Conference Agenda

Solar-induced fluorescence (SIF) has become a promising remote sensing proxy for photosynthetic activity and thus plant health, but its broad application across vegetation types and climate regimes remains underexplored. Here, we present the first continental-scale assessment of seasonal SIF signatures for 33 vegetation types across 24 climate zones in the contiguous United States, enabled by a new open-access visualization tool. The analysis uses TROPOMI satellite SIF data (2019-2021), along with MODIS-derived gross primary productivity (GPP), normalized difference vegetation index (NDVI), and vapor pressure deficit (VPD). Our results show that SIF has consistently stronger and more reliable correlations with GPP than NDVI across vegetation types and environmental conditions. This relationship remains robust even under high VPD conditions (except for several perennial crops), confirming the ability of SIF to track productivity even in dry environments. While NDVI retains structural sensitivity, it often decouples from GPP under stress, particularly in arid climates and perennial crops. We also identify clear differences in SIF-NDVI and GPP-NDVI relationships by vegetation type and climate, with NDVI showing limited responsiveness to dynamic changes in canopy physiology. Despite the coarse spatial resolution of TROPOMI, these results demonstrate the feasibility of constructing climate-specific SIF signatures for agricultural and ecological monitoring. By identifying these climate-specific signatures at the continental scale, this work highlights the value of SIF for climate-smart crop management, productivity assessment, and satellite-based ecosystem modeling.

Accurate estimation of gross primary production (GPP) is fundamental for quantifying the terrestrial carbon cycle. However, coarse-resolution products often fail to capture fine-scale spatial variations in carbon uptake across heterogeneous landscapes. While recent studies have begun to employ 10 m Sentinel-1 and Sentinel-2 imagery, they typically reduce these data to pixel-wise spectral indices, discarding the two-dimensional spatial structure (canopy architecture, land-cover transitions, within-stand heterogeneity) that the imagery encodes. This study investigates whether explicitly exploiting this spatial context via convolutional neural networks yields robust, transferable gains over tabular machine-learning baselines. We curate a quality-controlled dataset of 23,528 eight-day multi-sensor composites from 222 AmeriFlux sites (2015–2025), evaluated under site-wise cross-validation, temporal generalisation, and geographic transfer to an 18-site upper Midwest forest holdout. Under temporal transfer to unseen years (2023–2025), the best convolutional model achieves R² = 0.77 and RMSE = 1.95 gC m⁻² d⁻¹, an 18.6% RMSE reduction over ridge regression (R² = 0.65, RMSE = 2.40 gC m⁻² d⁻¹). Although this advantage narrows under geographic transfer to structurally novel regions (R² = 0.59 vs. 0.54), the convolutional models still outperform all tabular baselines. Spatial structure at 10 m therefore supports more robust temporal generalisation than spectral aggregates alone.

Accurate gross primary productivity (GPP) estimation is crucial for understanding ecosystem function and the global carbon cycle. Remote sensing offers promising GPP proxies, including solar-induced chlorophyll fluorescence (SIF) and the structural proxy NIRvP. However, their performance and underlying drivers of effectiveness vary significantly across biomes. This study comprehensively evaluated the accuracy and limitations of SIF and NIRvP against flux GPP across diverse biomes (CRO, GRA, DBF, ENF), also investigating physiological and structural controls on LUE.

We found that proxy performance was highly biome-specific. Notably, the removal of canopy escape probability (fesc) from observed SIF (SIFobs) to derive total emitted SIF (SIFall) did not consistently improve, and sometimes even diminished, its correlation with GPP, particularly in CRO and GRA. Furthermore, we elucidated distinct dominant controls on seasonal LUE variations: apparent SIF emission yield (ΦF×fesc) was paramount in ENF, while canopy structure (fesc) predominated in CRO, GRA, and DBF. Seasonal analysis in ENF further revealed a temporal decoupling, with fesc decline lagging LUE in winter, and ΦF failing to track autumnal LUE reductions. These findings underscore the biome-specific necessity for optimal GPP proxy selection, establishing a robust scientific foundation for improved remote sensing monitoring.

