Conference Agenda

Bathymetric Laser Scanning (BLS) enables high-resolution mapping of underwater topography using green-wavelength laser pulses that penetrate the water column. However, precise georeferencing of the BLS data is affected by refraction at the air–water interface, which displaces submerged features and affects conventional strip adjustment methods. This paper introduces an integrated refraction-aware georeferencing workflow that combines refraction correction with trajectory and boresight optimization within a unified adjustment framework. Implemented using the scientific OPALS laser scanning software, the workflow starts with direct georeferencing of uncorrected laser returns, derives a water surface model, applies Snell’s law-based refraction correction, and performs iterative strip adjustment until convergence. The approach was validated using UAV-borne topo-bathymetric LiDAR data from Lake Alm (Almsee) in Upper Austria, captured with a \emph{RIEGL} VQ-840-GE sensor system. Comparative analysis across multiple processing scenarios demonstrates that the proposed integrated method significantly improves internal consistency between overlapping flight strips. The residual height discrepancies, quantified by the median absolute deviation were reduced from 4.5 cm using standard processing workflows to 2.1 cm with the integrated approach — an improvement exceeding 50%. A single processing pass was sufficient for the relatively calm conditions of the test site, though iterative refinement may benefit more dynamic water surfaces. The presented methodology is generic and can be embedded in any laser scanning framework supporting modular georeferencing and refraction correction.

Coastal defense structures, such as breakwaters and groynes are an integral part of coastal engineering. These structures reduce the impact of waves and decrease beach erosion, but due to the constant forces to which they are exposed, repeated monitoring and evaluation is vital to the analysis of their structural integrity. However, coastal defense structures are most often located in the turbulent waters of the surf zone, which characteristics pose severe challenges for current methods. For example, waves pose challenges for image-based analysis, shallow-water limits sonar-based measurements, and currents, represent hazardous environments for surveying personnel. Here, recent advances in topo-bathymetric LiDAR have improved the ability to map data above and below the water surface within the same survey. In the field of structural engineering, point cloud data is already a commonly used information, and thus its applications in the monitoring of coastal defense structures present a natural extension of existing structural monitoring methods. Therefore, this study presents an automatic method for the detection of coastal defense structures with bathymetric LiDAR. The surveyed area consists of multiple groynes located along the Polish coast, which were surveyed using an airplane-based topo-bathymetric LiDAR scanner. The presented method then leverages the echo ratio and repeated clustering to extract the groynes from the data. We evaluate the extracted structures in comparison to manually annotated data. The results of this evaluation display a balanced accuracy of 92%, indicating an overall match with the reference data, but showing challenges and improvements for future work.

A state-of-the-art LiDAR sensor is assessed in terms of the accuracy, described as the sum of trueness and precision, of terrestrial and submerged points. The reference, against which the LiDAR data are evaluated, are conducted with a total station and can be assumed to show an uncertainty of less than 1 cm even for the submerged points. We find that the GNSS-based data set shows a systematic bias of about (-4, 7, 7) cm which can be defined as trueness and does not represent the quality of the LiDAR sensor but mostly of geo-referencing. The precision, which is a measure mostly influenced by the LiDAR sensor itself, is at 0.8 to 2.0 cm for terrestrial points and slightly worse with 1.1 to 2.6 cm for bathymetric points. Our study considers depths of up to 3 m and uses more than 300 points for the assessment.

Worldwide coastal areas comprise environmental triple points (air, land and seawater) that cope with coastal risks at unprecedented rates of change. Wind- and wave-related acute hazards add up to the chronic sea-level rise on interface zones that increasingly host human population and assets. Those societal challenges need to be overcome using the most discriminant and finest remote sensors.

