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Semantic segmentation of urban textured meshes through point sampling / Grégoire Grzeczkowicz in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 177 - 184 Titre : Semantic segmentation of urban textured meshes through point sampling Type de document : Article/Communication Auteurs : Grégoire Grzeczkowicz , Auteur ; Bruno Vallet , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 177 - 184 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] échantillonnage de données [Termes IGN] maillage [Termes IGN] maille carrée [Termes IGN] maille triangulaire [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] traitement de semis de points Mots-clés libres : maille texturée (maille qui porte l'information géométrique et radiométrique) Résumé : (auteur) Textured meshes are becoming an increasingly popular representation combining the 3D geometry and radiometry of real scenes. However, semantic segmentation algorithms for urban mesh have been little investigated and do not exploit all radiometric information. To address this problem, we adopt an approach consisting in sampling a point cloud from the textured mesh, then using a point cloud semantic segmentation algorithm on this cloud, and finally using the obtained semantic to segment the initial mesh. In this paper, we study the influence of different parameters such as the sampling method, the density of the extracted cloud, the features selected (color, normal, elevation) as well as the number of points used at each training period. Our result outperforms the state-of-the-art on the SUM dataset, earning about 4 points in OA and 18 points in mIoU. Numéro de notice : A2022-427 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2022-177-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-177-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100733 [article]

Efficient dike monitoring using terrestrial SFM photogrammetry / Laurent Froideval in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 359 - 366 Titre : Efficient dike monitoring using terrestrial SFM photogrammetry Type de document : Article/Communication Auteurs : Laurent Froideval, Auteur ; Christophe Conessa, Auteur ; Xavier Pellerin Le Bas, Auteur ; Laurent Benoit, Auteur ; Dominique Mouazé, Auteur Année de publication : 2022 Article en page(s) : pp 359 - 366 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] digue [Termes IGN] sable [Termes IGN] semis de points [Termes IGN] série temporelle [Termes IGN] structure-from-motion [Termes IGN] surveillance d'ouvrage Résumé : (auteur) Nature based solutions are growing rapidly in order to mitigate in the near future the effects of climate change and rise of sea level on most anthropogenic coasts. In that frame, the CHERbourg bLOC (CHERLOC) project aims to study new coastal engineering solutions (overtopping, sediment transport) thanks to two new artificial units in two test sites (Normandy, France) considering biodiversity preservation but also societal acceptability. This study details an efficient method to monitor such coastal infrastructure using terrestrial Structure from Motion (SfM). In 2021, surveys were conducted to acquire pictures in April, May, June and November. A time series of 3D photogrammetric models was generated using open source SfM software. The first model was georeferenced using Ground Control Points (GCP) measured by Differential Global Navigation Satellite System (DGNSS) so that it could be used as a reference for the following point clouds using surrounding ripraps assumed to be non-mobile through the period of the study. The georeferencing Root Mean Square Error (RMSE) was found to be 1.8 cm for the April model whereas RMSEs of relative registrations of the following dates were found to be sub-centimetric. These results can be used to observe and measure blocks displacements as well as sand volumes evolution throughout the time series. The biggest displacement was found to be 23 cm between April and June. Sand topographic variation shows a continuous accumulation on selected cross-sections between April and November with an overall height accumulation of about 30 cm. Sand volumes measurements show consistent results with an added volume of 3.67 m3 on the previous areas. Numéro de notice : A2022-429 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-359-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-359-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100734 [article]

