Four-dimensional (4D) topographic datasets are increasingly available at high spatial and temporal resolution, particularly from permanent terrestrial laser scanning (PLS) time series. These data offer unprecedented opportunities to analyse rapid and complex morphological processes occurring in sandy coastal environments, such as sandbar welding or bulldozer activity, as well as their longer-term impacts on sandy beaches. However, studying these processes requires the extraction and recognition of recurrent topographical surface dynamics across time, which in turn demands novel, automated methods. This study presents a novel workflow that combines 4D objects-by-change (4D-OBCs) with unsupervised classification using Self-Organizing Maps (SOMs) and hierarchical clustering. Applied to a three-year PLS time series comprising 21 194 hourly point clouds, the method identifies 4412 instances of short-term surface dynamics. These are organized into two SOMs (64 nodes each) and further grouped into 31 clusters representing distinct dynamic types, such as berm deposition, large-scale backshore erosion, and human interventions (e.g., bulldozer activity). The classification results enable detailed spatiotemporal analyses of coastal morphodynamics. The SOM topology reveals seasonal patterns in surface activity, where, for example, winter is dominated by erosional activity over the whole beach but depositional activity mainly occurs in the intertidal area. The broader clusters facilitate interpretation of environmental responses and identification of changes in cross-shore zonation of types of dynamics, like berm formation. This approach demonstrates the potential of integrating PLS and unsupervised learning to characterize complex surface dynamics, through a fully automated extraction and classification workflow. While the interpretation of clusters and their relation to environmental variables in this study is performed through expert-based analysis, the methods provide a framework for targeted, data-driven investigation and prediction of morphodynamic processes in high-resolution 4D remote sensing datasets.

Floods are among the most severe consequences of climate change, causing significant damage across several sectors, including agriculture. Nevertheless, the assessment of agricultural flood damage remains limited, particularly in agriculturally intensive regions where timely support is crucial. This work proposes a data-driven approach for assessing crop flood damage through a machine learning classification framework applied to features derived from Earth Observation (EO) data, trained and tested on field-level damage data collected by agronomists. Specifically, we applied a Random Forest model to classify fields into three damage classes by integrating Sentinel-2–derived indices, topographic information, and flood extent maps. The analysis focused on the flood event that struck the Emilia-Romagna region (Italy) in May 2023, one of the costliest floods globally that year. The model was trained and tested on 412 fields, achieving an overall accuracy of 0.74, with precision, recall, and F1 score of 0.75, 0.74, and 0.74, each with a standard deviation of 0.04, indicating stable model performance. The model accurately identified high-damage fields, which were characterized by greater flood exposure, lower elevations, and pronounced declines in vegetation indices. However, it struggled to distinguish between no-damage and medium-damage fields, particularly for permanent crops, where damage often occurs beneath the canopy and flooded areas may be partially occluded. The main novelty of this work lies in the use of in situ crop damage assessments, enabling a data-driven estimation of flood impacts. These results have direct implications for policymakers: the framework relies on free EO data, providing a tool that can support post-event compensation and decision-making in flood-prone regions.

Rapid urbanization challenges urban micro-climates, strains resources and affects public health. Understanding micro-climate dynamics is key to effective mitigation and sustainable development. Local Climate Zone (LCZ) classification supports climate-resilient planning but is complicated by the diversity and complexity of diverse urban landscapes and the coexistence of varying land uses and materials within small areas. While LCZ classification typically uses multispectral imagery, LiDAR, and land-use data, these sources often miss temporal thermal dynamic patterns. Thermal satellite imagery improves LCZ classification by distinguishing zones with similar structures but differing thermal behavior. This research proposes using deep learning-based multitemporal semantic segmentation to classify urban LCZs based solely on temporal thermal patterns from ECOSTRESS satellite imagery. The methodology is applied in a in a case study around the near coastal cities of Rotterdam and The Hague in The Netherlands and demonstrates how spatial and temporal factors (both diurnal and seasonal) influence the performance of the semantic segmentation model on different LCZ classes. The study shows that a U-Net architecture applied on spatio-temporal thermal imagery effectively classifies urban LCZs, achieving a test accuracy of 0.75. Temporal factors significantly impact model performance, with higher accuracies observed for daytime (0.8) and Spring/Summer imagery (0.78), as these conditions provide clearer thermal separability for distinguishing LCZs. The model achieved its highest test accuracy (0.83) when trained and tested on thermal images with the highest LST values. This suggests that focusing on high-value LST images with sufficient variability enhances classification performance compared to a generalized approach using the full dataset.