We present an innovative two-step methodology to produce an ultra-high spatial resolution (UHSR) topobathymetry using a fusion of a RGB camera mounted on an aerial drone with a multispectral satellite imagery provided with very high temporal resolution. The fusion relied on a DJI Zenmuse P1 (0,08 m pixel size) borne by a DJI Marice 300 RTK, the PlanetScope SuperDove imagery, provided with eight bands at 3 m, and linear or nonlinear (neural network with two hidden layers endowed with three neurons, each) regression. Once the fusion achieved, both topography and bathymetry were mapped using, either the digital surface model (DSM) derived from the drone-derived photogrammetry, or the DSM combined with the UHSR SuperDove imagery. Both datasets served as predictors to model a digital topobathymetric terrain LiDAR response using linear or neural network regression.

The best drone-satellite fusion was completed by the bandwise neural network regression, ranging from R2test of 0,79 for the purple to 0,94 for the red edge band. The UHSR topobathymetry has been mapped by merging the topography and the bathymetry, distinctly predicted by the combination of the DSM with the UHSR Superdove imagery (R2test of 0,68 and 0,92, respectively).

Rocky coastal environments are ecologically important areas where land and sea processes interact in complex ways. Monitoring these zones is challenging, as they include steep cliffs, partially submerged features, and narrow transition areas where traditional surveying methods often struggle. Several European environmental directives now emphasize the need for regular observation of these coastal systems, increasing demand for practical and accessible surveying tools.

This work presents the development and initial testing of a small uncrewed surface vehicle (USV) designed to collect images above and below the water surface at the same time. The platform is based on a commercially available catamaran-style drone and carries two GoPro cameras mounted on a rigid vertical rod, with one camera positioned above the water and the other just below it. Both cameras are synchronized using GPS time, and the system incorporates a PPK-capable GNSS receiver for improved positioning. The payload is wireless and modular, allowing the platform to be deployed quickly.

The main contribution of the system is its ability to document the air–water boundary in a single pass, reducing issues related to changing meteorological and sea conditions. The paper also discusses how the platform was tested at a rocky site in Sardinia and outlines the types of data that can be obtained for environmental mapping.

The approach aims to offer a low-cost, flexible option for coastal monitoring.

Underwater laser scanners and air-borne laser scanners offer considerable potential for high-resolution monitoring of fine-scale underwater structures in shallow, clear waters. An underwater laser scanner mounted on a vessel is used for kinematic data acquisition in coastal waters. Additionally they are surveyed by an air-borne laser scanner. In this investigation, the resulting point clouds from both systems are analyzed in terms of their performance and achievable relative geometric quality.

The spatial accuracy and resolution of Multibeam Echosounder data are inherently lower than those of high-resolution underwater LiDAR measurements. However, while Multibeam Echosounder provides wide coverage and extensive historical availability, LiDAR is costly and covers relatively small areas. In this study, we propose an innovative approach to enhance Multibeam Echosounder resolution using a Super-Resolution Generative Adversarial Network with direct comparison to LiDAR data for accuracy assessment. The methodology involves converting Multibeam Echosounder data into grayscale format using various depth gradient techniques, analyzing differences in submarine geomorphology through calculations of slope and aspect, and evaluating statistical accuracy. The results show that the Super-Resolution Generative Adversarial Network model successfully improves Multibeam Echosounder resolution, producing data that closely correspond to LiDAR measurements, particularly in flat, sandy seabed areas. In contrast, regions with complex or rocky terrain exhibited more pronounced deviations, especially in aspect metrics, emphasizing the challenges associated with maintaining topographic orientation throughout the resolution enhancement process. The main conclusion is that enhancing Multibeam Echosounder data using Super-Resolution Generative Adversarial Network enables broader utilization of existing datasets to generate high-resolution models, offering a more cost-effective and accurate solution for seafloor mapping in areas where LiDAR data are unavailable.