Vegetation cover mapping from RGB webcam time series for land surface emissivity retrieval in high mountain areas / Benedikt Hiebl in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 367 - 374 Titre : Vegetation cover mapping from RGB webcam time series for land surface emissivity retrieval in high mountain areas Type de document : Article/Communication Auteurs : Benedikt Hiebl, Auteur ; Andreas Mayr, Auteur ; Andreas Kollert, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 367 - 374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte de la végétation [Termes IGN] données de terrain [Termes IGN] emissivité [Termes IGN] flore alpine [Termes IGN] image RVB [Termes IGN] image thermique [Termes IGN] modèle numérique de surface [Termes IGN] montagne [Termes IGN] série temporelle [Termes IGN] température au sol [Termes IGN] variation saisonnière Résumé : (auteur) Land Surface Temperature (LST) products from thermal infrared imaging rely on information about the spatial distribution of Land Surface Emissivity (LSE). For portable, broadband thermal cameras for drone- or ground-based measurements with camera to object distances up to a few kilometres and with meter-scale resolution, threshold-based retrieval of LSE from Fractional green Vegetation Cover (FVC) can be used. As seasonal changes in vegetation LSE over the year cannot be accounted for by single satellite images or aerial orthophotos, this study evaluates an approach for FVC retrieval via permanently installed RGB webcams and derived Excess Green vegetation index (ExG) time series at a high-mountain test site in the European Alps. Daily ExG values were derived from the imagery of 27 days between 12/07/2021 and 30/10/2021 and projected to a 0.5 m Digital Surface Model (DSM). FVC reference data from 765 in-situ vegetation plots were used to assess the relationship between ExG and the vegetation cover and to determine the thresholds of ExG for no vegetation cover and full vegetation cover. Despite the bad correlation between ExG and in-field FVC with an R² score of 0.15, an approach using a well-tested orthophoto-retrieved NDVI for FVC retrieval performs just slightly better. The comparison of the remotely sensed data and the field measurements therefore remains complex. Time series analysis of both ExG and FVC for highly vegetated areas showed a significant decrease from summer to autumn, which reflects the seasonal changes of LSE for senescent vegetation. Calculated emissivities for vegetated pixels ranged from the minimum of 0.95 to the maximum of 0.985 over the season, while emissivity values for less vegetated pixels stayed constant during the season. The results of this study will be used as input to a correction model for remote LST measurements in the context of micro-scale investigations of the thermal niche of Alpine flora. Numéro de notice : A2022-428 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-367-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-367-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100735 [article]

Automatic training data generation in deep learning-aided semantic segmentation of heritage buildings / Arnadi Murtiyoso in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 317 - 324 Titre : Automatic training data generation in deep learning-aided semantic segmentation of heritage buildings Type de document : Article/Communication Auteurs : Arnadi Murtiyoso, Auteur ; Francesca Matrone, Auteur ; M.C. Martini, Auteur ; Andrea Lingua, Auteur ; Pierre Grussenmeyer, Auteur ; Roberto Pierdicca, Auteur Année de publication : 2022 Article en page(s) : pp 317 - 324 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] monument historique [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (auteur) In the geomatics domain the use of deep learning, a subset of machine learning, is becoming more and more widespread. In this context, the 3D semantic segmentation of heritage point clouds presents an interesting and promising approach for modelling automation, in light of the heterogeneous nature of historical building styles and features. However, this heterogeneity also presents an obstacle in terms of generating the training data for use in deep learning, hitherto performed largely manually. The current generally low availability of labelled data also presents a motivation to aid the process of training data generation. In this paper, we propose the use of approaches based on geometric rules to automate to a certain degree this task. One object class will be discussed in this paper, namely the pillars class. Results show that the approach managed to extract pillars with satisfactory quality (98.5% of correctly detected pillars with the proposed algorithm). Tests were also performed to use the outputs in a deep learning segmentation setting, with a favourable outcome in terms of reducing the overall labelling time (−66.5%). Certain particularities were nevertheless observed, which also influence the result of the deep learning segmentation. Numéro de notice : A2022-430 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-317-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-317-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100736 [article]

K-means clustering based on omnivariance attribute for building detection from airborne lidar data / Renato César Dos santos in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 111 - 118 Titre : K-means clustering based on omnivariance attribute for building detection from airborne lidar data Type de document : Article/Communication Auteurs : Renato César Dos santos, Auteur ; Mauricio Galo, Auteur ; A.F. Habib, Auteur Année de publication : 2022 Article en page(s) : pp 111 - 118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par nuées dynamiques [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] morphologie mathématique [Termes IGN] semis de points Résumé : (auteur) Building detection is an important process in urban applications. In the last decades, 3D point clouds derived from airborne LiDAR have been widely explored. In this paper, we propose a building detection method based on K-means clustering and the omnivariance attribute derived from eigenvalues. The main contributions lie on the automatic detection without the need for training and optimal neighborhood definition for local attribute estimation. Additionally, one refinement step based on mathematical morphology (MM) operators to minimize the classification errors (commission and omission errors) is proposed. The experiments were conducted in three study areas. In general, the results indicated the potential of proposed method, presenting an average Fscore around 97%. Numéro de notice : A2022-431 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-111-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-111-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100737 [article]

