GM3.1 | From historical images to modern high resolution topography: methods and applications in geosciences
From historical images to modern high resolution topography: methods and applications in geosciences
Co-organized by BG2/CR5/GI1/SSS10
Convener: Amaury Dehecq | Co-conveners: Katharina AndersECSECS, Anette EltnerECSECS, Livia PiermatteiECSECS, Benoît Smets
| Mon, 15 Apr, 08:30–10:15 (CEST)
Room G1
Posters on site
| Attendance Tue, 16 Apr, 10:45–12:30 (CEST) | Display Tue, 16 Apr, 08:30–12:30
Hall X3
Orals |
Mon, 08:30
Tue, 10:45
Recent advances in image collection, e.g. using unoccupied aerial vehicles (UAVs), and topographic measurements, e.g. using terrestrial or airborne LiDAR, are providing an unprecedented insight into landscape and process characterization in geosciences. In parallel, historical data including terrestrial, aerial, and satellite photos as well as historical digital elevation models (DEMs), can extend high-resolution time series and offer exciting potential to distinguish anthropogenic from natural causes of environmental change and to reconstruct the long-term evolution of the surface from local to regional scale.
For both historic and contemporary scenarios, the rise of techniques with ‘structure from motion’ (SfM) processing has democratized data processing and offers a new measurement paradigm to geoscientists. Photogrammetric and remote sensing data are now available on spatial scales from millimetres to kilometres and over durations of single events to lasting time series (e.g. from sub-second to decadal-duration time-lapse), allowing the evaluation of event magnitude and frequency interrelationships.
The session welcomes contributions from a broad range of geoscience disciplines such as geomorphology, cryosphere, volcanology, hydrology, bio-geosciences, and geology, addressing methodological and applied studies. Our goal is to create a diversified and interdisciplinary session to explore the potential, limitations, and challenges of topographic and orthoimage datasets for the reconstruction and interpretation of past and present 2D and 3D changes in different environments and processes. We further encourage contributions describing workflows that optimize data acquisition and processing to guarantee acceptable accuracies and to automate data application (e.g. geomorphic feature detection and tracking), and field-based experimental studies using novel multi-instrument and multi-scale methodologies. This session invites contributions on the state of the art and the latest developments in i) modern photogrammetric and topographic measurements, ii) remote sensing techniques as well as applications, iii) time-series processing and analysis, and iv) modelling and data processing tools, for instance, using machine learning approaches.

Session assets

Orals: Mon, 15 Apr | Room G1

Chairpersons: Amaury Dehecq, Anette Eltner, Benoît Smets
Modern high resolution topography
On-site presentation
Lander Van Tricht, Harry Zekollari, Matthias Huss, Philippe Huybrechts, and Daniel Farinotti

Uncrewed Aerial Vehicles (UAVs) are increasingly employed for glacier monitoring, particularly for small to medium-sized glaciers. The UAVs are mainly used to generate high-resolution Digital Elevation Models (DEMs), delineate glacier areas, determine surface velocities, and map supraglacial features. In this study, we utilise UAVs across various sites in the Alps and the Tien Shan (Central Asia) to monitor the mass balance of glaciers. We present a workflow for calculating the annual geodetic mass balance and obtaining the surface mass balance using the continuity-equation method. Our results demonstrate generally a close alignment between the determined mass balances and those obtained through traditional glaciological methods involving intensive fieldwork. We show that utilising UAV data reveals significantly more spatial details, such as the influence of debris and collapsing ice caves, which are challenging to capture using conventional methods that strongly rely on interpolation and extrapolation. This underscores the UAV's significance as a valuable add-on tool for quantifying annual glacier mass balance and validating glaciological assessments. Drawing on our experience in on-site UAV glacier surveys, we discuss the methodology's advantages, disadvantages, and potential pitfalls. 

How to cite: Van Tricht, L., Zekollari, H., Huss, M., Huybrechts, P., and Farinotti, D.: UAV’s to monitor the mass balance of glaciers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22358,, 2024.

On-site presentation
Alexandre Rétat, Nathalie Thommeret, Frédéric Gob, Thomas Depret, Jean-Stéphane Bailly, Laurent Lespez, and Karl Kreutzenberger

The European Water Framework Directive (WFD), adopted in 2000, set out requirements for a
better understanding of aquatic environments and ecosystems. In 2006, following the transposition of
the WFD into French law (LEMA), France began work on a field protocol for the geomorphological
characterization of watercourses, as part of a partnership between the Centre National de la Recherche
Scientifique (CNRS) and the Office Français de la Biodiversité (OFB). This protocol, known as "Carhyce"
(For « River Hydromorphological Caracterisation »), has been tested, strengthened and approved over
the last 15 years at more than 2500 reaches. It consists of collecting standardised qualitative and
quantitative data in the field, essential for the caracterisation of a watercourse: channel geometry,
substrate, riparian vegetation... However, certain rivers that are difficult to survey (too deep or too
wide) pose problems for data collection.
To address these issues, and to extend the analysis to a wider scale (full river section), using
remote sensing, and in particular LiDAR data, was considered. The major advantages of LiDAR over
passive optical sensors are better geometric accuracy and especially under vegetation. For a long time,
LiDAR data rarely exists at national scale with data density similar to passive imagery. Today, the French
LiDAR HD dataset (10 pulses per meter square) program run by the French mapping agency offers an
unprecedented amount of data at this scale. Thanks to them, a national 3D coverage of the ground can
be used, and numerous geomorphological measurements can be carried out on a more or less large
scale. This is the case for hydromorphological parameters such as water level and width.
The aim of this study is therefore to use this high-density lidar to automatically determine the
hydromorphological parameters sought in the Carhyce protocol. In particular, we have developed a
lidar-based algorithm to reconstruct the topography from point cloud and automatically identify the
bankfull level at reach scale. Designed to be applicable to every French river, the method must be
robust to all river features such as longitudinal slope, width, sinuosity, multi-channel etc... For
validation purposes, the bankfull geometry calculated by the algorithm has been compared with field
measurements at some twenty Carhyce stations across France. To determine the test stations, we
looked for the diversity of situations in terms of river characteristics describe above to observed the
influence of this features on the results.

How to cite: Rétat, A., Thommeret, N., Gob, F., Depret, T., Bailly, J.-S., Lespez, L., and Kreutzenberger, K.: Automatic detection of river bankfull parameters from high density lidar data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21396,, 2024.