Dutch beaches are increasingly urbanized with both permanent beach pavilions and seasonal sheds and holiday houses. The effect of these buildings on long term dune development between 1999 and 2024 is studied in this paper along ~ 100 km of coast on the outer delta in the south western part of the Netherlands. A total of ~ 7000 beach buildings have been manually identified in this period based on satellite images and the time line function of Google earth desktop. The effect of the buildings is determined and analyzed at 477 cross-shore profiles with dune volumes and properties like dune toe, top and heel based on airborne lidar datasets of 1999 and 2024. On natural beaches the dune toe position is derived from profile information, whereas on urbanized beaches near buildings the dune toe is based on the location of the buildings. Yearly volume changes at the profile locations vary between -10 m³/m/y and up to 40 m³/m/y. The results indicate that smaller and standalone buildings allow for larger variations in dune volume changes and suggest that larger buildings and connected buildings impede natural dune dynamics which could impact coastal resilience in the long run.

The causal drivers of short-term changes (days to months) in human-, wind-, and wave-driven sand transport on a sandy beach are not often considered in an integral and data-driven approach. However, improving current knowledge on (urban) sandy beach topographical change requires the incorporation of multi-scale, cross-sectional and human factors. In this research we process a time series of 21,194 hourly point clouds, obtained in a Permanent Terrestrial Laser Scanning setup. From this 3D time series we extract 5,102 short-term temporary surface dynamics, through a method called 4D objects-by-change (4D-OBCs). The causal drivers of two of these 4D-OBCs are investigated in detail. One is interpreted as an aeolian depositional surface dynamic (1), and one as a bulldozer deposit, that consecutively eroded under high wave energy conditions (2). The dynamics show clear correlation to a particular combination of wind direction and intensity (1), and wave height and wave period (2), indicating that point cloud time series derived 4D-OBCs are useful data to study causality of short-term surface dynamics of different origins. However, to study these surface dynamics systematically and derive statistical proof of causal relations we must consider multivariate correlations, as well as spatiotemporal dependence between sediment dynamics and larger scale morphological changes on the beach.

Fitting a smooth curve to 2D, a surface to 3D, and a manifold to 4D irregular point cloud data is becoming a common practice in many engineering and science applications. Piecewise-polynomial spline functions provide a powerful tool applicable to interpolation and approximation problems. This study presents the least squares B-spline approximation (LSBSA) theory, which is a generalized version of the spline interpolation and can be applied to any irregularly scattered point cloud data at knots specified by the user. The formulation allows to apply the well-established body of knowledge of least squares theory to the B-spline approximation. This for example has the benefit of embedding quality control measures such as hypothesis testing and proper error propagation to assess the quality of the approximation problem. The method is applicable to many 1D curve, 2D surface and 3D manifold fitting problems of which both simulated and real data are used to illustrate the efficacy of the proposed theory. In particular, its real-world applications to multi-beam echo-sounder bathymetric data, digital terrain modeling and Greenland ice sheet deformation monitoring will be highlighted. The performance of the method for linear, quadratic, cubic and quartic spline functions will be investigated. The primary application of LSBSA lies in its ability to perform 3D manifold fitting for deformation monitoring. This capability provides the possibility of monitoring changes in continuous spatial and temporal domains. The Python and Matlab source codes of LSBSA are freely accessible at https://github.com/tudelft4d/lsbsa.

Monitoring freshwater resources is essential for assessing the impacts of drought, water management and global warming. Spaceborne LiDAR altimeters allow researchers to obtain water height information, while water area and precipitation data can be obtained using different satellite systems. In our study, we examined 5 years (2018–2022) of data concerning the Euphrates–Tigris Basin (ETB), one of the most important freshwater resources of the Middle East, and the water bodies of both the ETB and the largest lake of Türkiye, Lake Van. A multi-sensor study aimed to detect and monitor water levels and water areas in the water scarcity basin. The ATL13 product of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was used to determine water levels, while the normalized difference water index was applied to the Sentinel-2 optical imaging satellite to monitor the water area. Variations in both water level and area may be related to the time series of precipitation data from the ECMWF Reanalysis v5 (ERA5) product. In addition, our results were compared with global HydroWeb water level data. Consequently, it was observed that the water levels in the region decreased by 5–6 m in many reservoirs after 2019. It is noteworthy that there was a decrease of approximately 14 m in the water level and 684 km2 in the water area between July 2019 and July 2022 in Lake Therthar.