Deep learning for the detection of early signs for forest damage based on satellite imagery / Dennis Wittich in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 307 - 315 Titre : Deep learning for the detection of early signs for forest damage based on satellite imagery Type de document : Article/Communication Auteurs : Dennis Wittich, Auteur ; Franz Rottensteiner, Auteur ; Mirjana Voelsen, Auteur ; Christian Heipke, Auteur ; Sönke Müller, Auteur Année de publication : 2022 Article en page(s) : pp 307 - 315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] dégradation de la flore [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] fonction de perte [Termes IGN] image Sentinel-MSI [Termes IGN] régression [Termes IGN] série temporelle [Termes IGN] surveillance forestière Résumé : (auteur) We present an approach for detecting early signs for upcoming forest damages by training a Convolutional Neural Network (CNN) for the pixel-wise prediction of the remaining life-time (RLT) of trees in forests based on Sentinel-2 imagery. We focus on a scenario in which reference data are only available for a related task, namely for a bi-temporal pixel-wise classification of forest degradation. This reference is used to train a CNN for the pixel-wise prediction of forest degradation. In this context, we propose a new sub-sampling-based approach for compensating the effects of a heavy class imbalance in the training data. Using the resulting classification model, we predict semi-labels for images of a Sentinel-2 time series, from which training data for a CNN designed to regress the RLT can be derived after some label cleansing. However, due to data gaps in the time series, e.g. caused by clouds, only intervals can be derived for the target variable to be regressed, and for some training pixels one of the interval limits may even be unknown. Consequently, we propose a new loss function for training a CNN for regressing the RLT that only requires the known interval limits. The method is evaluated on a data set in Germany, covering a time-span of 5 years. We show that the proposed sub-sampling strategy for dealing with strong label imbalance when training the classifier significantly reduces the training time compared to other approaches. We further show that our model predicts the RLT with a maximum error of two months for 80% of the forest pixels that die within one year from the acquisition date of the Sentinel-2 image. Numéro de notice : A2022-432 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-307-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-307-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100738 [article]

Railway lidar semantic segmentation with axially symmetrical convolutional learning / Antoine Manier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 135 - 142 Titre : Railway lidar semantic segmentation with axially symmetrical convolutional learning Type de document : Article/Communication Auteurs : Antoine Manier, Auteur ; Julien Moras, Auteur ; Jean-Christophe Michelin , Auteur ; Hélène Piet-Lahanier, Auteur Année de publication : 2022 Article en page(s) : pp 135 - 142 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] scène 3D [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] voie ferrée Résumé : (auteur) This paper presents a new deep-learning-based method for 3D Point Cloud Semantic Segmentation specifically designed for processing real-world LIDAR railway scenes. The new approach relies on the use of spatial local point cloud transformations for convolutional learning. These transformations allow an increased robustness to varying point cloud densities while preserving metric information and a sufficient descriptive ability. The resulting performances are illustrated with results on railway data from two distinct LIDAR point cloud datasets acquired in industrial settings. The quality of the extraction of useful information for maintenance operations and topological analysis is pointed together with a noticeable robustness to point cloud variations in distribution and point redundancy. Numéro de notice : A2022-433 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-135-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-135-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100739 [article]

Effect of label noise in semantic segmentation of high resolution aerial images and height data / Arabinda Maiti in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 275 - 282 Titre : Effect of label noise in semantic segmentation of high resolution aerial images and height data Type de document : Article/Communication Auteurs : Arabinda Maiti, Auteur ; Sander J. Oude Elberink, Auteur ; M. George Vosselman, Auteur Année de publication : 2022 Article en page(s) : pp 275 - 282 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] bruit (théorie du signal) [Termes IGN] données altimétriques [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image à très haute résolution [Termes IGN] image aérienne [Termes IGN] orthoimage [Termes IGN] segmentation sémantique Résumé : (auteur) The performance of deep learning models in semantic segmentation is dependent on the availability of a large amount of labeled data. However, the influence of label noise, in the form of incorrect annotations, on the performance is significant and mostly ignored. This is a big concern in remote sensing applications, wherein acquired datasets are spatially limited, labeling is done by domain experts with possible sources of high inter-and intra-observer variability leading to erroneous predictions. In this paper, we first simulate the label noise while conducting experiments on two different datasets with very high-resolution aerial images, height data, and inaccurate labels, responsible for the training of deep learning models. We then focus on the effect of these noises on the model performance. Different classes respond differently to the label noise. The typical size of an object belonging to a class is a crucial factor regarding the class-specific performance of the model trained with erroneous labels. Errors caused by relative shifts of labels are the most influential label errors. The model is generally more tolerant of the random label noise than other label errors. It has been observed that the accuracy gets reduced by at least 3% while 5% of label pixels are erroneous. In this regard, our study provides a new perspective of evaluating and quantifying the propagation of label noise in the model performance that is indeed important for adopting reliable semantic segmentation practices. Numéro de notice : A2022-434 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-275-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-275-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100741 [article]

