# Guidelines for the FAIRt publication of Terrestrial Laser Scanner Data in Geomorphic Research

Sara Susini, Clara Lévy, Marie-aurélie Chanut, Laure Manceau, David

Amitrano, Thomas JB Dewez

► To cite this version:

Sara Susini, Clara Lévy, Marie-aurélie Chanut, Laure Manceau, David Amitrano, et al. Guidelines for the FAIRt publication of Terrestrial Laser Scanner Data in Geomorphic Research. 2025. hal-05119291

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1 **Title: Guidelines for the FAIRt publication of Terrestrial Laser Scanner Data in Geomorphic Research**

2 **Authors**

3 S. Susini¹ (https://orcid.org/0009-0006-6996-3018), C. Lévy¹ (https://orcid.org/0000-0003-3922-0396),

4 M.A.nut² (https://orcid.org/0000-0001-8353-8049), L. Manceau³ (https://orcid.org/0009-0003-8137-

5 349X), D. Amitrano⁴ (https://orcid.org/0000-0003-3870-0806), T.J.B. Dewez¹ (https://orcid.org/0000-

6 0003-3150-0166)

7 **Affiliations**

8 ¹Bureau des Recherches Géologiques et Minières (BRGM), Orléans, France

9 ²Team GeoCoD, Cerema, 25 avenue François Mitterrand, Bron, France

10 ³Institut de Physique du Globe de Paris (IPGP), Paris, France

11 ⁴Univ. Grenoble Alpes, CNRS, ISTerre, 38000 Grenoble, France

12 **Abstract**

13 The increasing dissemination of data from Terrestrial or Permanent Laser Scanning (TLS/PLS) topographic
14 surveys in geomorphology requires the establishment of clear recommendations for their publication. The
15 work presented here proposes a framework to ensure that published datasets comply with the FAIRt
16 (Findable, Accessible, Interoperable, Reusable, trustworthy) principles, with a particular attention on the
17 documentation of the processing steps. This facilitates the evaluation of the relevance of published point
18 clouds and their reuse. Central to this proposal is a set of structured metadata that is essential to
19 document acquisition conditions, methodological choices and processing parameters in order to
20 understand how the published cloud was obtained and what it can be used for. The framework presented
21 in this article is organised into three distinct parts: the essential perimeter of publication, without which the
22 cloud cannot be exploited, pre-processing and processing. These recommendations are accompanied by
23 concrete examples of datasets to illustrate their implementation. To ensure the long-term interoperability
24 of distribution, it is recommended that the LAS format be used as the standard for TLS clouds, associated
25 with a GeoJSON file of metadata. This approach constitutes a component of a long-term preservation
26 process, which is based on the use of open sharing and storage platforms. This standard is therefore
27 intended to be a support tool for researchers, structuring publication practices while remaining open to
28 adapt to future technological and scientific developments. Designed for TLS acquisitions, it could be
29 extended to photogrammetric point clouds if specific photogrammetric processing information is provided.

30 **Keywords**

31 FAIRt, Terrestrial Laser Scanner TLS, guidelines, topographic survey

32 Highlights

33 • A framework is proposed to guide TLS data publication in geomorphological studies.

34 • Guidelines are designed for topographic surveys, generic to other TLS and PLS uses.

35 • TLS data and metadata should be published using open format as LAS and GeoJSON.

36 • The essential perimeter includes acquisition, raw data, quality, georeferencing.

37 • According to the level of data processing, the methods used ought to be documented.

39 **1. Introduction**

40 Terrestrial Laser Scanning (TLS) has become a widely used method in geomorphology (Telling et al., 2017),

41 leading to a significant increase in the publication of data within the movement of open science, in
42 accordance with the FAIRt (Findable, Accessible, Interoperable, Reusable, and Trustworthy) principles
43 (Wilkinson et al., 2016). In this paper, we present a framework specifically designed for the publication of
44 TLS point clouds, aiming to facilitate universal access to scientific data, thereby encouraging the sharing
45 of results and collaboration within the international community of geomorphologists. Beyond accessibility,
46 this approach also promotes the evaluation of the relevance of published results and supports long-term
47 data preservation through structured and well-documented publication practices.

