Xabier Blanch

The increasing frequency of extreme hydrological events, driven by climate change, necessitates dense and scalable water level monitoring networks. We evaluate a low-cost, non-contact, stereo-vision camera system for automated water level estimation. We compare two distinct image-processing pipelines — with and without semantic masking — to determine their camera pose stability and measurement accuracy. Our results show that raw stereo-vision estimates are highly correlated with reference sensor measurements (correlations ranging from 0.70 to 0.77), capturing the overall hydrologic behavior. By implementing a masking technique to isolate static environmental features, we successfully corrected a baseline error (i.e., offset), aligning the system with the true physical geometry. Although masking improves absolute accuracy, it introduces transient instability (i.e., spikes in pose estimation). This study serves as a proof of concept for the deployment of low-cost, edge-based stereo-vision systems in hydrological monitoring.

To robustly support glacier calving monitoring at high temporal resolution and enable future AI-based calving forecasts, this study presents an optimized Multi-Epoch Multi-Imagery (MEMI) strategy for automated 4D point cloud model generation. To date, the dataset comprises over 160,000 images acquired since December 2024 by an autonomous multi-camera system operating at 30 min intervals at Glacier Perito Moreno (GPM), Argentina. Despite high scene variability and harsh environmental conditions, the proposed MEMI workflow effectively addresses constraints imposed by continuous glacier motion and image degradation. The enhanced strategy aims to generate precise dense clouds with high alignment accuracy and computational efficiency, forming the basis for subsequent analysis of glacier front evolution. To achieve this, various parameter configurations are evaluated, including AI-based image masking and adaptive, optimized alignment-adjustment settings. Results from a representative eight-day subset show that variations in the tie point computation strategy lead to measurable differences in alignment-adjustment efficiency, with the best configuration being about 11 % faster than the least efficient one. By contrast, adaptive alignment-adjustment consistently improves alignment accuracy. Moreover, masking enhances both image quality checking and reconstruction quality, and, albeit modestly, improves pre-failure deformation analysis. Furthermore, daily seasonal responses to alignment are observed, as accuracy varies with solar illumination relative to the camera positions. Applying the optimal configuration to 260 MEMI projects in under 42 h produced 518 high-precision dense clouds and detected calving retreat magnitudes of up to 17.5m, demonstrating the robustness and scalability of the proposed MEMI strategy for high-temporal-resolution 4D point cloud generation.

Continuous seismic monitoring has proven effective for detecting rockfalls, yet most studies rely on multiple stations or dense arrays, increasing cost and complexity. This study demonstrates that a single seismic station, located approximately 100 m from the event site, can detect small rockfalls (

River bank erosion represents a dynamic geomorphic hazard, particularly in meandering channels where migration rates threaten critical infrastructure and agricultural land. While our previous work on the Sajó River (Hungary) established a novel, low-cost monitoring framework utilizing Raspberry Pi (RPi) cameras for near-continuous observation, the reliability of photogrammetric reconstruction under uncontrolled outdoor conditions remains a critical challenge. This study presents a systematic evaluation of the accuracy constraints inherent in automated Structure-from-Motion (SfM) processing pipelines, with a specific focus on optimizing image alignment across a wide range of scene conditions.To determine the robustness of RPi imagery, we conducted a comprehensive sensitivity analysis of the SfM-based image alignment phase. We systematically tested over 120 variations of processing parameters, manipulating keypoint and tie-point limits, upscaling factors, and masking strategies. The implementation of rigorous masking was critical, as the imagery is geometrically challenging: the moving river surface in the foreground and the sky in the background occupy the majority of the field of view, leaving only a narrow, static fraction of the image relevant for reliable 3D reconstruction. These combinations were evaluated against a dataset representing the full range of environmental variability, including clear, cloudy, dark, foggy, overexposed, and rainy conditions, as well as distinct hydrological states such as low flows, flood events, and snow cover.Preliminary results indicate that a specific balance of 30,000 keypoints and 5,000 tie points (ratio 6.0) optimizes reconstruction fidelity, achieving an RMS error of 0.75 pixels under clear weather conditions. Notably, the system demonstrated unexpected robustness in low-light scenarios, maintaining consistent error margins of 1.17–1.18 pixels across various configurations. Conversely, scaling up these limits beyond the optimum yielded diminishing returns, confirming that higher computational loads do not necessarily equate to improved geometric accuracy. Furthermore, we applied gradual selection algorithms to filter sparse point clouds, removing unreliable points based on reconstruction uncertainty to isolate the most geometrically valid features.The crucial final phase of this research bridges the gap between digital reconstruction and physical reality. We validate the optimized SfM-based point clouds by comparing them directly against high-precision Terrestrial Laser Scanning (TLS) data acquired during two previous campaigns and upcoming field surveys. This multi-temporal comparison allows us to quantify specific error margins for volumetric and horizontal material displacement calculations. By defining these accuracy constraints, we establish a validated protocol for calculating erosion volumes during high-flow events, ensuring that automated, low-cost monitoring systems can provide actionable, high-precision data for river management even under adverse environmental conditions.---The research was funded by the DAAD-2024-2025-000006 project-based research exchange program (DAAD, Tempus Public Foundation).