Accurate quantification of gross primary productivity (GPP) is essential for understanding carbon dynamics under climate change. However, satellite-based GPP estimates face spatial–temporal trade-offs, limiting accuracy in heterogeneous landscapes. To overcome this challenge, we proposed a novel framework named UNified, high-resolution Intelligent carbon QUantification and Explanation (UNIQUE), which produces daily 30 m GPP maps by integrating spatial relationships between 500 m MODIS and 30 m Landsat imagery. UNIQUE consists of two components. First, two AI models were trained using MODIS- and Landsat-based vegetation indices combined with meteorological reanalysis data and validated with 309 eddy-covariance flux tower observations across the Northern Hemisphere. The Light Gradient Boosting Machine (LGBM) showed the best performance, achieving r = 0.80 and RMSE = 2.47 gC/m²/day for MODIS-based GPP, and r = 0.83 with RMSE = 2.43 gC/m²/day for Landsat-based GPP. Second, a diffusion-based deep learning model was used to downscale MODIS-based GPP to 30 m resolution. The diffusion model from MODIS to Landsat GPP exhibited good performance, demonstrating an RMSE of 2.12 gC/m²/day for the testing sites. The proposed approach enabled the analysis of spatiotemporal characteristics of GPP across different plant functional types, facilitating enhanced high-resolution carbon flux monitoring in diverse ecosystems.

Insights into crop productivity have long been of great interest to almond growers, as they enable effective planning to optimise economic returns. Advances in sensor technology have made it possible to collect hyperspectral imagery, which captures detailed information across a continuous range of wavelengths and has become a powerful tool for assessing crop physiological status. Solar-induced chlorophyll fluorescence (SIF), along with other plant pigments and structural traits retrieved through radiative transfer modelling, can effectively track crop photosynthetic activity. However, the ability to quantify SIF is strongly influenced by the spectral resolution of the sensor.

This study examines how the spectral resolution of airborne hyperspectral sensors affects the ability to explain yield variability in a commercial almond orchard, by comparing SIF derived from the 760 nm and 687 nm oxygen absorption bands at different spectral resolutions.

Green roofs (GRs) are constructed systems that replicate natural ecosystems and provide runoff reduction, cooling effect, habitat support and improved air quality services. Over 1,000 GRs have been constructed in Toronto since the GR Bylaw was enacted. As they are dispersed and primarily small-scale stormwater assets on private properties, it is crucial yet difficult to assess these roofs' condition to ensure they continue to deliver the desired advantages and adhere to maintenance regulations. To maintain green stormwater infrastructures in ideal conditions, it is advised that the vegetation be maintained with 80% coverage. GR vegetation experiences plant loss, water stress, and other maintenance concerns requiring regular inspections. This study presents a framework to enable remote assessment of green roof conditions utilizing Sentinel-2A satellite imagery, which captures images every five days. This method overcomes logistical challenges associated with drone imagery inspections, which are limited in frequency and require permits. The study was conducted using Google Earth Engine, focusing on the intra- and inter-annual variation of four GR modules. The study assessed the vegetation health from 2018 to 2025 using NDVI, EVI and NDMI, highlighting the long-term dynamics and distribution of GR vegetation. The results present the effectiveness of NDVI, EVI, and NDMI in assessing plant coverage and moisture content, with low NDMI being an important factor resulting in low NDVI and EVI. The study contributes to the potential of satellite images for scalable and continuous monitoring of GRs and supports efficient and complementary inspection.