Arsenic contamination in shallow aquifers of Holocene alluvial basins is a serious health risk affecting millions of people [1]. Detection of arsenic hotspots is a slow and tedious process based on the analysis of groundwater samples. This study improves arsenic risk prediction by incorporating geomorphological features such as oxbow lakes and clay plugs into a machine learning (ML) approach. Advances in remote sensing [2], often combined with ML, enable the efficient detection of these and other proxy features, significantly reducing reliance on labour-intensive fieldwork. By combining these features with environmental and demographic data, the approach provides more accurate and cost-effective risk assessments, enabling better-targeted interventions in vulnerable regions and supporting proactive environmental monitoring.

The production of cocoa beans contributes to 7.5 % of European Union (EU) driven deforestation. As a result, the recent European Union Deforestation-free Regulation (EUDR) mandates producers to track cocoa farm extents comprehensively. While Remote Sensing has enormous capacity in dynamic crop monitoring, cocoa crop detection shows challenges due to cocoa complex canopy structure, spectral similarity to forest, variable farming methods, and location in frequently cloudy regions. Previous research on cocoa crop detection has mainly focused on pixel-based classification, disregarding spatial context. In this research we have performed a semantic segmentation approach to incorporate spatial configuration and enhance cocoa crop detection. We have applied Convolutional Neural Network (CNN) for the to semantic segmentation of cocoa parcels, considering both spectral and spatial characteristics. Additionally, we have evaluated the impact of combining Synthetic Aperture RADAR (SAR) and MSI (Multi-Spectral Imagery) data in the training of a CNN to demonstrate the importance of texture, moisture, and canopy characteristics in identifying cocoa canopies. The impact of MSI dataset stack with different SAR polarizations, seasons and temporality has been evaluated. The methodology is tested on Sentinel 1 and 2 data over an area of 100 × 100 km in Ghana for which an extensive ground truth data set of almost 90,000 polygons was available for training and validation. The results show that the addition of single-day and temporal SAR to a single-day MSI image can improve the predictions, reaching an F1 score of 86.62 %. This research demonstrates the influence of SAR measurements, seasons, polarization, and ground truth classes on the semantic segmentation of cocoa.

Operational forest fire danger rating systems uses meteorological variables to estimate vegetation conditions and predict fire occurrence and spread. This study introduces a novel approach to relate live fuel conditions retrieved from MODIS optical and thermal bands with fire behaviour and the probability of extreme events. The analysis focusses on land surface temperature (LST) anomaly and on the perpendicular moisture index (PMI) to evaluate fire characteristics like burned area, duration, and rate of spread. Results show that PMI is a strong covariate of burned area and rate of spread but not fire duration, while LST anomaly is a strong covariate of burned area and fire duration, and a weak covariate of rate of spread. Comparing these findings with the Canadian forest fire weather index (FWI) system components reveals that LST anomaly and PMI are effective predictors of fire characteristics, potentially enhancing fire danger models and preparedness strategies.

Satellite-derived bathymetry (SDB) provides a cost-effective solution for coastal mapping, but challenges remain in model interpretability and uncertainty quantification. This study investigates the applicability of the least-squares-based deep learning (LSBDL) framework for SDB, leveraging its hybrid structure that integrates neural networks with the available least-squares theory to enhance model transparency. ICESat-2 photon-counting LiDAR was used to train depth estimation from Sentinel-2 multispectral imagery over an approximately 30 km × 30 km region of near-coastal bathymetry at Anegada, British Virgin Islands. ICESat-2 provided high-precision depth information, of which 80% were used for training and the remainder for validation. LBSDL depth estimation achieved a root-mean-square error (RMSE) of 2.74 m, representing around 10% of the maximum observed depth, with the best performance in the 2–15 m depth range. These findings demonstrate the potential of LSBDL for interpretable and reliable bathymetric mapping, highlighting ICESat-2 as a globally accessible training and validation source and advancing SDB capabilities for data-sparse coastal regions.