Virtual presentation
Laure Guerit, Philippe Steer, Paul Leroy, Dimitri Lague, Dobromir Filipov, Jiri Jakubinsky, Ana Petrovic, and Valentina Nikolova

3D data for natural environments are now widely available via open data at large scales (e.g., OpenTopography) and can be easily acquired on the field by terrestrial LiDAR scan (TLS) or by structure-from-motion (SFM) from camera or drone imagery. The 3D description of landscapes gives access to an unprecedented level of details that can significantly change the way we look at, understand, and study natural systems. Point clouds with millimetric resolution even allow to go further and to investigate the properties of riverbed sediments: dedicated algorithms are now able to extract the sediment size distribution or their spatial orientation directly from the point cloud. 

Such data can be real game changers to study for example torrential streams prone to flash floods or debris flows. Such events are usually associated with heavy rainfall events, while conditioned by the geomorphological state of a stream (e.g., channel geometry, vegetation cover). The size and the shape of the grains available in the river also strongly influence river erosion and sediment transport during a flood. 3D data can thus help to design prevention and mitigation measures in streams prone to torrential events. 

However, it is not straightforward to go from data acquisition to river erosion or to grain-size distributions. Indeed, isolating and classifying the areas of interest can be complex and time-consuming. This can be done manually, at the cost of time and absence of reproducibility. We rather take advantage of state-of-the-art classification method (3DMASC) to develop a general classifier for point clouds in fluvial environments designed to identify five classes usually found in such settings: coarse sediments, sand, bedrock, vegetation and human-made structures. We also improved the G3Point sediment segmentation algorithm, developed by our team, to make it more efficient and straightforward to use in the CloudCompare software, which is dedicated to point cloud visualization and analysis. We apply it to the coarse sediments class identified by 3DMASC to provide a more accurate description of grain size and orientation. We also make a profit of the sand class to estimate its relative areal distribution that can then be compared to the coarse sediment class. This provides valuable information about the type of flows which are also important for planning torrential events mitigation measures.

We illustrate this combined approach with two field examples. The first one is based on SFM data acquired along streams prone to torrential events in Bulgaria and in Serbia where we documented sediment size and orientation. The second one is based on TLS data acquired along a bedrock river in France that experienced a major flood which induced dramatic changes in the river morphology. 

This work has been partially funded by PHC Danube n° 49921ZG/ n° KP-06-Danube/5, 14.08.2023 (National Science Fund, Bulgaria) and the H2020 European Research Council (grant no. 803721). 

How to cite: Guerit, L., Steer, P., Leroy, P., Lague, D., Filipov, D., Jakubinsky, J., Petrovic, A., and Nikolova, V.: Classification and segmentation of 3D point clouds to survey river dynamics and evolution , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15896,, 2024.

On-site presentation
Reuma Arav, Sagi Filin, and Yoav Avni

Examining deposition and erosion dynamics during the late Pleistocene and Holocene is crucial for gaining insights into soil development, erosion, and climate fluctuations. This urgency intensifies as arable lands face escalating degradation rates, particularly in arid and semi-arid environments. Nevertheless, as the destructive nature of erosional processes allows only for short-term studies, long-term processes in these regions are insufficiently investigated. In that respect, the ancient agricultural installations in the arid Southern Levant offer distinctive and undisturbed evidence of long-term land dynamics. Constructed on a late Pleistocene fluvial-loess section during the 3rd-4th CE and abandoned after 600-700 years, these installations record sediment deposition, soil formation, and erosion processes. The challenge is to trace and quantify these processes based on their current state. In this presentation, we demonstrate how the use of 3D point cloud data enables us to follow past geomorphological processes and reconstruct trends and rates. Utilizing data gathered in the immediate vicinity of the UNESCO World Heritage Site of Avdat (Israel), we illustrate how these point clouds comprehensively document the history of soil dynamics in the region. This encompasses the initial erosion phase, subsequent soil aggradation processes resulting from anthropogenic interruption, and the ongoing reinstated erosion. The unique setting, which uncovers the different fluvial sections, together with the detailed 3D documentation of the site, allows us to develop means for the reconstruction of the natural environment in each of the erosion/siltation stages. Therefore, by utilizing the obtained data, we can recreate the site during its developmental stages till the present day. Furthermore, we utilize terrestrial laser scan data sequence acquired in the past decade (2012-2022) to compute current erosion rates. These are then used to determine past rates, enabling inferences about the climatic conditions prevalent in the region over the last millennium. The in-depth examination of these installations provides valuable insights into approaches for soil conservation, sustainable desert living, and strategies to safeguard world-heritage sites subjected to soil erosion. As the global imperative to address soil erosion intensifies, this case study gains heightened relevance.

How to cite: Arav, R., Filin, S., and Avni, Y.: Using current 3D point clouds as a tool to infer on past geomorphological processes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14680,, 2024.

On-site presentation
Bernhard Höfle, Ronald Tabernig, Vivien Zahs, Alberto M. Esmorís Pena, Lukas Winiwarter, and Hannah Weiser

AIM: We will present how virtual laser scanning (VLS), i.e., simulation of realistic LiDAR campaigns, can be key for applying machine/deep learning (ML/DL) approaches to geographic point clouds. Recent results will be shown for semantic classification and change analysis in multitemporal point clouds using exclusively open source scientific software.

MOTIVATION: Laser scanning is able to deliver precise 3D point clouds which have made huge progress in research in geosciences over the last decade. Capturing multitemporal (4D: 3D + time) point clouds enables to observe and quantify Earth surface process activities, their complex interactions and triggers. Due to the large size of 3D/4D datasets that can be captured by modern systems, automatic methods are required for point cloud analysis. Machine learning approaches applied to geographic point clouds, in particular DL, have shown very promising results for many different geoscientific applications [1,2].