48 The work presented here responds to the need expressed by the scientific community for unified
49 publication standards to support data reuse and dissemination (Muralikrishnan, 2021). Establishing
50 standards is a necessity to ensure that datasets can be understood, reused, and integrated into new
51 studies. A key component of this process is the inclusion of metadata providing essential context for data
52 validity and usability. They describe various aspects of the dataset such as its origin, survey objectives,
53 acquisition conditions, and spatio-temporal references. In case of processed data, metadata document
54 the processing steps applied after acquisition, which influence the final characteristics of the published
55 data. The absence of metadata in publications can make it difficult or even impossible to reuse. Ensuring
56 transparency through systematic metadata documentation of processing and methods is necessary to
57 assess compliance with the FAIRt principles.

58 Based on existing standards developed for other techniques — such as the Structure-from-Motion (SfM)
59 recommendations by James et al. (2019), LiDAR (Light Detection And Ranging) data platforms like
60 OpenTopography, and a series of publications of point clouds resulting from TLS or SfM acquisitions (Day
61 et al., 2013; Prémaillon et al., 2021; Scott et al., 2022)—this framework adapts and applies these principles
62 to TLS, for which such standard has yet to be formalized. It applies to scientific articles, databases, and
63 data papers. While it may be extrapolated to other applications, it is primarily designed for natural
64 environments (e.g., cliffs, natural slopes, road embankments).

65 The proposed standard documents all stages of a TLS study, from acquisition to advanced processing. It is
66 summarised in the flowchart in Figure 1, which outlines three phases: the essential perimeter
67 (georeferenced point cloud acquisition), preprocessing (e.g., filtering), and processing (on a single cloud or
68 via diachronic comparison). Each phase is associated with specific metadata, detailed in the Table 1
69 provided in the supplementary material. Some are essential, while others are merely recommended — they
70 must document the history of the point cloud.

71 We illustrate this standard through three examples representing increasing levels of processing. The
72 corresponding metadata are summarised in the final columns of the Table. Point clouds in LAS format
73 (“LAS Specification 1.4,” n.d.), accompanied by a GeoJSON metadata file, are available in the
74 supplementary material. It is recommended that the metadata be gathered in GeoJSON files with the same
75 name, due to current working practices, which generally associate the GeoJSON file with other data and
76 systematically accompany it with its master file (“Earth Observation GeoJSON (EO-GeoJSON) Standard |
77 OGC,” n.d.). It is therefore a suitable format for carrying metadata alongside point clouds. The following
78 sections describe each stage of the standard in detail, from the essential perimeter to more advanced
79 processing steps, with justification for the associated metadata at each level.
<figure><img src="image_2.png" /><figcaption>Figure 1: Flowchart summarising the TLS data publication standard</figcaption></figure>

This flowchart is divided into three parts: an essential perimeter (from acquisition to georeferencing), pre-processing (decimation, filtering, masking, classification) and processing, in which only the two most common analyses are presented: feature extraction and change detection. The white boxes are mandatory, while the blue boxes only become mandatory if the respective pre-processing or processing stage have been performed. Each box has a set of metadata detailed in the supplementary material. The same colour code is used throughout.
2. Essential perimeter: 81

The essential perimeter, which includes acquisition, raw data and quality control, conversion from proprietary to opensource format and georeferencing, is a fundamental stage, without which the cloud cannot be used. Passalacqua et al. (2015) propose a series of questions to be asked before the acquisition of HRT (High Resolution Topography) data and the analysis of an existing cloud. These questions relate in particular to data acquisition and cloud georeferencing. The following metadata are also based on the recommendations made for SfM photogrammetry by James et al. (2019), adapted here to LiDAR methods. 82
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2.1. Acquisition metadata: 88

This includes information on the study area, the spatial and temporal references, the material and the survey design which must be coherent with the objective of the study, as well as the responsible organizations and the conditions of the survey. In particular, it specifies the location and extent of the surveyed area, the coordinate system used (whether local or global), and the georeferencing elements (targets, control points). This information helps to replace the data in the context in which it was collected, to anticipate any errors associated with the measurement equipment and to ensure a correct interpretation. 89
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2.2. Data description: 96