Accurate large-scale dense 3D reconstruction is fundamental for geospatial mapping, robotics, and autonomous navigation. Conventional photogrammetric workflows can reconstruct environments from ground-level imagery but often suffer from cumulative drift over kilometer-scale trajectories and require extensive calibration. Recent advances in feed-forward 3D reconstruction, notably the Visual Geometry Grounded Transformer (VGGT), have demonstrated the ability to generate dense point clouds directly from RGB images without explicit optimization. VGGT jointly estimates camera poses, depth, and dense geometry from multiple uncalibrated frames in a single forward pass. However, its scalability is limited by two factors: (1) the lack of absolute metric scale and (2) high GPU memory demands.

Many national mapping agencies (e.g., USGS, IGN, Ordnance Survey) have released Airborne Laser Scanning (ALS) datasets covering vast urban and rural areas. These high-quality aerial point clouds provide globally consistent, metrically referenced data that can serve as external constraints for ground-level reconstructions. Building upon this opportunity, we propose VGGT-ALS, a framework that leverages open ALS point clouds to enable VGGT-based systems to produce large-scale, metrically accurate dense maps from mobile mapping imagery.

High-precision vehicle localization in GNSS-denied urban areas requires alternatives to costly HD maps. In this paper, we present a novel framework for feature extraction and benchmark generation to enable high-precision localization using abstract LoD2/DTM maps as a replacement for HD maps. Our first contribution, a semantic-geometric pipeline, processes dense LiDAR and camera data to extract map primitives. This is accomplished by a RANSAC-fitted ground plane extraction step, followed by a semantic filter that discards dynamic objects. Finally, geometric clustering (HDBSCAN) and RANSAC plane fitting isolate large-scale vertical facades. Our second contribution, a multi-stage GT generation framework, resolves annotation ambiguity using a Human-In-The-Loop (HITL) system. A robust 2D pose is computed by finding the geometric median of bootstrapped transformation samples on the SE(2) manifold, which is then refined to a 6-Degree-of-Freedom pose via point-to-plane ICP, before being validated by a human for a final check. We evaluated our feature extraction pipeline against the generated benchmark, achieving 95.04% precision and 83.74% recall. An analysis of this performance shows the pipeline correctly rejects small, ambiguous features while achieving high recall on all large, stable features, proving its suitability for a robust localization filter.

As civil infrastructure in South Korea ages, the demand for systematic monitoring has grown. While Unmanned Aerial Vehicles (UAVs) provide a safer alternative to manual inspections, Global Navigation Satellite System (GNSS) signal degradation beneath bridge structures remains a critical barrier to autonomous flight. Unlike existing hardware-centric or SLAM-based solutions that require high costs and computational overhead, this study proposes a robust, algorithm-based path planning methodology using a 3D spatial grid framework.

Two strategies were evaluated through field tests at the Bukhangang Bridge: the Photography Point Method (PPA) and the GNSS Non-Shadowing Area Method (WPS-GNSA). Results demonstrated that WPS-GNSA significantly enhances signal reliability by strategically positioning waypoints outside electromagnetic shadow zones. At an optimal 19-meter separation distance, WPS-GNSA maintained GNSS Level 5 connectivity—the threshold for recommended autonomous flight—for 9.4% of the duration and Level 4 or higher for 79.7%, whereas the PPA peaked at Level 4.

These findings indicate that WPS-GNSA enables reliable autonomous inspections using standard commercial drones without specialized hardware modifications. While the current model relies on pre-existing digital blueprints, future research will integrate AI-based navigation and real-time environment perception to enhance scalability and adapt to dynamic obstacles in complex infrastructure environments.