Geometric correction of satellite images is an essential pre-processing step for accurate geospatial analysis, but non-experts often face practical limitations because detailed sensor models and Ground Control Point data are not readily accessible. Traditional methods rely on physical sensor models or the Rational Function Model (RFM) using vendor-provided Rational Polynomial Coefficients (RPC). However, this information is often unavailable or lacks sufficient accuracy. This paper proposes a two-stage framework that utilizes AI matching technologies and open access data to automatically correct satellite images lacking georeferencing information. In Stage 1, a coarse Affine correction is executed using SuperPoint and LightGlue with an open basemap (Sentinel-2). In Stage 2, precise corresponding points are extracted through patch-based hierarchical LoFTR matching, and 3D GCPs are generated utilizing the SRTM. Subsequently, sensor-independent RPC are robustly estimated through the rpcfit library, and the final geometrically corrected image is generated through resampling.

This framework was verified by applying it to 4.8m resolution BlueBON satellite images that lack georeferencing information. In seven experimental regions with diverse geographical characteristics, an average Root Mean Square Error (RMSE) of 8.050m (1.68 pixels based on BlueBON resolution) referenced to the Sentinel-2 basemap, and an average of 9.02m (1.88 pixels) referenced to Google Maps, was achieved. This result demonstrates that it is possible to precisely correct 4.8m medium-resolution images using a 10m open basemap, providing a practical, accessible, and automated geometric correction solution for general users.

Star centroid localization accuracy fundamentally limits spacecraft attitude determination precision. Existing methods face a critical accuracy-efficiency trade-off: traditional intensity-weighted approaches achieve computational efficiency (<1 ms/star) but suffer from poor noise robustness, while Gaussian fitting and deep learning methods provide high accuracy at prohibitive computational costs. We address this fundamental limitation by developing a principled Bayesian Multi-Scale Adaptive Iteratively Weighted (BMAI) centroid localization algorithm that achieves high accuracy approaching theoretical limits while maintaining real-time computational efficiency. The algorithm integrates four key technical contributions: (1) SNR-adaptive window extraction with robust threshold estimation, (2) regularized iteratively weighted framework with proven convergence properties, (3) multi-scale fusion with SNR-dependent weighting, and (4) gradient-based refinement to mitigate systematic bias. Rigorous theoretical analysis establishes convergence guarantees, derives error bounds, and evaluates Cramér-Rao Lower Bound (CRLB) efficiency. Comprehensive evaluation on 16,500 synthetic star images across six diverse imaging scenarios demonstrates that under high-SNR conditions (SNR >25, n=2,000), BMAI achieves mean RMSE of 0.0120 pixels (95% CI: [0.0116, 0.0124] pixels), representing a 98.6% relative improvement over intensity-weighted centroiding (0.857 pixels), 35.8% improvement over Gaussian fitting (0.0187 pixels) and 95.3% improvement over CNN methods(0.2566 pixels). The algorithm maintains computational efficiency of 0.89ms per star—8.7× faster than Gaussian fitting—while achieving CRLB efficiency of 79.2%. Robustness analysis demonstrates stable performance across SNR range 3-100 with graceful degradation under challenging conditions. The BMAI algorithm fundamentally resolves the accuracy-efficiency trade-off in star centroid localization through principled Bayesian inference and multi-scale processing.

This paper presents a systematic investigation of the PhaseOne native IIQ format for drone and aerial cameras (in particular the recent iXM-RS250 and the iMX-GS120), focusing on the influence of different compression levels on geometric, radiometric and computational aspects of the photogrammetry pipeline. The aim of the presented research and experiments is to demonstrate the actual quality of these (compressed) images for photogrammetric purposes.

Small format multi-head cameras are becoming available and can be flown on light drones to provide simple access to oblique and nadir views of built-up areas. A number of missions with different parameters (flying height, etc.) are investigated to understand the trade-offs in applying those sensors and question the established accuracy laws. We investigate and quantify the ability to completely cover the facades using those sensors in the different scenarios.