Many topographic scenes exhibit complex dynamic behavior that is difficult to map, quantify, predict and understand. A terrestrial laser scanner fixed on a permanent position can be used to monitor such scenes in an automated way with centimeter to decimeter quality at ranges of up to several kilometers. Laser scanners are active sensors, and are therefore able to continue operation during night. Their independence from texture conditions ensures that in principle they provide stable range measurements for varying surface conditions. Recent years have seen a strong increase in the employment of such systems for different scientific applications in geosciences, environmental and ecological sciences, including forestry, glaciology, and geomorphology. At the same time, this employment resulted in a new type of 4D topographic data sets (3D point clouds + time) with a significant temporal dimension, as systems are now able to acquire thousands of consecutive epochs in a row. Extracting information from these 4D data sets turns out to be challenging, first, because of insufficient knowledge on error budget and correlations, and, second, because of lack of algorithms, benchmarks, and best-practice workflows. This paper provides an overview of different 4D systems for near-continuous laser scanning, and discusses systematic challenges including instability of the sensor system, meteorological and atmospheric influences, and data alignment, before discussing recently developed methods and scientific software for extracting and parameterizing changes from 4D topographic data sets, in connection to the different applications.

Historical aerial imagery provides valuable data from regions and periods with limited geospatial information. A common method to utilize this data is through the generation of ortho-photos and 3D models using Structure-from-Motion (SfM) techniques. However, many of these images were scanned decades after their acquisition and require geometric calibration, along with internal and external camera parameter estimation, for accurate reconstruction. Manual identification of key features, such as fiducial marks and text annotations, is labour-intensive, while existing automated methods struggle with poor-quality datasets. This paper presents an automated workflow that combines computer vision and machine learning techniques to detect and extract these key features from historical aerial images. To address challenges related to image quality, we also introduce estimation protocols that compensate for missing or unreliable detections by leveraging redundancy across multiple flight paths. The methodology was evaluated on the TMA (Trimetrogon Aerial) archive, a collection of historical images from the Antarctic Peninsula. Our test dataset comprised over 7000 images from 20 different flight paths. The workflow demonstrated high success rates in detecting and extracting fiducial marks, image subsets, and textual annotations. Approximately 70% of the images provided usable focal length data, while fiducial mark detection exhibited high accuracy except in cases of severe scanning artifacts. Altitude data extraction proved to be the most challenging, with successful results in only 15% of images due to degraded altimeter readings. Despite these limitations, the automated workflow effectively estimated missing parameters, ensuring robust image reconstruction across flight paths. The code for this workflow is open-source and publicly available on GitHub at https://github.com/fdahle/hist_meta_extraction.

The TriMetrogon Aerial (TMA) archive is an archive of historical images of Antarctica taken by the US Navy between 1940 and 2000 with analogue cameras. The analysis of such historic data can give a view of Antarctica's glaciers predating modern satellite imagery and provide unique insights into the long-term impact of changing climate conditions with essential validation data for climate modelling. However, the lack of semantic information for these images presents a challenge for large-scale computer-driven analysis. Such information can be added to the data using semantic segmentation, but traditional algorithms fail on these scanned historical grayscale images, due to varying image quality, lack of colour information and artefacts in the images. To address this, we present a deep-learning-based U-net workflow. Our approach includes creating training data by pre-processing and labelling the raw images. Furthermore, different versions of the U-net are trained to optimize its hyperparameters and augmentation methods. With the optimal hyper-parameters and augmentation methods, a final model has been trained for a use-case to segment 118 images covering Adelaide Island. We tested our approach by segmenting challenging historical images using a U-net model with just 80 training images, achieving an accuracy of 73% for 20 validation images. While no test data is available for our use case, a visual examination of the segmented images shows that our method performs effectively. The comparison of the hyper-parameters and augmentation methods provides directions for training other U-net-based models so that the presented workflow can be used to segment other archives with historical imagery. Additionally, the labelled training data and the segmented images of the test are publicly available at https://github.com/fdahle/antarctic_segmentation.