METHODS & RESULTS: While new approaches for deep neural networks are rapidly developing [1], the bottleneck of sufficient and appropriate training data (typically annotated point clouds) remains the major obstacle for many applications in geosciences. Those data hungry learning methods depend on proper domain representation by training data, which is challenging for natural surfaces and dynamics, where there is high intra-class variability. Synthetic LiDAR point clouds generated by means of VLS, e.g., with the open-source simulator HELIOS++ [3], can be a possible solution to overcome the lack of training data for a given task. In a virtual 3D/4D scene representing the target surface classes, different LiDAR campaigns can be simulated, with all generated point clouds being automatically annotated. VLS software like HELIOS++ allows to simulate any LiDAR platform and settings for a given scene, which offers high potential for data augmentation and the creation of training samples tailored to specific applications. In recent experiments [1], purely synthetic training data could achieve similar performances to costly labeled training data from real-world acquisitions for semantic scene classification.

Furthermore, surface changes can be introduced to create dynamic VLS scenes (e.g., erosion, accumulation, movement/transport). Combining LiDAR simulation with automatic change analysis, such as offered by the open-source scientific software py4dgeo [5], enables to perform ML for change analysis in multitemporal point clouds [6]. Recent results show that rockfall activity mapping and classification for permanent laser scanning data can be successfully implemented by combining HELIOS++, py4dgeo and the open-source framework VL3D, which can be used for investigating various ML/DL approaches in parallel.

CONCLUSION: Expert domain knowledge (i.e., definition of proper 3D/4D scenes) and the power of AI can be closely coupled in VLS-driven ML/DL approaches to analyze 3D/4D point clouds in the geosciences. Open-source scientific software already offers all required components (HELIOS++, VL3D, py4dgeo). 


[1] Esmorís Pena, A. M., et al. (2024): Deep learning with simulated laser scanning data for 3D point cloud classification. ISPRS Journal of Photogrammetry and Remote Sensing. under revision.

[2] Winiwarter, L., et al. (2022): DOI: 

[3] HELIOS++:

[4] VL3D framework:

[5] py4dgeo:

[6] Zahs, V. et al. (2023): DOI:

How to cite: Höfle, B., Tabernig, R., Zahs, V., Esmorís Pena, A. M., Winiwarter, L., and Weiser, H.: Machine-learning based 3D point cloud classification and multitemporal change analysis with simulated laser scanning data using open source scientific software, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1261,, 2024.

On-site presentation
Yihui Yang, Daniel Czerwonka-Schröder, and Christoph Holst

The permanent terrestrial laser scanning (PLS) system has opened the possibilities for efficient data acquisition with high-temporal and spatial resolution, thus allowing for improved capture and analyses of complex geomorphological changes on the Earth's surface. Accurate georeferencing of generated four-dimensional point clouds (4DPC) from PLS is the prerequisite of the following change analysis. Due to the massive data volume and potential changes between scans, however, efficient, robust, and automatic georeferencing of 4DPC remains challenging, especially in scenarios lacking signalized and reliable targets. This georeferencing procedure can be typically realized by designating a reference epoch and registering all other scans to this epoch. Addressing the challenges in targetless registration of topographic 4DPC, we propose a simple and efficient registration method called Piecewise-ICP, which first segments point clouds into piecewise patches and aligns them in a piecewise manner.

Assuming the stable areas on monitored surfaces are locally planar, supervoxel-based segmentation is employed to generate small planes from adjacent point clouds. These planes are then refined and classified by comparing defined correspondence distances to a monotonically decreasing distance threshold, thus progressively eliminating unstable planes in an efficient iterative process as well as preventing local minimization in the ICP process. Finally, point-to-plane ICP is performed on the centroids of the remaining stable planes. We introduce the level of detection in change analysis to determine the minimum distance threshold, which mitigates the influence of outliers and deformed areas on registration accuracy. Besides, the spatial distribution of empirical registration uncertainties on registered point clouds is derived based on the variance-covariance propagation law.

Our registration method is demonstrated on two datasets: (1) Synthetic point cloud time series with defined changes and transformation parameters, and (2) a 4DPC dataset from a PLS system installed in the Vals Valley (Tyrol, Austria) for monitoring a rockfall. The experimental results show that the proposed algorithm exhibits higher registration accuracy compared to the existing robust ICP variants. The real-time capability of Piecewise-ICP is significantly improved owing to the centroid-based point-to-plane ICP and the efficient iteration process.

How to cite: Yang, Y., Czerwonka-Schröder, D., and Holst, C.: Piecewise-ICP: Efficient Registration of 4D Point Clouds for Geodetic Monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5674,, 2024.

On-site presentation
Kossi Nouwakpo, Anette Eltner, Bernardo Candido, Yingkui Li, Kenneth Wacha, Mary Nichols, and Robert Washington-Allen

Understanding the complex processes occurring at the soil surface is challenging due to the intricate spatial variability and dynamic nature of these processes. An effective tool for elucidating these phenomena is three-dimensional (3D) reconstruction, which employs advanced imaging technologies to create a comprehensive representation of the soil surface at high spatial resolution, often at the mm-scale. Three-dimensional reconstruction techniques are increasingly available to scientists in the fields of soil science, geomorphology, hydrology, and ecology and many studies have employed these novel tools to advance understanding of surface processes. Much of the data being collected in these studies are however not interoperable, i.e., 3D data from one study may not be directly combined with 3D data from other studies thus limiting the ability of researchers to advance process understanding at a broader scope. The limited interoperability of existing data is due in part to the fact that 3D surface reconstruction data are influenced by many factors including experimental conditions, intrinsic soil properties and accuracy and precision limits of the 3D reconstruction technique used. These ancillary data are crucial to any broad-scope efforts that leverage the increasing number of 3D datasets collected by scientists across disciplines, geographic regions, and experimental conditions. We have developed a relational database that archives and serves ancillary data associated with published high-resolution 3D data representing soil surface processes. This presentation introduces the structure of the database with its required and optional variables. We also provide analytics on the currently available records in the database and discuss potential applications of the database and future developments.

How to cite: Nouwakpo, K., Eltner, A., Candido, B., Li, Y., Wacha, K., Nichols, M., and Washington-Allen, R.: A database for ancillary information of three-dimensional soil surface microtopography measurements., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10373,, 2024.

On-site presentation
Aliki Konsolaki, Emmanuel Vassilakis, Evelina Kotsi, Michalis Diakakis, Spyridon Mavroulis, Stelios Petrakis, Christos Filis, and Efthymios Lekkas

The evolution of technology, particularly the integration of Unmanned Aerial Systems (UAS), earth observation datasets, and historical data such as aerial photographs, stand as fundamental tools for comprehending and reconstructing surface evolution and potential environmental changes. In addition, the active geodynamic phenomena in conjunction with climate crisis and the increasing frequency of extreme weather phenomena can cause abrupt events such as rockfalls and landslides, altering completely the morphology on both small and large scales.