In recent years, commercial small optical satellites, e.g., Skysat, BlackSky, and PlanetScope, have become widely used for a variety of Earth remote sensing applications, providing high-resolution images with sub-meter resolution. They are operated in a constellation of multiple satellites, which compensates for the spatial and temporal limitations of traditional satellite observations. Moreover, their data acquired during stereo viewing have been experimentally used to generate digital surface models (DSMs). The CE-SAT-IE is a small optical satellite developed by Japan’s commercial company Canon Electronics Inc. (CE) and was launched on 17 February 2024, by Japan Aerospace Exploration Agency’s (JAXA’s) H3 launch vehicle test flight no.2. It is equipped with an optical frame sensor derived from a commercial off-the-shelf (COTS) camera Canon EOS R5. The ground sampling distance (GSD) is 0.8 m with a scene size of 6.5 km × 4.3 km. The calibration and validation of the sensor are being conducted in collaboration between CE and JAXA, drawing on JAXA’s extensive experience with past satellites. The geometric and radiometric performance of the sensor is analysed in detail, and the results will be used for its subsequent mission, which may involve a constellation for stereo observation to generate high-quality DSMs. This paper reports initial results for geometric calibration and validation of the sensor using ground control points (GCPs) and the experimental generation of DSMs from stereo observation images using the calibrated parameters.

This paper presents a novel hybrid calibration pipeline that jointly estimates the spatial and temporal alignment between a handheld laser scanner and a smartphone camera without any hardware synchronization. The method combines deep-learning-based target detection with classical geometric calibration using 2D-3D correspondences derived from black and white hourglass planar targets. Target centers are precisely localized in both the RGB images and the 3D point cloud using a symmetric templatematching scheme, enabling robust solution of the perspective-n-point (PnP) problem for spatial calibration.

To address the lack of hardware synchronization, we introduce a temporal calibration method that exploits geometric correspondences between rendered intensity images and camera frames.

On a Lixel L2 Pro scanner with a Huawei P20 Pro camera, the pipeline achieves a median Reprojection error of 0.76 px for static calibration and 2.19 px across 91 dynamic evaluations. The approach enables accurate image-pointcloud fusion for scanners without syncronisation interfaces and provides a foundation for colorization, image analysis, and redensification of laser data.

LunarDEM2025 is a lunar topography model (±60°) created by fusing JAXA’s SLDEM2013, CAS’s CE2TMap2015 and NASA’s LOLA laser altimetry tracks. A tile-based, terrain-aware co-registration aligns photogrammetric DEMs to LOLA points, while a slope-constrained residual-compensation filter eliminates striping, voids and artefacts. The resulting dataset shows visibly smoother relief, smaller vertical biases and fewer tile-boundary discontinuities than its predecessor SLDEM2015. The product is ready for landing-site analysis, rover path planning and various other applications.

After completing the 1:2,500,000-scale (1:2.5 M) lunar geologic atlas, our team began exploring the techniques and work flows for compiling larger scale lunar geological maps. Geologic maps integrate multidimensional information such as lithology, structure, and geologic age. Using the Copernicus crater region (0°–16°N, 30°W–10°W) as a case study, this research develops a 1:1,000,000-scale (1:1 M) regional geologic map and, in turn, explores the lithologic and structural classification systems applicable to lunar geologic maps at different scales. Based on imagery, topography, spectral, gravity, and sample data, we analyze geologic features including impact craters, impact basins, compositions, and structures, and subsequently delineate geological units. In the study area, the Copernicus crater and Imbrium basin represent the most prominent geological events and can serve as benchmarks for relative age determination. The cross-cutting relationships among geological units, together with existing absolute age constraints (from isotopic dating and crater size-frequency distribution chronology), are used to establish the stratigraphic relationships among mapped features and layers, ultimately producing a regional geologic map. Based on this map, the geological evolution history of the region is reconstructed.