A precise description of the published point cloud is required. This description must include the X, Y, and Z coordinates in a specified reference frame (see georeferencing section), as well as any scalar fields (the most common being reflectance, phi, theta, time, range, intensity, etc.), distinguishing between those measured during acquisition and those resulting from processing (see processing section). 97
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An assessment of the overall quality of the raw data is recommended (Passalacqua et al., 2015). This evaluation includes the total number of points and their distribution (refer to supplementary material for density formulas), the detection of any areas with particularly low point density, and the presence of artefacts or occluded zones, as illustrated in Figure 2. These can result either from transient occlusions (e.g., clouds, flying birds, passing cars) or from permanent occlusions linked to the relative scanning position respective to obstacles and environment (e.g., foreground vegetation, shadow areas). This initial assessment is essential to ascertain the reliability of the information obtained. 101
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108
<figure><img src="image_3.png" /><figcaption>Figure 2: Occlusions and uncertainties in TLS acquisitions

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111 The diagram illustrates common artefacts in TLS data acquisition, such as occlusions caused by surface geometry (e.g. hidden recesses), absorption of laser beams by atmospheric elements (e.g. clouds), and partially hidden surfaces. A zoom on a reference target shows the dispersion of measured points around the mean plane, along with a histogram of their distances to that plane, illustrating the relative positioning uncertainty (precision) in the point cloud.
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115</figcaption></figure>

### 2.3. Conversion to interoperable format:

117 The raw formats obtained are, in most cases, proprietary formats that cannot be read outside of proprietary software. It is therefore necessary to convert point clouds into an open-source and interoperable format.
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119 The Open Geospatial Consortium (OGC) and the American Society of Photogrammetry and Remote Sensing (ASPRS) endorsed the LAS format as the standard for airborne LiDAR data, which is open and fully documented (“LAS Specification 1.4,” n.d.). We also note that OpenTopography and the French geographical survey (IGN) use the LAZ format (Eickenberg, 2024), which is a compact extension of the LAS format. Being widely used, we recommend them. A human readable format such as TXT can also be used.
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123 While far less compact and rich, this option ensures the durability of the data. In all instances, the conversion process and the employed libraries for converting proprietary format point clouds into interoperable files must be documented. Libraries’ examples are provided in the supplementary material.
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### 2.4. Georeferencing:

128 This is a particularly important stage in geomorphology for tracking Earth surface phenomena, as incorrect georeferencing can result in the inaccurate calculation of changes. Georeferencing involves the repositioning of the cloud within its spatial and contextual framework, a task that is imperative for future understanding and use of the cloud. Ideally, the data should be provided in a way that makes it interpretable within an explicit and non-ambiguous system. ESPG provides one such system matching a 4 to 5 digit code with coordinate systems. Given the storage space required for full-coordinate datasets, it is often more efficient to provide point clouds in local coordinates, with a Global Shift that allows accurate conversion into the identified reference system.
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136 Three main instances can be applied for georeferencing (Passalacqua et al., 2015):
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1. Direct methods, which involve recording the sensor's position during acquisition and propagating this information to each point;

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2. Indirect methods which match well known georeferenced targets with their image deduced from LiDAR cloud;

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3. No georeferencing (not recommended). In this case, the minimum to provide is a shapefile or a WKT polygon delimiting the scanned area, along with the corresponding EPSG code. While the scale may be known, the origin (Ox, Oy, Oz) and orientation (omega, phi, kappa) typically remain undefined. However, contextual metadata such as the extent of the cloud or reference directions, may allow for the reconstruction of a transformation matrix, enabling georeferencing by solving for the origin and rotation relative to reference axes.

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Whichever method is used, it must be clearly disclosed and explained. The required metadata are listed in the supplementary material.

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Example 1 illustrates a point cloud published with the bare minimum i.e. a cloud in opensource format, LAZ, without georeferencing, but accompanied by a polygon in KML format representing a concave footprint (Figure 3). The point cloud was acquired at La Grande Roche, located in the vicinity of Nantua (01130) in France (Auvergne-Rhône-Alpes region, in the Jura mountain range). The objective of this acquisition is to extract relative measurements from the cloud, in particular the dimensions of the compartments irrespective of its absolute location, so pre-processing is not necessary. The associated metadata, its GeoJSON file and location polygon are available in the supplementary material.