This study deals generally with the temporal evolution of landscapes and specifically focuses on the detection and quantification of a significant rockfall event that occurred at Kalamaki Beach on Zakynthos Island, Greece – a very popular summer destination. Utilizing UAS surveys conducted in July 2020 and July 2023, this research revealed a rockfall that has significantly altered the coastal morphology. During this period, two severe natural phenomena occurred, one of which could potentially be the cause of this rockfall event. Initially, the Mediterranean hurricane (‘medicane’) ‘Ianos’ made landfall in September 2020, affecting a large part of the country including the Ionian Islands. The result was severe damage to property and infrastructures, along with human casualties, induced by intense precipitation, flash flooding, strong winds, and wave action. Second, in September of 2022, an ML=5.4 earthquake struck between Cephalonia and Zakynthos Islands in the Ionian Sea, triggering considerable impact in both islands. The study employs satellite images postdating these natural disasters, to detect the source of the rockfall in Kalamaki Beach. Additionally, historical analog aerial images from 1996 and 2010 were used as assets for understanding the surface’s evolution. For the quantitative analysis, we applied 3D semi-automated change detection techniques such as the M3C2 algorithm, to estimate the volume of the rockfall.

The results provide insights into the complex interplay between natural disasters and geological processes, shedding light on the dynamic nature of landscapes and the potential implications for visitor-preferred areas.

This research not only contributes to our understanding of landscape evolution but also underscores the importance of integrating modern and historical datasets to decipher the dynamic processes shaping the Earth's surface. The methodology proposed, serves as a valuable approach for assessing and managing geological hazards in coastal regions affected by both climatic events and geodynamic activities.

How to cite: Konsolaki, A., Vassilakis, E., Kotsi, E., Diakakis, M., Mavroulis, S., Petrakis, S., Filis, C., and Lekkas, E.: A Time-Series Analysis of Rockfall Evolution in a Coastal Region Using Remote Sensing Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10361,, 2024.

Historical images
On-site presentation
Joaquín M. C. Belart, Sydney Gunnarson, Etienne Berthier, Amaury Dehecq, Tómas Jóhannesson, Hrafnhildur Hannesdóttir, and Kieran Baxter

The archive of historical aerial photographs of Iceland consists of ~140,000 vertical aerial photographs acquired between the years 1945 and 2000. It contains an invaluable amount of information about human and natural changes in the landscape of Iceland. We have developed a series of automated processing workflows for producing accurate orthomosaics and Digital Elevation Models (DEMs) from these aerial photographs, which we’re making openly available in a data repository and a web map visualization service. The workflow requires two primary inputs: a modern orthomosaic to automatically extract Ground Control Points (GCPs) and an accurate DEM for a fine-scale (sub-meter) alignment of the historical datasets. We evaluated the accuracy of the DEMs by comparing them in unchanged terrain against accurate recent lidar and Pléiades-based DEMs, and we evaluated the accuracy of the orthomosaics by comparing them against Pléiades-based orthomosaics. The data are becoming available at To show the potential applications of this repository, we present the following showcases where these data reveal significant changes the landscape in Iceland in the past 80 years: (1) volcanic eruptions (Askja 1961, Heimaey 1973 and the Krafla eruptions, 1975-1984), (2) decadal changes of Múlajökull glacier from 1960-2023, (3) Landslides (Steinsholtsjökull 1967, Tungnakvíslarjökull 1945-present) and (4) coastal erosion (Surtsey island).

How to cite: Belart, J. M. C., Gunnarson, S., Berthier, E., Dehecq, A., Jóhannesson, T., Hannesdóttir, H., and Baxter, K.: Unleashing the archive of aerial photographs of Iceland, 1945-2000. Applications in geosciences , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12105,, 2024.

On-site presentation
Felix Dahle, Roderik Lindenbergh, and Bert Wouters

Our research explores the potential of historical images of Antarctica for change detection in 2D and 3D. We
make use of the TMA Archive, a vast collection of over 330,000 black and white photographs of Antarctica taken
between 1940 to 1990. These photographs, available in both nadir and oblique, are systematically captured
from airplanes along flight paths and offer an unprecedented historical snapshot of the Antarctic landscape.
Detecting changes between past and present observations provides a unique insight into the long-term impact
of changing climate conditions on Antarctica’s glaciers, and their dynamical response to ice shelf weakening and
disintegration. Furthermore, it provides essential validation data for ice modelling efforts, thereby contributing
to reducing the uncertainties in future sea level rise scenarios.

In previous work, we applied semantic segmentation to these images [1]. By employing classes derived from this
segmentation, we can focus on features of interest and exclude images with extensive cloud coverage, enhancing
the accuracy of change analyses. In the next step, we geo-referenced the images: We assigned the images to
their actual position, scaled them to their true size, and aligned them with their genuine orientation. This
presents novel opportunities for detecting environmental changes in Antarctica, particularly in the retreat of
glaciers and sea ice.

Furthermore, the combination of these two steps allows for the first time a large scale reconstruction of these
images in 3D through Structure from Motion (SfM) techniques, which enables further multidimensional change
detection by comparing historical 3D models with contemporary ones. Due to the high number of images,
manual processing is impractical. Therefore, we are investigating the possibility of automatizing this process.
We utilize MicMac, an open-source software developed by the French National Geographic Institute for the
creation of the 3D models. Its high modularity allows for necessary customizations to automate the SfM
process effectively. Further adaptions are required due to the poor image quality and monotonous scenery. By
comparing historical 3D models with contemporary ones, we can assess alterations in elevation due to factors
such as glacial isostatic adjustments and glacier retreat.

We have already employed geo-referenced images for detecting changes on the Antarctic peninsula and are in the
process of creating initial 3D models. Our presentation will outline the workflow we developed for this process
and showcase the initial results of the change detection, both in 2D and 3D formats. This approach marks a
significant step in understanding and visualizing the impacts of climate change on the Antarctic landscape.

This work was funded by NWO-grant ALWGO.2019.044.