High-accuracy planetary mapping products are increasingly required for landing-site assessment, precision navigation, and future surface operations on the Moon and Mars. Although massive orbital remote sensing images are available, the geometric accuracy and spatial resolution of many existing mapping products is still insufficient for engineering applications. A major bottleneck is large-scale bundle adjustment, whose reliability is strongly affected by data quality, control network strength, as well as engineering experience. Compared with Earth observation photogrammetry, planetary mapping faces great challenges such as heterogeneous sensor models, complex illumination, sparse absolute control. This paper summarizes a practical quality control framework for large-scale bundle adjustment of planetary remote sensing images. The workflow is divided into four coupled stages: data preprocessing, control network construction, parameter setting, and accuracy evaluation. The framework is distilled from previous planetary mapping studies, open-source software platforms and our practical experience in processing tens of thousands of planetary images. Experiments using LRO NAC datasets demonstrate that satisfactory bundle adjustment results can be achieved when the proposed strategy is applied. The framework improves the overall efficiency, controllability, and reliability of large-scale planetary photogrammetric processing.

China has successfully carried out the Chang'e-1 to Chang'e-6 lunar missions and the Tianwen-1 Mars mission. In these missions, planetary photogrammetry and remote sensing technologies provide timely spatio-temporal information services across all phases of the missions, playing a crucial supporting role in ensuring the mission safety and scientific output. In the current era of artificial intelligence (AI), the deep integration of photogrammetry and remote sensing, geomatics, and artificial intelligence is gradually evolving into Spatio-Temporal Intelligence (STI). This paper presents an overview of the advances and applications of STI in China’s lunar and Mars explorations, and discuss the future directions of STI in deep space exploration.

Based on microwave radiometer (MRM) data from China's Chang'e (CE)-1/2 satellites, the Brightness Temperature Difference (TBD) technique offers a method for probing lunar regolith properties. However, its global application is compromised by systematic latitudinal biases and an unverified link to subsurface deposits.

This study introduces a novel parameter, the effective TBD (TBDeff), to overcome these limitations. The methodology first defines an equivalent TBD (eTBD), simulating the TBD for a location as if it were on the lunar equator to mitigate latitudinal effects. Recognizing inherent limitations in this simulation, a supplementary parameter (sup_TBD) is derived. TBDeff is then developed by integrating sup_TBD with the observed TBD (TBDobs) from CE-2 data.

Results demonstrate that TBDeff successfully removes latitudinal bias on a global scale, enabling clearer discrimination between lunar maria and highlands. Furthermore, extensive low-TBDeff signals in polar regions (>85°) suggest a new potential for detecting subsurface deposits in permanently shadowed areas.

Crucially, correlation analysis with (FeO+TiO₂) abundance reveals that TBDeff exhibits a significantly stronger relationship with regolith composition than traditional TBD or simple brightness temperatures (TB), especially at lower frequencies (reaching a correlation coefficient of 0.86 at 3.0 GHz). This confirms that (FeO+TiO₂) abundance is a key factor influencing the dielectric properties of subsurface materials, a effect previously obscured by latitudinal interference. The TBDeff method thus provides a more reliable tool for interpreting lunar composition from microwave data.

This study analyzed and compared in situ spectral obtained by the Chang’E-3(CE-3) and Chang’E-4(CE-4) rovers, laboratory spectra of Chang’E-5(CE-5) soils and remote sensing spectra. The remote sensing spectra exhibit significantly darker and shallower absorption features than laboratory or in situ spectra, reflecting highly weathered nature of the undisturbed lunar surface. The spectral upturn even just right >2 μm can be contributed by thermal emission, revealing micro-scale temperature variations and low thermal inertia of lunar soils. CE-5 sample spectra show significantly higher reflectance and absorption depths than in situ and remote sensing, indicating samples are fresh and couldn’t represent pristine/true lunar surface. The CE-5 samples provide a new ground truth for estimating the TiO2 content of young basalts, which have the largest uncertainty in TiO2 content. Contrary to traditional opinion, CE-3 in situ spectra revealed that the effect on the spectral slope caused by space weathering is wavelength-dependent: the visible slope (VS) decreases not increases. The optical effects of space weathering and TiO2 are identical: both reduce albedo and blue the spectra. This suggests that a new TiO2 abundance algorithm is needed.