Accurate and reliable positioning is fundamental for the development of a wide number of applications. Despite in most of the regular working conditions the use of a GNSS receiver is sufficient for properly solving the problem, determining a reliable solution in challenging conditions can be difficult. In such conditions, exploiting the information shared by different sensors and platforms can be useful for reliably determining the platforms' positions. In this work, both ground and aerial platforms are considered: each platform is assumed to be provided with communication capabilities, which can be exploited to share its knowledge. Since GNSS positioning is usually less effective at ground level than on a flying platform, the aerial platforms are assumed to be provided with good GNSS-based positioning information. Instead, GNSS is assumed to be unavailable to the ground vehicles, which, instead, can use LiDAR/visual odometry for dead-reckoning positioning, UWB inter-platform ranging for relative positioning, and camera-based positions, provided by aerial platforms, for assessing their georeferenced positions. This work focuses on assessing the positioning performance when exploiting vision-based information about the georeferenced ground vehicle positions from a camera mounted on a UAV. The camera acquired oblique views of the scene while moving over the case study area during the test. YOLO was used to detect cars from the image frames and the vehicle coordinates have been extracted from 3D reconstructions obtained from the MoGe-2 network. Average errors at meter level on the determined georeferenced coordinates were obtained when combining UWB vehicle-to-vehicle ranges with MoGe-2 reconstructions.

Reliable positioning and mapping in dense urban environments remain challenging due to signal blockage, multipath, and dynamic scenes. Progress on multi-sensor integrated positioning and visual/lidar SLAM has been driven by open datasets, yet most existing resources are either perception-centric with limited raw navigation data, focused on controlled environments, or built around outdated software platforms and/or data formats. In this paper, we present the Melbourne Multi-Sensor Urban Positioning and Mapping Dataset, a new resource targeting urban vehicle navigation and mapping tasks. The dataset was collected using a custom mobile mapping platform equipped with a tactical-grade INS, a survey-grade Leica GNSS receiver, a low-cost UBLOX GNSS receiver, a high-resolution Ouster OS1 128 lidar, and four industrial FLIR cameras providing 360° coverage. Seven data collection trips were recorded on dynamic streets in several inner suburbs of Melbourne, including multiple closed loops and a repeated route with day–night variation. For better compatibility and future-proofing, all raw data are provided as standard ROS2 message streams in MCAP format, complemented by commonly used individual formats and GNSS products for multi-sensor integrations. We benchmark three GNSS--based positioning packages (RTKLib, Net_Diff and Ginan) and four state-of-the-art lidar(-inertial) odometry/SLAM methods (FAST-LIO2, KISS-ICP, KISS-SLAM and PIN-SLAM), demonstrating the applicability and compatibility of our dataset for modern positioning and mapping software pipelines. The dataset is designed as a robust, ROS2-native testbed for research on GNSS/IMU/lidar/camera fusion for the testing and validation of vehicle positioning and mapping in urban environments, which is available open-source at https://github.com/zjjdes/melbourne_dataset.

Accurate and robust localization is essential for autonomous vehicles and mobile robots operating in complex, dynamic environments. However, existing learning-based LiDAR-inertial odometry (LIO) methods typically rely on simple weighted fusion or purely global attention, which may not fully exploit cross-modal complementarity. In this paper, we propose CMLGF-LIO, a cross-modal local-global fusion framework that improves LIO accuracy and robustness. At the local level, we design a Local Split-Attention (LSA) module that injects IMU-derived motion priors into local LiDAR feature groups and adaptively allocates attention weights, suppressing redundant information while preserving discriminative local geometry for fine-grained fusion. At the global level, we introduce a Global MLP-Mixer (GMM) module that aligns LiDAR and IMU token sequences and models global cross-modal interactions using an MLP-Mixer backbone. Experiments demonstrate that CMLGF-LIO is more robust than learning-based baselines under challenging conditions, and ablation studies validate the effectiveness of the proposed local-global fusion strategy.