[1] F. Dahle, R. Lindenbergh, and B. Wouters. Revisiting the past: A comparative study for semantic segmen-
tation of historical images of Adelaide Island using U-nets. ISPRS Open Journal of Photogrammetry and
Remote Sensing, 11:100056, 2024.

How to cite: Dahle, F., Lindenbergh, R., and Wouters, B.: Utilizing historical aerial imagery for change detection in Antarctica, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15439,, 2024.

Posters on site: Tue, 16 Apr, 10:45–12:30 | Hall X3

Display time: Tue, 16 Apr 08:30–Tue, 16 Apr 12:30
Chairpersons: Livia Piermattei, Katharina Anders
Modern high resolution topography
László Bertalan, Gábor Négyesi, Gergely Szabó, Zoltán Túri, and Szilárd Szabó

Wind erosion constitutes a prominent land degradation process in regions of Hungary characterized by low annual precipitation. In these areas, it poses significant challenges to agricultural productivity and adversely impacts soil and environmental quality. Presently, human activities exert a more pronounced influence on the endangered areas of Hungary in comparison to climate-related factors. It is noteworthy that the wind erodibility of Hungarian soils not only poses a soil conservation challenge but also gives rise to economic ramifications, such as nutrient loss, as well as environmental and human health concerns. Within agricultural landscapes, wind erosion contributes to the removal and transportation of the finest and biologically active soil fractions, rich in organic matter and nutrients.

High-resolution topographic surveys have become integral for assessing volumetric changes in sand dune mobility and mapping wind erosion. While Unmanned Aerial Systems (UAS) surveys have been extensively employed for erosion rates exceeding the decimeter scale, Terrestrial Laser Scanning (TLS) surveys have demonstrated efficiency in capturing more extensive negative erosional forms, even in a vertical orientation. To enhance the field of view, a mounting framework can be implemented to elevate the TLS. However, determining centimeter-scale material displacement in flat terrain conditions remains challenging and requires an increased number of scanning positions.

To identify optimal settings for surveying centimeter-scale wind erosion magnitudes, we conducted combined multi-temporal TLS and UAS surveys at the Westsik experimental site near Nyíregyháza during the spring of 2023. This site features dune topography with a height of 6 meters. Our investigations encompassed various UAS image acquisition modes, involving different flight altitudes and camera settings, utilizing a DJI Matrice M210 RTK v2 drone and a Zenmuse X7 24 mm lens. Additionally, we generated diverse point clouds through various scanning scenarios using a Trimble X7 TLS device. In the data processing phase, we explored multiple co-registration algorithms to address the challenge of larger Root Mean Square Error (RMSE) in Digital Terrain Models (DTMs) from UAS Structure from Motion (SfM) compared to the actual wind erosion rates.


The research is supported by the NKFI K138079 project.

How to cite: Bertalan, L., Négyesi, G., Szabó, G., Túri, Z., and Szabó, S.: Evaluating the efficacy of multitemporal TLS and UAS surveys for quantifying wind erosion magnitudes of sand dune topography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4399,, 2024.

Kourosh Hosseini, Jakob Hummelsberger, Daniel Czerwonka-Schröder, and Christoph Holst

Landslides are a pervasive natural hazard with significant societal and environmental impacts. In addressing the critical need for accurate landslide detection and monitoring, our previous research introduced a feature-based monitoring method enhanced by histogram analyses, straddling a middle ground between point-based and point cloud-based methods. This paper expands upon that foundation, introducing an innovative contour line extraction technique from various epochs to precisely identify areas prone to deformation. This refined focus diverges from conventional methodologies that analyze entire point clouds. By applying on regions where contour lines do not match, indicating potential ground movement, we significantly elevate the efficiency and precision of our feature-based monitoring system.


One of the principal challenges of feature-based monitoring is managing a substantial number of outliers. Our prior research tackled this issue effectively by integrating feature tracking with histogram analysis, thereby filtering these outliers from the final results. However, the process of extracting features from each patch and matching them with corresponding patches from different epochs was time-intensive.


The incorporation of contour line extraction into our workflow, using high-resolution laser scanner data, allows for a more focused and efficient analysis. We can now identify and analyze areas of landscape alteration with greater accuracy. This approach limits the application of feature tracking and histogram analysis to these critical areas, thus streamlining the process and significantly reducing computational demands. This focused methodology not only accelerates data processing but also enhances the accuracy of landslide predictions.


Our findings indicate a substantial improvement in the efficiency of landslide monitoring methods. This methodology represents a promising advancement in geospatial analysis, particularly for environmental monitoring and risk management in regions susceptible to landslides. This research contributes to the ongoing efforts to develop more effective, efficient, and accurate approaches to landslide monitoring, ultimately aiding in better informed and timely decision-making processes for hazard mitigation and risk management.

How to cite: Hosseini, K., Hummelsberger, J., Czerwonka-Schröder, D., and Holst, C.: Enhancing 3D Feature-based Landslide Monitoring Efficiency by Integrating Contour Lines in Laser Scanner Point Clouds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5670,, 2024.

Bo Rui Chen and Wei An Chao

The water level and discharge of river are crucial parameters to understand the variance in riverbed scour. The detail behavior of scouring can be studied by the hydraulic simulation. The grain-size distribution of riverbed is also one of crucial parameter for modeling. Thus, how to investigate the grain-size of riverbed efficiently and swiftly is the urgent issue. However, the conventional measurement methods including Wolman counts (particles sampled at a fixed interval) which are a long and laborious task cannot survey the grain-size efficiently in the large area. In recent years, with an advantage of image segmentation and recognition has been applied to the investigation of grain-size, for example, capturing images through UAV and generating orthoimage is one of commonly used image technique. Although above the method can investigate the grain-size in the large area, it does not provide the information in the field immediately. Hence, a recent study developed the low-cost portable scanner to obtain the information of grain-size distribution in the field. However, the calibrating parameters of camera (e.g., height camera capture) are necessary before survey, and the uncertainties in calculation of image resolution will significantly affect the accuracy of grain-size analysis. Therefore, this study provides the additional algorithm to analyze the grain-size by using RGB-D image as inputs. The application of RGB-D can be categorized into two-dimensional (2D) and three-dimensional (3D) spaces. In a case of 2D, it integrates depth information with traditional RGB image processing to separate the grain-size of riverbed from the background (e.g., bottomland). Furthermore, depth information is also applied for grain-size edge detection. In a case of 3D, the collected RGB-D image information is transformed into point cloud data, then extract 3D features of grain particle by Deep learning, specifically PointNet. Our study demonstrates that clustering of 3D features can achieve the automatic identification of particle. The grain-size of particle can also be estimated by fitting 3D ellipsoid geometry. In the end, results show the grain-size distribution curves with the RGB、RGB-D、PointNet recognition, and compare with the true observations. 3D image information provides the cloud points of grain object, leading the possibility of estimating the 3D geometric morphology of the object. Our study successfully overcomes the limitations of conventional RGB-based process, which could only capture size and shape information in 2D planar. RGB-D-based image recognition, is an innovative technique for the hydraulic problem, not only advances survey efficiency but also addresses the intricate steps required for field investigations.