<figure><img src="image_4.png" /><figcaption>Figure 3: KML polygon delimiting the scanned area, EPSG: 4979 (example 1, Nantua)
Minimum information to provide when the TLS point cloud is not georeferenced.</figcaption></figure>

Source: www.geoportail.gouv.fr

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## 2.5. *Quality assessment*:

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Error assessment is a crucial step in evaluating the quality of georeferenced point clouds, in particular to quantify the uncertainties associated with point positioning. In the case of a single cloud with no known independent checkpoints with which to compare measurements, this step is not possible (James et al., 2019). However, it becomes essential in the case of multi-station acquisitions to avoid internal distortion of the cloud. These errors include accuracy, which corresponds to the difference between the measured position and the reference position, and precision, which measures the dispersion of the points around the mean, indicating the repeatability of the measurements (Figure 2).
167 In the absence of checkpoints, precision (i.e. relative positioning) can be assessed by comparing two
168 clouds of points in absence of change in the morphology, e.g. two successive measurements on the same
169 area with time delay short enough to rule out the fortuitous occurrences of significant changes. By
170 calculating the standard deviation, which corresponds to the dispersion of the points around the mean,
171 an estimate of the relative positioning is obtained.

172 Regarding accuracy (absolute positioning), it can be calculated using several parameters such as the
173 mean error, the Root Mean Square error (RMSE), quantiles (median, 68%, 95%) or Normalised Median
174 Absolute Deviation (NMAD, Höhle and Höhle, 2009)), which gives a robust estimate of the error. More
175 advanced statistical methods, including quantiles regression (Koenker, 2005) or block bootstrapping
176 (Lahiri, 2013), have been developed to account for the spatial variability of errors and improve accuracy
177 description despite complex morphological contexts.

178 It is also important to specify the spatial distribution of errors, which enables to map the areas where
179 uncertainty is greatest (Höhle and Höhle, 2009; James et al., 2017). The latter can be uniform, which is
180 often approximated by assuming that the error is linked solely to the sensor and the acquisition geometry,
181 or uniform by zone, taking into account local conditions (Passalacqua et al., 2015).

182 **3. Preprocessing:**

183 Pre-processing refers to all operations required to correct and filter point clouds, by removing or flagging
184 geometric features, with the objective of optimising subsequent processing. If the shared data has been
185 pre-processed, it is essential that all the employed operations and methods are explicitly indicated, in
186 addition to the software and libraries that have been used. Among the pre-processing operations,
187 decimation reduces the number of points using point removal or refinement techniques, with the aim of
188 reducing computation time and storage size. Geometric masks can also be applied to remove low
189 relevance areas, thereby optimising data relevance.

190 Filters can be used to remove erroneous points and improve the quality of the data. There are several types
191 of filters, the use of which must be justified. These include filters based on morphological criteria, or filters
192 based on progressive densification starting from seeds (Passalacqua et al., 2015). An overview of the
193 various methods that may be employed is provided in the flowchart.

194 Another significant pre-processing operation is classification. This operation involves the classification of
195 points in the cloud according to the type of object to which they belong, including soil, vegetation, water,
196 buildings, etc. This stage is of particular importance in geomorphological studies of natural environments,
197 as the observation of variations in the soil or rock is dependent on this preliminary classification stage.
198 Several methods can be considered:

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* Edge-based algorithms, which involve the detection of boundaries between features (Sappa and Devy (2001)).

* Region growing algorithms: clustering of points belonging to the same edge delimited surface, e.g. the connected components method, proposed by Rabani et al. (2006)

* Algorithms specialised in the detection of primitive shapes: Hough Transform (Hough, 1962), Random Sample Consensus method (RANSAC, Fischler and Bolles, 1981).

• Machine learning methods:

* Unsupervised algorithms: K-mean clustering (Hartigan and Wong, 1979), Principal Component Analysis (PCA, Yang et al. (2020)), hierarchical clustering methods such as the one proposed by Lu et al. (2016), called Pairwise Linkage.

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* Supervised algorithms: CGAL (David et al., n.d.), 3DMASC (Letard et al., 2024), Canupo (Brodu and Lague, 2012).
211 It is important to note that the aforementioned methods are not exhaustive, and that others may be used.
212 Any can be used as long as they are clearly described, and the parameters are made explicit. Once the
213 classification has been carried out, certain classes may be deleted, which is often done to remove
214 vegetation from the surveyed areas. If devegetation has been conducted, this should be indicated, with the
215 employed methodology described.