This paper presents a low-cost mobile mapping system built on a consumer vehicle (2026 Tesla Model Y) equipped with a roof-mounted Velodyne VLP-16 LiDAR and three post-processed kinematic (PPK) GNSS receivers. A processing pipeline was developed that fuses scan-to-scan LiDAR registration via KISS-ICP with multi-receiver PPK trajectories to produce georeferenced 3D point clouds. The system was tested on a 2 km loop on the Ohio State University campus. KISS-ICP achieved a registration fitness of 1.0 across all 3,400+ frames, producing locally crisp point clouds. A yaw-only Procrustes alignment followed by interactive 7-parameter refinement maps the SLAM trajectory into an East-North-Up geodetic frame. We document the complete pipeline architecture, including automated GPS time synchronization, multi-receiver vehicle pose estimation, and a streaming LAS export capable of handling 70+ million points. We systematically evaluate ten post-hoc trajectory correction strategies and identify a fundamental trade-off between inter-frame consistency (point cloud crispness) and absolute geodetic accuracy. The primary unresolved challenge is a staircase artifact caused by ~40 m of accumulated SLAM drift over the loop, which cannot be corrected without degrading local registration quality. We conclude that loop closure detection and pose graph optimization within the SLAM pipeline are necessary to resolve this tension and outline a path toward survey-grade mobile mapping from consumer vehicle platforms.

The Virtual Campus Project at the University of Cologne (UoC) has as a main objective the creation of a highly detailed 3D model of the university campus and its publication and distribution through OGC 3D Tiles. Further objectives include the development of integrated applications leveraging this 3D model, such as a web-based 3D viewer, game engine-driven geospatial augmented reality (GeoAR) and virtual reality (VR) experiences, and an indoor positioning system utilizing 3D building models indoor geometries. This paper focuses on and details the methodology for developing and implementing the georeferenced 3D model and establishes an Open Geospatial Consortium (OGC) 3D Tiles-compliant Spatial Data Infrastructure (SDI). The main result is a Tool Suite or Software Framework and the description of the tool pipeline or workflows for collecting, creating and modelling the 3D geospatial data and publishing it as OGC 3D Tiles data. This framework ensures campus-wide 3D data accessibility through 3D Tiles standard clients, including Desktop GIS like ArcGIS or QGIS, game engines like Unity, Unreal or O3DE and Webmapping libraries like MapLibre, three.js or CesiumJS.

The assessment of sidewalk infrastructure for accessibility compliance is an important task in urban planning; however, traditional methods are often manual, subjective, and resource-intensive. This paper introduces BirdCV-LiDAR, a multimodal data fusion framework for an automated assessment of sidewalk infrastructure. The proposed approach integrates high-resolution bird's-eye-view (HR-BEV) imagery with aerial LiDAR point cloud (ALPC) data to automatically detect, measure, and assess sidewalk features for compliance with accessibility standards. By combining YOLO-oriented bounding box (OBB) models with precise LiDAR-based elevation data, the framework enables accurate dimensional and slope evaluations of sidewalk features, such as crosswalks and truncated domes. Validation with a 12-inch inclinometer shows that LiDAR-based slope measurements achieve 84.7% accuracy, with a root-mean-square error (RMSE) of 0.1152 meters for crosswalk width measurements. The framework achieves 81.0% accuracy in determining ADA-PROWAG compliance, providing an adaptable, expandable solution for improved urban accessibility assessments.

This paper proposes a micro-scale walkability metric based on harmonised indicators that supports pedestrian route planning, which prioritises pleasant environments alongside distance efficiency. The employed method quantifies street segments and crossings using geospatial indicators, including pavement width, slope, shade, adjacency to traffic, park context, and crossing type and width. Indicator values are transformed to percentile ranks to harmonise heterogeneous inputs and are aggregated into a single edge-level walkability score on a 0 to 1 scale. The score is integrated into a routing cost function that reduces edge cost with higher walkability, which favours calmer, greener, and wider links while bounding detours relative to the shortest path. The method also accommodates the incorporation of street-level perceptions through a structured survey instrument and a confidence-weighted fusion scheme.

The results show various spatial patterns. Central areas and park-adjacent segments exhibit higher scores, while steep, narrow, and traffic-exposed links score lower, and several suburban and foothill districts display reduced walkability. The comparison with a distance-only baseline shows selection of quieter alignments with modest length increases, indicating potential gains in perceived pleasantness.