Key words: Riverbed grain size, RGB-D image, Point cloud, Deep Learning

How to cite: Chen, B. R. and Chao, W. A.: A point-cloud deep learning model based on RGB-D images: Application of riverbed grain size survey, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14087,, 2024.

Vivien Zahs, Bernhard Höfle, Maria Federer, Hannah Weiser, Ronald Tabernig, and Katharina Anders

We advance the characterization of landscape dynamics through analysis of point cloud time series by integrating virtual laser scanning, machine learning and innovative open source methods for 4D change analysis. We present a novel approach for automatic identification of different surface activity types in real-world 4D geospatial data using a machine learning model trained exclusively on simulated data.

Our method focuses on classifying surface activity types based on spatiotemporal features. We generate training data using virtual laser scanning of a dynamic coastal scene with artificially induced surface changes. Scenes with surface change are generated using geographic knowledge and the concept of 4D objects-by-change (4D-OBCs) [1, 2], which represent spatiotemporal subsets of the scene that exhibit change with similar properties. A realistic 3D scene modelling is essential for accurately replicating the dynamic nature of coastal landscapes, where morphological changes are driven by both natural processes and anthropogenic activities.

The Earth's landscapes exhibit complex dynamics, spanning large spatiotemporal scales, from high-mountain glaciers to sandy coastlines. The challenge lies in effectively detecting and classifying diverse surface activities with varying magnitudes, spatial extents, velocities, and return frequencies. Effective characterization of these dynamics is crucial for understanding the underlying environmental processes and their interplay with human activities. Supervised machine learning classification of surface activities from point cloud time series is challenging due to the limited availability of comprehensive and diverse real-world datasets for training and validation. Our approach combines virtual laser scanning with machine learning-based classification, enabling the generation of comprehensive training datasets covering the full spectrum of expected change patterns [3].

In our approach, the simulation of LiDAR point clouds is performed in the open-source framework HELIOS++ [4, 5]. HELIOS++ allows the flexible simulation of custom LiDAR campaigns with diverse acquisition modes and settings together with automatic annotations of artificially induced surface changes. We train a supervised machine learning model to classify synthetic 4D-OBCs into typical surface activity types of a sandy beach (e.g. dune erosion/accretion, sediment transport, etc.). Moreover, we investigate descriptors for 4D-OBCs, assessing their suitability for representing general types of surface activity (transferable between use cases) and types specific to particular surface processes.

We evaluate our model for 4D-OBC classification in terms of its capacity to discriminate surface activity types in a real-world dataset of a sandy beach in the Netherlands [6]. 4D-OBCs are extracted, classified into our target classes and validated with manually labelled reference data based on expert evaluation.

Our study showcases the efficacy of coupling virtual laser scanning, innovative open-source 4D change analysis methods, and machine learning for classifying natural surface changes [7]. Our findings not only contribute to advancing the understanding of landscape dynamics but also provide a promising approach to mitigating environmental challenges.


[1] Anders et al. (2022): DOI:

[2] py4dgeo: 

[3] Zahs et al. (2022): DOI:

[4] HELIOS++:

[5] Winiwarter et al. (2022): DOI: 

[6] Vos et al. (2022): DOI:

[7] CharAct4D:

How to cite: Zahs, V., Höfle, B., Federer, M., Weiser, H., Tabernig, R., and Anders, K.: Automatic Classification of Surface Activity Types from Geographic 4D Monitoring Combining Virtual Laser Scanning, Change Analysis and Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1640,, 2024.

Diana Krawczyk, Tobias Vonnahme, Ann-Dorte Burmeister, Sandra Maier, Martin Blicher, Lorenz Meire, and Rasmus Nygaard

Our study focuses on the geologically, topographically, and oceanographically complex region of Disko Bay in West Greenland. Disko Bay is also considered a marine biodiversity hotspot in Greenland. Given the impact of commercial fishing on seafloor integrity in the area, seafloor habitats studies are crucial for sustainable use of marine resources. One of the key fishery resources in Greenland, as well as in the North Atlantic Ocean, is northern shrimp.

In this study we analyzed multiple (1) monitoring datasets from 2010 to 2019, including data from shrimp and fish surveys, commercial shrimp fishery catches, satellite chlorophyll data, and (2) seafloor models, encompassing high-resolution (25 x 25 m) multibeam data with a low-resolution (200 x 200 m) IBCAO grid. Using multivariate regression analysis and spatial linear mixed-effect model we assessed the impact of physical (water depth, bottom water temperature, sediment type), biological (chlorophyll a, Greenland halibut predation), and anthropogenic factors (shrimp fishery catch and effort) on shrimp density in the area. The resulting high-resolution predictive model of northern shrimp distribution in Disko Bay is the first model of this kind developed for an Arctic area.

Our findings reveal that shrimp density is significantly associated with static habitat factors, namely sediment type and water depth, explaining 34% of the variation. The optimal shrimp habitat is characterized by medium-deep water (approximately 150-350 m) and mixed sediments, primarily in the north-eastern, south-eastern, and north-western Disko Bay. This pioneering study highlights the importance of seafloor habitat mapping and modeling, providing fundamental geophysical knowledge necessary for long-term sustainable use of marine resources in Greenland.

The developed high-resolution model contributes to a better understanding of detailed patterns in northern shrimp distribution in the Arctic, offering valuable insights for stock assessments and sustainable fishery management. This novel approach to seafloor habitat mapping supports the broader goal of ensuring the responsible utilization of marine resources, aligning with principles of environmental conservation and fisheries management. Our work serves as a foundation for ongoing efforts to balance economic interests with the preservation of marine ecosystems, fostering a harmonious coexistence between human activities and the fragile Arctic environment.