216 In case the classification values used do not comply with the LAS standard (e.g. 1: unclassified, 2: ground,
217 3: low vegetation, etc., “LAS Specification 1.4,” n.d.), the scalar field values used should be specified. It is
218 recommended to use numerical values in the range 64 or higher to avoid conflicts with standard LAS
classes. Even when only a single class is retained, including a scalar field remains preferable to ensure the
219 nature of the points is explicitly described.
220

221 Example 2 illustrates a point cloud resulting from a multi-station acquisition, in which several individual
222 scans have been merged and co-registered (Erreur ! Source du renvoi introuvable.). In such cases, it is
223 essential to include a scalar field identifying acquisition station origin. The dataset was collected on chalk
224 cliffs at Mesnil-Val, Normandie, France (Dewez et al., 2013). Several pre-processing operations were
225 performed, including filtering and classification. The point cloud in LAS format and the GeoJSON
226 associated metadata file are available in the supplementary material.

<figure><img src="image_5.png" /><figcaption>Figure 4: LiDAR 3D point cloud of the chalk cliffs at Mesnil-Val, France.
A) Point cloud colored by acquisition station (one color per station). The black squares indicate the positions of the terrestrial laser scanner during acquisition. B) Zoom on the overlapping area between scans from stations 3 and 4. A shadow zone is visible for station 4, caused by a cliff overhang.</figcaption></figure>

227
228 **4. Processing:**
229
A multitude of processing possibilities exists. In the following section we describe two of the most
230 common: feature extraction (identification of geomorphological features) and change detection
231 (quantification of the positional differences between two clouds at two different epochs). In the event of
232 the use of any other processing method, it will be necessary to describe it, detail the parameters by
specifying whether the values used are software default values or not, and detail the characterisation of
uncertainties. The objective of this approach is to ensure the transparency of data processing and,
consequently, to evaluate its relevance for the study and for new research objectives.

## 4.1. Feature extraction:

This processing is carried out on a single point cloud. This method allows specific geomorphological
features to be extracted. The initial step involves the formulation of a research question, i.e. the
identification of the object to be studied and the purpose of the study. The identification process can be
based on continuous fields (e.g. intensity, relief) or on discrete objects, such as channel heads (Lashermes
et al., 2007; Pelletier, 2013; Sofia et al., 2011), fluvial terraces (Stout and Belmont, 2014), landslides (Booth
et al., 2009; Tarolli et al., 2012), channel morphology (Fisher et al., 2013; Passalacqua et al., 2012),
vegetation characteristics (Arrizza et al., 2024; Brandtberg, 2007; Yang et al., 2020), etc. The extraction of
these elements necessitates the implementation of selection and segmentation techniques. Further
details regarding these methodologies can be found in the section dedicated to classification. As part of
this process, two distinct results can be obtained: either continuous surfaces or a discretised set of values
isolating distinct objects. It is imperative to specify the criteria employed for the identification of
geomorphological features. Furthermore, the use of complementary data, such as satellite images or field
observations, to refine the identification of features, must be explicitly indicated (Giuliano et al., 2022).

For instance, the FACETS method (Dewez et al., 2016) is a segmentation technique for extracting sets of
planar surfaces from unstructured 3D point clouds, particularly for structural geology analysis such as
identifying fracture systems. This method is based on an initial division of the cloud into locally flat cells
using two possible approaches: a k-d tree or a fast-matching method. In both cases, cells are defined as
sufficiently flat if they meet a roughness threshold, which is generally expressed as the root mean square
(RMS) value between the points and the fitted plane. These elementary facets are then clustered into larger
planes according to the orientation of their normals and subsequently assembled into families of parallel
planes. However, a limitation of the FACETS method is the lack of an inverse operation to reassign original
point cloud identifiers to the extracted facets, thus losing the link to the original data. This would allow the
transfer of facet data (e.g., identifiers, area, deviation, centroid, and dip/dip direction) to the original points
in the cloud.