Urban areas are continuously expanding outward with economic development and demographic growth, while simultaneously growing higher vertically. However, few efforts have been made to evaluate the impact of development priorities in different regions on urban sustainability, which limited our understanding of how urbanization has been affected by imbalanced evolution rhythm. Here we developed a 3D structure-based approach to assess volumetric urban expansion, as well as a refined evaluation system for assessing urban imbalanced growth trends. Results show that the 3D expansion patterns of urban areas exhibited significant heterogeneity globally. As urbanization accelerates, urban areas in the Global South are showing a trend of faster expansion accompanied by faster vertical growth. In addition, imbalanced growth types across different dimensions are significantly more complicated in the Global South than in the Global North, indicating the variance of development priorities is greater in the Global South. Furthermore, the imbalances are intensifying over time, as indicated by the temporal indices. Our study enhances the understanding of urban 3D patterns and imbalanced urban evolution, providing crucial insights for more balanced urbanization especially in the Global South.

Urban green spaces are important for residents’ physical and mental health, but green visibility is difficult to quantify in high-rise, high-density cities, especially across different height levels. To address this problem, this study proposes a stratified green visibility framework based on airborne LiDAR point clouds and a voxel model. Using the Dutch AHN5 dataset, the study area was converted into a unified 3D voxel space and classified into trees, grass, buildings, ground, and empty space. A voxel-level penetration probability model based on the Beer–Lambert law was introduced to represent the semi-transparent blocking effect of tree canopies, improving upon conventional binary visibility models. Multi-directional line-of-sight (LOS) tracing was then applied to calculate green visibility (GVI) and spatial openness (SOP) at different height layers. The results show that GVI is generally high around parks, large green spaces, and some enclosed courtyards, but its contribution from street trees is limited. Vertically, GVI decreases with height, while SOP tends to increase. Combining the two indicators helps identify different spatial types with distinct visual characteristics. The study demonstrates that airborne LiDAR, combined with voxelization and probabilistic 3D simulation, can effectively capture the vertical variation of urban GVI and support large-area assessment in high-density residential environments.

Accurate building-level mobile device positioning is critical for fine-grained location-based services and human activity analysis, as people spend 80–90% of time indoors. Existing techniques rely on dedicated infrastructure or dense fingerprinting, limiting scalability. This study proposes a lightweight, infrastructure-free framework integrating two core modules: 1) Indoor/outdoor classification via a random forest model trained on a multi-scene sample library, using satellite, Wi-Fi, Bluetooth, and cellular sensor features with similarity-guided training selection; 2) Building matching through a Bayesian inference model leveraging three-scale spatial features (device, building, area) and prior knowledge from anonymous crowdsourced data. Validated in Beijing, Nanjing, and Xi’an, the framework achieves over 90% overall precision for indoor/outdoor classification and ≥70% precision for building matching with satellite or Wi-Fi features alone. It requires no extra infrastructure or extensive labeled data, offering a scalable solution for smart city applications like population analytics, emergency response, and context-aware services across heterogeneous urban regions.

In Japan, the combined effects of declining birth and marriage rates have accelerated population decline, leading to spatial porosity and fragmentation in urbanised areas: a phenomenon known as “Urban spongification”. This study analyses local population distributions in order to identify localised low-population areas embedded within densely populated urban environments, with the aim of understanding spatial porosity and fragmentation in Osaka Prefecture. A multi-scale spatial autocorrelation approach was applied to detect the spatial extent of localised low-population areas, and results were compared between 1995 and 2020. The analysis further examined how the formation and change of localised low-population areas differ across Use Districts and according to long-term land-use transition histories. The findings reveal pronounced spatial variability within districts that cannot be captured by conventional population density metrics alone. The study demonstrates that the emergence, persistence, and transformation of localised low-population areas are closely related to zoning regulations and historical land-use processes. These results provide insights into the spatial processes contributing to urban porosity and fragmentation and offer a basis for future evaluations of residential inducement areas designated under Location Optimisation Plans.