How to cite: Krawczyk, D., Vonnahme, T., Burmeister, A.-D., Maier, S., Blicher, M., Meire, L., and Nygaard, R.: Arctic puzzle: pioneering a shrimp habitat model in topographically complex Disko Bay (West Greenland), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5757,, 2024.

Emanuele Colica, Daniel Fenech, Christopher Gauci, and George Buhagiar

The Maltese coasts extend for approximately 273km, representing a notable resource for the country and of one of its pillar economies, the tourism sector. Natural processes and anthropic interventions continue to threaten Malta's coastal morphology, shaping its landscape and triggering soil erosion phenomena. Therefore, many research projects (Colica et al., 2021, 2022 and 2023) have concentrated their work on the investigation and monitoring of the instability of cliffs and the erosion of pocket beaches. The results of such activities can be widely disseminated and shared with expert and non-expert users through web mapping, which has only been used in a very limited way in collaborative coastal management and monitoring by different entities in Malta. This study describes the performance of a WebGIS designed to disseminate the results of innovative geomatic investigations for monitoring and analyzing erosion risk, performed by the Research and Planning Unit within the Public Works Department of Malta. While aiming to include the entire national coastline, three study areas along the NE and NW regional coasts of the island of Malta have already been implemented as pilot cases. This WebGIS was generated using ArcGIS pro software by ESRI and a user-friendly interactive interface has been programmed to help users view in 2D and 3D, satisfying both multi-temporal and multi-scale perspectives. It is envisaged that through further development and wider dissemination there will be a stronger uptake across different agencies involved in coastal risk assessment, monitoring and management.


Colica, E., D’Amico, S., Iannucci, R., Martino, S., Gauci, A., Galone, L., ... & Paciello, A. (2021). Using unmanned aerial vehicle photogrammetry for digital geological surveys: Case study of Selmun promontory, northern of Malta. Environmental Earth Sciences, 80, 1-14.

Colica, E. (2022). Geophysics and geomatics methods for coastal monitoring and hazard evaluation.

Colica, E., Galone, L., D’Amico, S., Gauci, A., Iannucci, R., Martino, S., ... & Valentino, G. (2023). Evaluating Characteristics of an Active Coastal Spreading Area Combining Geophysical Data with Satellite, Aerial, and Unmanned Aerial Vehicles Images. Remote Sensing, 15(5), 1465.

How to cite: Colica, E., Fenech, D., Gauci, C., and Buhagiar, G.: Integrating structure-from-motion photogrammetry with 3D webGIS for risk assessment, mapping and monitoring of coastal area changes in the Maltese archipelago, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16939,, 2024.

Historical images
Benoît Smets, Antoine Dille, Olivier Dewitte, and François Kervyn

The acquisition of aerial photographs for cartographic applications started in the 1930s, and more intensively after World War II. Such old, often panchromatic, imagery offers metre to sub-metre scale spatial resolution over landscapes that have significantly evolved over the decades. Before the appearance of the first digital aerial camera systems at the end of the 20th Century, surveys were performed with analogue metric cameras, with images acquired on films or glass plates and, next, developed on photo papers. In Europe and North America, several institutions hold unique collections of historical aerial photographs having local, national and, in some cases, colonial coverages. They represent invaluable opportunities for environmental studies, allowing the comparison with today’s land use land cover, and the analysis of long-term surface displacements.

Initially, the photogrammetric processing of analogue aerial photographs would require expensive equipment, specialised operators, and significant processing time. Thanks to the digital revolution of the past two decades and the development of modern digital photogrammetric approaches, the processing of this type of image datasets has become less cumbersome, time consuming and expensive, at least in theory. In practice, this is more complex, with digitising and processing issues related to the ageing and quality of conservation of the aerial photographs, the potential distortions created during the digitising process, and the lack of ancillary data, such as, flight plans, and camera calibration reports. The limited overlap between photographs, typically 60 % and 10-20 %, along-track and across-track, respectively, make their processing with Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetry poorly reliable to accurately reconstruct the topography and orthorectify the images. Given the fact that some collections reach up to millions of historical aerial photographs, the digitising, pre-processing, and photogrammetric processing of these images remain a challenge that must be properly tackle if we would like to ensure their preservation and large-scale valorisation.

In the present work, we describe the mass-digitising, digital image pre-processing and photogrammetric processing approaches implemented at the Royal Museum for Central Africa (RMCA, Belgium) to preserve and valorise the collection of >320,000 historical aerial photographs conserved in this federal institution. This imagery was acquired between the 1940’s and the 1980’s, over Central Africa, and mostly D.R. Congo, Rwanda and Burundi. For the digitising, a system of parallelized flatbed scanners controlled by a Linux computer and a self-developed software allows speeding-up the scanning of the entire collection in only few years. A series of Python scripts were developed and combined to allow a swift pre-processing that prepare and optimise the digitised images for photogrammetric processing. Finally, a SfM-MVS photogrammetric approach adapted to historical aerial photos is used. Examples of application for geo-hydrological hazards studies in the western branch of the East African Rift are shown.

How to cite: Smets, B., Dille, A., Dewitte, O., and Kervyn, F.: Digitising, pre-processing and photogrammetric processing of historical aerial photographs for the production of high resolution orthomosaics and the study of geohazards, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2356,, 2024.

Christian Ginzler, Livia Piermattei, Mauro Marty, and Lars T. Waser

Historical aerial images, captured by film cameras in the previous century, have emerged as valuable resources for quantifying Earth's surface and landscape changes over time. In the post-war period, historical aerial images were often acquired to create topographic maps, resulting in the acquisition of large-scale aerial photographs with stereo coverage. Using photogrammetric techniques on stereo-images enables extracting 3D information to reconstruct Digital Surface Models (DSMs), and orthoimages.

This study presents a highly automated photogrammetric approach for generating nationwide DSMs for Switzerland at 1 m resolution using aerial stereo-images acquired between 1979 and 2006. The 8-bit scanned images, with known exterior and interior orientation, were processed using BAE Systems' SocetSet (v5.6.0) with the "Next-Generation Automatic Terrain Extraction" (NGATE) package for DSM generation. The primary objective of the study is to derive four nationwide DSMs for the epochs 1979-1985, 1985-1991, 1991-1998, and 1998-2006. The study assesses DSM quality in terms of vertical accuracy and completeness of image matching across different land cover types, with a focus on forest dynamics and management research.

