scalable, remote sensing–assisted framework for monitoring ecosystem health and contaminant transport, our work directly supports SDG 13 by identifying atmospheric pathways of pollutant stress under warming trends, contributes to SDG 15 by protecting fragile alpine ecosystems through phytoremediation, and aligns with SDG 17 as an interdisciplinary approach.

The relationship between aerosol optical depth (AOD) and surface PM2.5 concentrations remains a significant difficulty in remote sensing-based air quality assessments due to meteorological conditions and aerosol vertical structure. This relationship is investigated using daily observations from 2021 to 2024 in Sherbrooke, Quebec, Canada.

Ground-based AERONET AOD500 and satellite-based MAIAC AOD at 550 nm are analyzed separately, together with surface PM2.5 measurements from a local PurpleAir sensor. Meteorological parameters such as relative humidity, boundary layer height, temperature, and wind speed are available from ERA5 reanalysis. Vertically resolved aerosol information from MPLNET lidar is used to identify elevated aerosol layers associated with transported wildfire smoke.

The approach combines Pearson and Spearman correlations, partial correlation analysis, multivariate regression, and Random Forest (RF) modeling to capture nonlinear interactions. Results indicate weak but statistically significant correlations between AOD and PM2.5 (r ≈ 0.26-0.30), with stronger monotonic relationships. A pronounced seasonal dependence is observed, with the strongest coupling in autumn and weak or insignificant relationships in winter. Partial correlation analysis suggests that a residual association between AOD and PM2.5 remains after accounting for meteorological influences.

RF models improve predictive performance (R² ≈ 0.39), although performance degrades in winter. Sensitivity analysis indicates that transported smoke plumes can influence the AOD-PM2.5 relationship, particularly when partial mixing into the boundary layer occurs.

Over the past decade, nearly ten satellites dedicated to atmospheric CO2 concentration monitoring have been launched, significantly advancing our understanding of the global carbon cycle. In 2022, China launched the DaQi-1 (DQ-1) satellite, which carries the Aerosol and Carbon Dioxide Lidar (ACDL)—the first spaceborne lidar sensor for CO2 monitoring. Relying on laser-based active sensing, ACDL can detect global XCO2 at nighttime, serving as an important complement to existing passive optical CO2 satellite missions. This study aims to introduce the scientific community to the XCO2 retrieval methodology of ACDL and its initial XCO2 product. The first version of ACDL XCO2 products scheduled for release is called “v1.0”. This paper presents a comparison between XCO2 at daytime and nighttime. Nonetheless, challenges remain, including reliance on meteorological reanalysis data and uncertainties in spectroscopic parameters. In future product versions, we plan to improve data quality through enhanced denoising techniques and signal processing methods for low signal-to-noise ratio (SNR) cases. We hope that this initial ACDL XCO2 product will spark broader interest and participation from the scientific community, thereby contributing fresh momentum to climate change research.

Traditional forecasting methods of air pollutants show intrinsic limitations due to the complexity of atmospheric interactions. Recent research has moved toward the employment of artificial intelligence (AI)-based approaches and satellite data processing. The framework proposed in this study is a transfer learning (TL) model to estimate surface-level NO2 concentrations across multiple locations by using satellite and environmental data.

The approach integrates Sentinel-5P TROPOMI-derived tropospheric NO2 columns, meteorological variables (temperature, precipitation etc), spatial coordinates and temporal features.

A CatBoost regression model is implemented, leveraging a Leave-One-City-Out (LOCO) TL framework across five cities (Berlin, London, Madrid, Paris and Toronto) in the world. This enables the model transfer from multiple source domains to a new target city with minimal ground-based data. Experimental results are outperforming city-specific baseline models, by showing an increased prediction accuracy, a reduced Root Mean Square Error (RMSE) by approximately 7% and a Coefficient of Determination (R2) higher by 2.7%. Toronto, which represents an environment with a low monitoring density, benefits most from TL, with R2 improving from 0.58 (baseline) to 0.66 (transfer) and RMSE dropping from 6.44 µg/m3 to 5.84 µg/m3.

A detailed Leave-One-Block-Out (LOBO) ablation study shows how each group of features contributes to the performance of the model. Spatial coordinates and meteorological features are the most influential predictors of NO2 concentration, while the satellite NO2 data increase model generalization. These results highlight the potential of cross-city TL and remote sensing synergy for scalable urban air pollution monitoring, especially in limited ground-based monitoring scenarios.

High-resolution mapping of tropospheric ozone is essential for urban environmental assessment; however, satellite-derived ozone products are generally too coarse to capture neighborhood-scale variability in complex megacities such as Tehran. This study introduces an interpretable deep-learning framework that downscales coarse Sentinel-5P ozone observations to a 30-m spatial grid by integrating a U-Net convolutional architecture with SHapley Additive exPlanations (SHAP). A diverse suite of predictors—including land-surface indicators, meteorological parameters, terrain morphology, and chemical precursors—was harmonized and resampled to a unified spatial resolution. SHAP analysis was applied to quantify each predictor’s contribution, enabling the removal of redundant or low-impact variables before model training. Using spring 2020 as the evaluation period, the optimized U-Net successfully reconstructed fine-scale ozone gradients and reproduced Tehran’s characteristic north–south pattern driven by topography and emission density. Comparative analysis with preliminary outputs demonstrates that feature optimization enhances spatial coherence, reduces noise artifacts, and improves the representation of localized hotspots. Statistical evaluation further showed strong agreement between the downscaled ozone estimates and observational data at both station and district scales, demonstrating effective generalization across heterogeneous urban environments. Overall, the findings highlight the potential of combining deep learning with interpretability techniques to refine coarse satellite ozone observations and provide a scalable, high-resolution framework for urban air-quality monitoring and exposure assessment.