## 4.2. Change detection:

This processing allows the diachronic comparison of two clouds to identify changes in the study area
between two specific dates. In instances where multiple clouds are used in the analysis (two or more), it is
imperative to provide the metadata for all the clouds and to describe the pre-processing of each in detail.
The selection of the appropriate cloud to be compared with another is then necessary. They both need to
have an adequate density, consistent with the scientific objective and the dimensions of the object of
interest, and to be in the same coordinate system. The reference cloud is defined as a cloud of high quality
that serves as a reliable basis for the analysis. The compared cloud, meanwhile, is compared with the
reference cloud in order to detect any changes between the two clouds' dates.

Two categories of change can be distinguished: (1) the displacement of geomorphological features,
including landslide displacement fields, earthquake deformation or ice flow velocities; and (2) the
modification of geomorphological features, such as erosion, sedimentation, rockfalls or other phenomena.

### 1. Displacement of geomorphological features:

To track ground movement, realignment of point clouds is required so as to track features
displacement. This process results in a 2D or 3D displacement field. A number of methods can be
used to do this, such as 3D piecewise iterative closest point (ICP, Nissen et al., 2012; Teza et al.,
2007), point cloud cross-correlation (Borsa and Minster, 2012;nut et al., 2018) or 2D
278 correlation techniques such as Particle Image Velocimetry (PIV, Aryal et al., 2012; Lyda et al., 2016)
279 and optical flow approaches (Brigot et al., 2016).
280

281 2. Modification of geomorphological features:

282 It is first necessary to ensure that the two clouds are perfectly aligned. This alignment can be
283 achieved by employing the aforementioned alignment methods. If the cloud comparison method
284 requires the calculation of normals, it is essential to specify the method used (e.g. primitive type
285 such as plane fitting), the associated parameters (e.g. search radius for surface reconstruction),
286 and the direction in which they were calculated. This type of change can be quantified by
287 calculating distances and volumes, some methods of which are given in the following. In the
288 context of quantifying alterations to planar surfaces, such as river banks or glaciers, the utilisation
289 of 2.5 DTMs and the implementation of vertical grid subtraction, also known as DEM of difference,
290 can be used (DoD, Li et al., 2024; Schürch et al., 2011; Staley et al., 2014). Furthermore, in the
291 context of three-dimensional surface modification calculations, it is advantageous to employ
292 point clouds, as this method preserves three-dimensional accuracy and avoids the loss of
293 information caused by interpolation. The most common method, termed cloud-to-mesh, involves
294 the calculation of the distance between a cloud of points and a 3D reference triangulated mesh
295 (C2M, Cignoni et al., 1998; Olsen et al., 2010). However, the creation of such a mesh can be
296 complex for rough surfaces or incomplete point clouds, for instance due to occlusions. In
297 particular, Lim et al. (2005) noted that the greatest evaluated changes often occur within the
298 interpolation polygons at the edges of the point cloud, where data is sparse or missing.
299 Consequently, another approach involves the direct estimation of distances between two point
300 clouds. This approach, commonly referred to as cloud-to-cloud (C2C, Girardeau-Montaut et al.,
301 2005), can be achieved by calculating the closest point distance, which is a rapid method because
302 it does not require normal computation. Finally, the Multiscale Model to Model Cloud Comparison
303 method (M3C2), developed by Lague et al. (2013), can be used to detect smaller changes and
304 assess whether they are statistically significant. A more recent approach adapts the M3C2
305 algorithm by using planar surface correspondences to reduce uncertainty in rough environments
306 (Zahs et al., 2022).

307 The processing of data must also be subject to quality assessment. In instances where processing involves
308 multiple point clouds, it is necessary to estimate the error for each cloud independently. Then, the errors
309 for each cloud can be propagated. In the context of change detection, for instance, the residual error can
310 be estimated by calculating the square root of the sum of errors in quadrature (Passalacqua et al., 2015).

311 As illustrated in Example 3, a diachronic comparison is made between two point clouds of the St-Eynard
312 cliff, located in the Auvergne-Rhône-Alpes region of France (Figure 5). The acquisitions were conducted on
313 6th and 18th December 2023, as part of a study on rock fall detection (Manceau et al., 2025). The two point
314 clouds are provided in open-source format and are georeferenced to EPSG 2154. They were pre-processed
315 by applying a geometric mask and de-vegetation using the CGal algorithm (David et al., n.d.). The distance
316 calculation method employed for the comparison is the M3C2 method, and the associated parameters are
detailed in the GeoJSON file.
317
<figure><img src="image_6.png" /><figcaption>Figure 5: LiDAR 3D point cloud of the St Eynard cliff, Chartreuse Massif</figcaption></figure>

A) Change detection using the M3C2 method between 2023/12/06 and 2023/12/18. Significant changes (i.e., block falls) are shown in red. B) Close-up view of the largest significant change. The black line indicates the cross-section displayed in C. C) Cross-section: the surface from 2023/12/06 is shown in red and the surface from 2023/12/18 in blue. A volume of 420 m³ detached between these two dates.