The elevation accuracy of the generated DSMs was assessed using two reference datasets. Firstly, the elevation differences between a nationwide reference Digital Terrain Model (DTM - swissAlti3d 2017 by Swisstopo) and the generated DSMs were calculated on points classified as "sealed surface". Secondly, elevation values of the DSMs were compared to approximately 500 independent geodetic points distributed across the country. Six study areas were chosen to assess completeness, and it was calculated as the percentage of successfully matched points to the potential total number of matched points within a predefined area. This assessment was conducted for six land cover classes based on the land cover/land-use statistics dataset from the Federal Office of Statistics.

Across the entire country, the median elevation accuracy of the DSMs on sealed points ranges between 0.28 to 0.53 m, with a Normalized Median Absolute Deviation (NMAD) of around 1 m (maximum 1.41 m) and an RMSE of a maximum of 3.90 m. The elevation differences between geodetic points and DSMs show higher accuracy, with a median value of a maximum of 0.05 m and an NMAD smaller than 1 m. Completeness results reveal mean completeness between 64 % to 98 % for the classes "glacial and perpetual snow" and "sealed surfaces," respectively and 93 % specifically for the “closed forest” class.

This work demonstrates the feasibility of generating accurate DSM time series (spanning four epochs) from historical scanned images for the entire Switzerland in a highly automated manner. The resulting DSMs will be available upon publication, providing an excellent opportunity to detect major surface changes, such as forest dynamics.

How to cite: Ginzler, C., Piermattei, L., Marty, M., and Waser, L. T.: Four nationwide Digital Surface Models from airborne historical stereo-images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5142,, 2024.

Virginia Chiara Cuccaro, Claudio Di Giovannantonio, Giovanni Pica, Luca Malatesta, and Fabio Attorre

Rural landscapes inherited from the past are marked by a strong interaction between man and nature, a relationship rooted in a long history that testifies to the importance of the landscape as one of the most historically representative expressions of a country's cultural identity.

In this broad context, olive groves markedly characterize the agricultural landscape of many European rural areas, particularly in the Mediterranean region. Along with other rural landscapes, they form a semi-natural environment that can contribute to biodiversity conservation, soil protection and ecosystem resilience.

In addition to the global increase in temperatures, the main threats affecting these agrarian landscapes include the abandonment of traditional practices and the intensification of cultivation through the installation of irregular, intensive and overly dense planting beds.

The Land Cover classification and change-detection can provide useful indications for the restoration, conservation, and enhancement of olive groves

The objective of this work was to identify , rural landscapes in the Lazio region with characteristics of historical interest and determine their level of conservation. In particular, it was investigated the olive landscape of Cures (historic province of Sabina) trough a multi-temporal analysis of literature and cartographic information (e.g. orthophotos from the Italian Aeronautical Group flight of 1954)

The technique concerns the VASA (Historical Environmental Assessment) methodology, which allows the temporal evaluation of a given landscape and can inform on how agricultural practices and land use have changed over time.

Softwares  Collect Earth and Google Earth were employed to manipulate the historical series of high-resolution satellite images and implement photointerpretation. The coverage of identitied land units  was then estimated to address the configuration of the target landscape.

Landscape evolution over time was achieved by overlaying the 1954 and 2022 land use polygons, resulting in a merging database, in which an evolutionary dynamic was associated with each land use change.

The approach generated in-depth insights on the significant elements of the CURES olive landscape and informed on the dynamics of the area in relation to the risk of their disappearance, making it possible to identify what are the "landscape emergencies," i.e., the land uses that have seen the most̀ reduction in their area.

The methodologies employed have proven reliability in improving the knowledge ng target landscapes.  It might be useful to promote  sustainable agricultural practices for better preservation and management of rural environments so that cultural traditions can be preserved as well, and the environmental balance of the agrarian land can be maintained.

How to cite: Cuccaro, V. C., Di Giovannantonio, C., Pica, G., Malatesta, L., and Attorre, F.: Employng satellite immagery interpretation tools to detect land-use land-change dynamics in Italian historical rural landscapes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11949,, 2024.

Daniel Fenech, Jeremie Tranchant, Christopher Gauci, Daniela Ghirxi, Ines Felix-Martins, Emanuele Colica, and George Buhagiar


Jeremie' Tranchant1, Daniel Fenech1, Christopher Gauci1, Daniela Ghirxi1, Ines Felix Martins1, Emanuele Colica1, George Buhagiar1

1  Research and Planning Unit, Ministry for Transport, Infrastructure and Public Works, Project House, Triq Francesco    Buonamici, Floriana, FRN1700, Malta

The assessment of coastal erosion through shoreline change analysis, is an exercise of national utility undertaken in many countries. The Maltese Islands are particularly vulnerable to coastal erosion given the economic value of coastal activities and their high ratio of coast-to-land surface. The integration of historical cartographic material is often used to hindcast shoreline change across long periods of time, as well as to model future erosion rates. The Public Works Department have produced detailed 1:2500 maps of Malta in collaboration with the British Ordnance Survey from the end of the 19th century to 1957, however these maps have never been scientifically assessed. The initial research carried out evaluated the usefulness of the two oldest 25-inches Maltese maps series (early 20th century and 1957) for shoreline change analysis.  The two series were digitised, georeferenced, and compared in a GIS environment to assess their differences. The inaccuracies of the original drawings, absent shoreline indicators, and the absence of a geographic coordinate system (datum and projection) were identified as limitations for their use in evaluating small gradual changes, but were ideal for the identification of stochastic, large-scale historic erosion events using difference maps. This assessment showed that the two series are highly congruous and any changes between the two series are largely attributed to changes in infrastructure. There were, however, minor exceptions and these need to be explored on a case-by-case basis. These methods and the insights garnered from their production will function as scientific steppingstones towards developing a holistic coastal erosion national monitoring program.  

How to cite: Fenech, D., Tranchant, J., Gauci, C., Ghirxi, D., Felix-Martins, I., Colica, E., and Buhagiar, G.: Evaluating Ordnance Survey sheets (1890s – 1957) for shoreline change analysis in the Maltese Islands , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17822,, 2024.