The utility of historical image repositories is often limited due to the lack of geo-referencing. A good example is the TriMetrogon Aerial (TMA) archive, a collection of historical aerial images of Antarctica between 1940 and 2000. These images are difficult to use, as their geolocation is only approximately, with location errors in the order of tens of km. This study addresses this challenge by developing an automated geo-referencing workflow that leverages recent advancements in machine-learning-based tie-point matching. We use the algorithm LightGlue, to establish tie-points between geo-referenced Sentinel-2 satellite imagery and historical non-geo-referenced aerial images. To aid the process, we use already known approximate positions of the historical images. Due to the sub-optimal and inhomogeneous quality of the aerial images, only a portion of the images can be geo-referenced directly by matching. For the remaining images, we employed alternative means of geo-referencing, again based on tie-point matching. Out of a subset of 4,459 images located inside the research area, 3,393 images could be geo-referenced, a percentage of 76%. Reasons for the geo-referencing failing are insufficient contrast or an approximate position too far away from the real position. The workflow can easily be applied to historical images from other archives, to enhance the usability of historical image repositories for scientific research.

Inspired by the attractive features of least-squares theory in many practical applications, this contribution introduces least-squares-based deep learning (LSBDL). Least-squares theory connects explanatory variables to predicted variables, called observations, through a linear(ized) model in which the unknown parameters of this relation are estimated using the principle of least-squares. Conversely, deep learning (DL) methods establish nonlinear relationships for applications where predicted variables are unknown (nonlinear) functions of explanatory variables. This contribution presents the DL formulation based on least-squares theory in linear models. As a data-driven method, a network is trained to construct an appropriate design matrix of which its entries are estimated using two descent optimization methods: steepest descent and Gauss–Newton. In conjunction with interpretable and explainable artificial intelligence, LSBDL leverages the well-established least-squares theory for DL applications through the following three-fold objectives: (i) Quality control measures such as covariance matrix of predicted outcome can directly be determined. (ii) Available least-squares reliability theory and hypothesis testing can be established to identify mis-specification and outlying observations. (iii) Observations’ covariance matrix can be exploited to train a network with inconsistent, heterogeneous and statistically correlated data. Three examples are presented to demonstrate the theory. The first example uses LSBDL to train coordinate basis functions for a surface fitting problem. The second example applies LSBDL to time series forecasting. The third example showcases a real-world application of LSBDL to downscale groundwater storage anomaly data. LSBDL offers opportunities in many fields of geoscience, aviation, time series analysis, data assimilation and data fusion of multiple sensors.

Coastal dunes provide an important role to society by fulfilling various ecosystem functions. These are however under pressure due to sea level rise, climate change and urbanization. Shoreward sand transport can partly mitigate sea level rise and climate effects by contributing to dune growth but increasing urbanization of beaches can block this transport and restoration of ecosystem functions. In this paper, we investigate the long-term effects of buildings and other anthropogenic influences on dune development up to a decadal temporal scale.

A series of three topographic datasets is used to study the effect of anthropogenic actors on local dune development at an urban beach in Noordwijk, the Netherlands. Datasets range from a 100 to 3000-meter spatial scale and from a weekly to yearly temporal scale. On the smallest spatio-temporal scale topographic measurements of the effects of two containers placed on Noordwijk beach are studied. The intermediate dataset is obtained from the 2-year CoastScan project monitoring surface elevation around one beach pavilion at (bi) monthly intervals. Finally, 15 years of annual airborne lidar data along a 2.7-kilometer stretch of the beach/dune system in Noordwijk is used to evaluate the effect of 17 pavilions.

The small-scale experiment shows that horseshoe-shaped deposition patterns developed on the leeside of the containers. These depositions follow daily wind changes and leave deposits corresponding to the residual wind direction over the whole measuring period. Similar patterns are found around the beach pavilion, but, due to anthropogenic influences like bulldozing and beach shaping, longer term patterns in the direct vicinity of the pavilion and the dunes are hard to discern.

Evaluation of the longer term dataset reveals large variations in dune height and volume in the neighborhood of beach pavilions. Dune height/volume increases vary between 1-8 m in dune height and vary between 0-200 m3 in dune volume after 15 years along 2.7 km of coast. An autocorrelation analysis shows that the alongshore variability length scale in dune volume of urbanized dunes can be 10 times smaller than for natural dunes. For about half the beach pavilions, variations in dune height and volume are significantly correlated to the location of the beach pavilion. Here the growth behind the buildings is lower than in the surrounding area which might have consequences for long-term resilience against future climate changes.