In this study, we present a high temporal resolution (30-minute) multi-view stereo (MVS) photogrammetric dataset capturing the front of the lake-terminating Perito Moreno Glacier during daylight over more than one year. We aim to provide novel data to investigate glacier dynamics, which are strongly affected by climate change and pose increasing risks to ecosystems and human infrastructure. The images for the photogrammetric reconstruction are automatically inspected and prepared to create high-precision 4D (3D + time) point clouds, which are then compared per epoch to detect surface changes along an approx. 300 m wide study reach.The MVS system comprises eight DSLR cameras and uses 4G connectivity for daily data transfer, aiming for near-real-time monitoring. This has resulted in more than 75,000 images stored on a central server, which allows the calving analysis based on more than 5,000 models. To isolate image regions relevant for the generation of dense clouds with maximum precision across as many epochs as possible, the images are segmented using the artificial intelligence (AI)-based image segmentation model SAM2. The relevant regions are assessed using blur metrics, which identify low contrast caused by harsh glacier conditions, such as moisture or water droplets, thereby reducing the risk of including images of low quality that interfere with image correlation success.The results of our image pre-processing demonstrate that lighting conditions have the greatest impact on image segmentation performance. In contrast, the final model quality of the 4D point cloud reconstructions, which are based on a multi-epoch multi-imagery (MEMI) strategy, is mostly affected by the presence of adjacent dynamically changing regions, such as floating ice on the lake, highlighting the need for masking these regions. Applying masking further seems to improve the robustness of detecting subtle glacier surface changes, which is essential for pre-failure deformation analysis, providing valuable input for future calving prediction efforts.The point cloud sequences are analyzed using the Multiscale Model to Model Cloud Comparison (M3C2) algorithm to quantify surface changes. Although calibration parameters of focal length and principal point exhibit temporal variability, a constant calibration strategy is examined to ensure consistent alignment across all epochs, which furthermore enables the observation of glacier flow velocities in both horizontal and vertical directions. Initial results for a test period during a week in summer indicate that a constant calibration does not adversely affect model generation, suggesting that calibration stability may be sufficient under favorable summer conditions, while ongoing analyses will assess the robustness during winter months. Current efforts focus on refining dense cloud quality by separating glacier surfaces from points reconstructed on stable rock areas, which were intentionally retained during point cloud generation to provide stable reference regions for the MEMI workflow.Our introduced workflow enables the creation of a reliable calving inventory, exceeding the spatio-temporal resolution of conventional glacier monitoring techniques. In a next step, we aim to combine the created calving inventory with associated dynamic parameters, such as glacier velocity, and with environmental variables to support the development of AI-based calving prediction models.

The study presents a robust, automated camera gauge for long-term river water level monitoring operating in near real-time. The system employs artificial intelligence (AI) for the image-based segmentation of water bodies and the identification of ground control points (GCPs), combined with photogrammetric techniques, to determine water levels from surveillance camera data acquired every 15 min. The method was tested at four locations over a period of more than 2.5 years. During this period almost 218 000 images were processed. The results demonstrate a high performance, with mean absolute errors ranging from 0.96 to 2.66 cm in comparison to official gauge references. The camera gauge demonstrates resilience to adverse weather and lighting conditions, achieving an image utilisation rate of above 95 % throughout the entire period. The integration of infrared illumination enabled 24/7 monitoring capabilities. Key factors influencing absolute error were identified as camera calibration, GCP stability, and vegetation changes. The low-cost, non-invasive approach advances hydrological monitoring capabilities, particularly for flood detection and mitigation in ungauged or remote areas, enhancing image-based techniques for robust, long-term environmental monitoring with frequent, near real-time updates.

Soil erosion represents a major global threat, necessitating a detailed understanding of its spatial and temporal dynamics. Advanced geospatial technologies such as time-lapse structure-from-motion (SfM) photogrammetry provide high-resolution monitoring of surface changes. This study presents a novel event-driven approach for near-continuous monitoring of hillslope surface dynamics over a multi-annual period. The system employed synchronized digital single-lens reflex cameras at three slope stations, triggered by a rain gauge and a daily timer. Ground control points (GCPs) were surveyed with millimeter accuracy to ensure precise georeferencing. An automated Python-based workflow was developed to synchronize images, detect GCPs using a convolutional neural network, generate daily digital 3D surface models via SfM, and compute 3D surface models of difference. The absolute accuracy of SfM point clouds ranged between 8 and 12 mm on average, primarily due to registration errors, with lower deviations (<5 mm) in central areas after height adjustment. Relative accuracy decreased concentrically with distance from the cameras, with level of detection values between 5 and 25 mm depending on distance and location. Time series analysis revealed surface changes driven by rainfall, snowmelt, and agricultural activity. The most significant changes often occurred shortly after tillage, even with minimal rainfall, indicating both erosional and non-erosional processes. A strong negative correlation between rainfall and elevation loss was especially evident within the first seven days following tillage. Seasonal surface lowering of 3–5 cm during winter and occasional positive changes due to frost or vegetation growth were also observed. The monitoring system and workflow are transferable, and the resulting high-resolution datasets are expected to be valuable for analyzing erosion dynamics and testing process-oriented soil erosion models.