## 5. Discussion

The metadata standard proposed in this work aligns with existing international recommendations for the description and sharing of geographic and observational data. In particular, it is consistent with the core concepts defined by ISO 19156 ("ISO 19156," 2023). According to this definition, an observation is defined as the process of determining the value of a property related to a geographic feature, using a specific procedure, an algorithm, or a processing chain. The metadata fields defined in our standard incorporate the key elements identified in ISO 19156: the feature of interest (e.g., rock cliff), the observed property (e.g., surface displacement, rock fall), the procedure used (e.g., terrestrial laser scanning, M3C2), as well as the phenomenon time and result time associated with the observation. These temporal elements are among the metadata required and are detailed in the supplementary material. The valid time, which defines the
333 period during which a result remains interpretable, is considered open-ended — results are assumed valid
334 until new developments justify reprocessing.

335 The structure of the proposed metadata is also consistent with ISO 19115 (“ISO 19115-1,” 2014), which
336 establishes the general framework for describing digital geographic resources. While ISO 19115 provides a
337 flexible and comprehensive structure, our proposal adapts it to the context of 3D point cloud data in
338 geomorphological studies, by specifying fields that are particularly relevant to these datasets—such as
339 acquisition conditions, processing methods, and known limitations.

340 **6. Conclusion and perspectives**

341 The establishment of a standard for the publication of TLS data is an essential step in ensuring compliance
342 with the FAIRt principles. By proposing a structured and detailed framework for metadata, this standard
343 promotes the sustainability, sharing and reuse of point clouds in geomorphological studies following open
344 science principles.

345 Although developed for TLS, the principles outlined here could potentially be extended to point clouds
346 derived from photogrammetry. However, in such instances, it would be necessary to complement the data
347 with detailed information on the photogrammetric workflow, including image acquisition, processing
348 parameters, and reconstruction methods.

349 Data sustainability also depends on maintaining and updating formats like LAS and anticipating future
350 developments. As pre-processing and processing techniques evolve, particularly due to advances in
351 artificial intelligence and new methods for estimating uncertainties, the proposed standard will also need
352 to remain adaptable. Regular updates to metadata structures will be necessary to reflect evolving scientific
353 practices.

354 Beyond the act of structuring publications, this work must be part of long-term preservation strategies.
355 Specialised scientific repositories, especially in the geosciences, play a central role. International and
356 open-access platforms such as OpenTopography or PANGAEA offer reliable solutions for archiving and
357 disseminating datasets, while ensuring compliance with FAIRt principles and broad accessibility.

358 This standard, therefore, should not be regarded as a static framework, but as a shared foundation upon
359 which to construct a collaborative, evolving, and sustainable approach to the publication of TLS data. Its
360 evolution will depend on user feedback, emerging needs, and new possibilities offered by digital tools. The
361 allocation of funding towards the maintenance of the standard, as well as structures dedicated to
362 exchanges within the community, will also be a contributing factor.

363 **Credit authorship contribution statement**

364 **S. Susini:** Conceptualization, Writing – original draft, Writing – review and editing, Visualization. **C. Lévy:**
365 Conceptualization, Writing – review and editing, Supervision, Project administration, Funding acquisition.
366 **M.A.nut:** Conceptualization, Writing – review and editing, Supervision, Funding acquisition. **L.**
367 **Manceau:** Conceptualization, Writing – review and editing. **D. Amitrano:** Conceptualization, Writing –
368 review and editing, Supervision, Funding acquisition. **T. Dewez:** Conceptualization, Writing – review and
369 editing, Supervision, Funding acquisition.

370 **Acknowledgments/financial support**

371 This research was funded by the French National Research Agency (ANR) under the project C2R-IA
372 (https://anrc2ria.fr/), grant ANR-22-CE56-0005-06.

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</table>