Sandy beach-dune systems make up a large part of coastal areas world wide. Their function as an eco-system as well as a protective barrier for human and natural habitat is under increased threat due to climate change. A thorough understanding of change processes at the sediment surface is essential to facilitate prediction of future development and management strategies to maintain their function. Especially slow and small scale processes happening over several days up to weeks at cm level, such as aeolian sand transport are difficult to identify and analyse. Permanent laser scanning (PLS) is a useful tool in the study and analysis of coastal processes as it captures a data representation of the evolution of the sediment surface over extended periods of time (up to several years) with high detail (at cm-dm level). The PLS data set considered for this study, consists of hourly acquired 3D point clouds representing the surface evolution of a section of the Dutch coast during three years. However, it is challenging to extract concrete information on specific change processes from the large and complex PLS data set. We use multiple hypothesis testing in order to reduce the PLS data set to a so-called inventory of trends, consisting of 12.8 million partial time series with associated rate of change and elevation. The inventory of trends proofs to be a suitable tool to identify natural processes such as storms and aeolian sand transport in our test area in the aeolian zone of a sandy beach-dune system on the Dutch coast. We identify these processes and provide a tool to derive summarising data from the complex PLS data set. We find that all partial time series identified as most likely representing aeolian sand transport, result in 1354 m³ of sand deposition in our study area over the course of three years. We also show a comparison with transects from JarKus data and find a correlation between anthropogenic activities and erosion in our test area with a correlation coefficient of 0.3.

Shoreward sand transport and dune development are increasingly influenced by the urbanization of beach-dune systems in the Netherlands. Three topographic datasets, on various spatio-temporal scales, are used to study the effect of standalone buildings on long term local dune development. On the smallest scale, terrestrial laser scans are used to study the geomorphological effects of two sea containers on the beach. On the intermediate scale, the geomorphological effects of a beach pavilion on the local dune development are studied with a 2-year topographic dataset of (bi) monthly permanent laser scans. Finally, 15 yearly airborne lidar scans of the beach-dune system in Noordwijk are used to evaluate the effect of multiple beach pavilions on dune growth variations. The small-scale experiment shows that horseshoe-shaped deposition patterns developed on the leeside of the containers. These depositions follow daily wind changes and leave deposits corresponding to the residual wind direction over the whole measuring period. Similar patterns are found around the larger beach pavilion, but anthropogenic activities like bulldozing and beach shaping make the determination of the effect on dune development harder to discern. Evaluation of the longer-term dataset reveals large variations in dune height and volume around beach pavilions. Dune height/volume increases vary between 1 and 8 m in height and 0–200 m3 in volume. A variability analysis shows that the length scale of alongshore variability in dune height/volume of urbanized dunes can be 10 times smaller than for natural dunes. For about half the beach pavilions, variations in dune height and volume are significantly correlated to the location of beach pavilions but correlation to particular beach pavilion properties is yet inconclusive.

The paper presents a new dry-dock method for assessing the deformation of submarine hulls using TLS point cloud data and the point cloud spatial expansion method (PCSE). The advantage of the proposed approach is the high-resolution deformation analysis that can be conducted in the case of both the availability and a lack of technical documentation on the submarine hull. The geometry assessment involves two-plane hull symmetry in longitudinal sections of a tested Kobben-class submarine located in Gdynia, Poland. The features of PCSE introduce additional geometrical parameters that are not available in the original point cloud method. The procedure for local fitting of a plane into the expansion eliminates the problem of varying densities of the hull point cloud. Accuracies of several millimetres are achieved and are applicable to multi-temporal monitoring of the deformations of submarine hulls. The assessment of similar deformations is not possible in the original point cloud method, due to the unknown parameters of the orientation and curvature of the convex cylindrical hull surface. The PCSE-based parameterisation presented here enables the creation of alternative quasi-planar point cloud projections to extract new spatial information on the object. The results of this study were verified using theoretical values derived from design data on the Kobben-class submarine, and demonstrated the effectiveness of our method in terms of detecting deformations even without design references. The proposed methodology is uniform, and can be adapted to other symmetrical structures.