Soil erosion represents a major global threat, necessitating a detailed understanding of its spatial and temporal dynamics. Advanced geospatial technologies such as time-lapse structure-from-motion (SfM) photogrammetry provide high-resolution monitoring of surface changes. This study presents a novel event-driven approach for near-continuous monitoring of hillslope surface dynamics over a multi-annual period. The system employed synchronized DSLR (digital single-lens reflex) cameras at three slope stations, triggered by a rain gauge and a daily timer. Ground control points (GCPs) were surveyed with millimeter accuracy to ensure precise georeferencing. An automated Python-based workflow was developed to synchronize images, detect GCPs using a convolutional neural network (CNN), generate daily digital 3D surface models via SfM, and compute 3D surface models of difference (DoDs). The absolute accuracy of SfM point clouds ranged between 8 mm and 12 mm on average, primarily due to registration errors, with lower deviations (< 5 mm) in central areas after height adjustment. Relative accuracy decreased concentrically with distance from the cameras, with level of detection (LoD) values between 5 mm and 25 mm depending on distance and location. Time series analysis revealed surface changes driven by rainfall, snowmelt, and agricultural activity. The most significant changes often occurred shortly after tillage, even with minimal rainfall, indicating both erosional and non-erosional processes. A strong negative correlation between rainfall and elevation loss was especially evident within the first seven days following tillage. Seasonal surface lowering of 3–5 cm during winter and occasional positive changes due to frost or vegetation growth were also observed. The monitoring system and workflow are transferable, and the resulting high-resolution datasets are valuable for analyzing erosion dynamics and validating soil erosion models.

Soil erosion represents a major global threat, necessitating a detailed understanding of its spatial and temporal dynamics. Advanced geospatial technologies such as time-lapse structure-from-motion (SfM) photogrammetry provide high-resolution monitoring of surface changes. This study presents a novel event-driven approach for near-continuous monitoring of hillslope surface dynamics over a multi-annual period. The system employed synchronized DSLR (digital single-lens reflex) cameras at three slope stations, triggered by a rain gauge and a daily timer. Ground control points (GCPs) were surveyed with millimeter accuracy to ensure precise georeferencing. An automated Python-based workflow was developed to synchronize images, detect GCPs using a convolutional neural network (CNN), generate daily digital 3D surface models via SfM, and compute 3D surface models of difference (DoDs). The absolute accuracy of SfM point clouds ranged between 8 mm and 12 mm on average, primarily due to registration errors, with lower deviations (< 5 mm) in central areas after height adjustment. Relative accuracy decreased concentrically with distance from the cameras, with level of detection (LoD) values between 5 mm and 25 mm depending on distance and location. Time series analysis revealed surface changes driven by rainfall, snowmelt, and agricultural activity. The most significant changes often occurred shortly after tillage, even with minimal rainfall, indicating both erosional and non-erosional processes. A strong negative correlation between rainfall and elevation loss was especially evident within the first seven days following tillage. Seasonal surface lowering of 3–5 cm during winter and occasional positive changes due to frost or vegetation growth were also observed. The monitoring system and workflow are transferable, and the resulting high-resolution datasets are valuable for analyzing erosion dynamics and validating soil erosion models.

Soil erosion represents a major global threat, necessitating a detailed understanding of its spatial and temporal dynamics. Advanced geospatial technologies such as time-lapse structure-from-motion (SfM) photogrammetry provide high-resolution monitoring of surface changes. This study presents a novel event-driven approach for near-continuous monitoring of hillslope surface dynamics over a multi-annual period. The system employed synchronized DSLR (digital single-lens reflex) cameras at three slope stations, triggered by a rain gauge and a daily timer. Ground control points (GCPs) were surveyed with millimeter accuracy to ensure precise georeferencing. An automated Python-based workflow was developed to synchronize images, detect GCPs using a convolutional neural network (CNN), generate daily digital 3D surface models via SfM, and compute 3D surface models of difference (DoDs). The absolute accuracy of SfM point clouds ranged between 8 mm and 12 mm on average, primarily due to registration errors, with lower deviations (< 5 mm) in central areas after height adjustment. Relative accuracy decreased concentrically with distance from the cameras, with level of detection (LoD) values between 5 mm and 25 mm depending on distance and location. Time series analysis revealed surface changes driven by rainfall, snowmelt, and agricultural activity. The most significant changes often occurred shortly after tillage, even with minimal rainfall, indicating both erosional and non-erosional processes. A strong negative correlation between rainfall and elevation loss was especially evident within the first seven days following tillage. Seasonal surface lowering of 3–5 cm during winter and occasional positive changes due to frost or vegetation growth were also observed. The monitoring system and workflow are transferable, and the resulting high-resolution datasets are valuable for analyzing erosion dynamics and validating soil erosion models.