Learning from the past: crowd-driven active transfer learning for semantic segmentation of multi-temporal 3D point clouds / Michael Kölle in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 259 - 266 Titre : Learning from the past: crowd-driven active transfer learning for semantic segmentation of multi-temporal 3D point clouds Type de document : Article/Communication Auteurs : Michael Kölle, Auteur ; Volker Walter, Auteur ; Uwe Soergel, Auteur Année de publication : 2022 Article en page(s) : pp 259 - 266 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données étiquetées d'entrainement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] données multitemporelles [Termes IGN] orthoimage couleur [Termes IGN] production participative [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] traitement de données localisées Résumé : (auteur) The main bottleneck of machine learning systems, such as convolutional neural networks, is the availability of labeled training data. Hence, much effort (and thus cost) is caused by setting up proper training data sets. However, models trained on specific data sets often perform unsatisfactorily when used to derive predictions for another (yet related) data set. We aim to overcome this problem by employing active learning to iteratively adapt an existing classifier to another domain. Precisely, we are concerned with semantic segmentation of 3D point clouds of multiple epochs. We first establish a Random Forest classifier for the first epoch of our data set and adapt it for successful prediction to two more temporally disjoint point clouds of the same but extended area. The point clouds, which are part of the newly introduced Hessigheim 3D benchmark data set, incorporate different characteristics with respect to the acquisition date and sensor configuration. We demonstrate that our workflow for domain adaptation is designed in such a way that it i) offers the possibility to greatly reduce labeling effort compared to a passive learning baseline or to an active learning baseline trained from scratch, if the domain gap is small enough and ii) at least does not cause more expenses (compared to a newly initialized active learning loop), if the domain gap is severe. The latter is especially beneficial in scenarios where the similarity of two different domains is hard to assess. Numéro de notice : A2022-435 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-259-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-259-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100743 [article]

Virtual laser scanning of dynamic scenes created from real 4D topographic point cloud data / Lukas Winiwarter in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 79 - 86 Titre : Virtual laser scanning of dynamic scenes created from real 4D topographic point cloud data Type de document : Article/Communication Auteurs : Lukas Winiwarter, Auteur ; Katharina Anders, Auteur ; Daniel Schröder, Auteur ; Bernhard Höfle, Auteur Année de publication : 2022 Article en page(s) : pp 79 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] filtre de Kalman [Termes IGN] modèle de simulation [Termes IGN] scène 3D [Termes IGN] scène virtuelle [Termes IGN] semis de points [Termes IGN] Tyrol (Autriche) Résumé : (autuer) Virtual laser scanning (VLS) allows the generation of realistic point cloud data at a fraction of the costs required for real acquisitions. It also allows carrying out experiments that would not be feasible or even impossible in the real world, e.g., due to time constraints or when hardware does not exist. A critical part of a simulation is an adequate substitution of reality. In the case of VLS, this concerns the scanner, the laser-object interaction, and the scene. In this contribution, we present a method to recreate a realistic dynamic scene, where the surface changes over time. We first apply change detection and quantification on a real dataset of an erosion-affected high-mountain slope in Tyrol, Austria, acquired with permanent terrestrial laser scanning (TLS). Then, we model and extract the time series of a single change form, and transfer it to a virtual model scene. The benefit of such a transfer is that no physical modelling of the change processes is required. In our example, we use a Kalman filter with subsequent clustering to extract a set of erosion rills from a time series of high-resolution TLS data. The change magnitudes quantified at the locations of these rills are then transferred to a triangular mesh, representing the virtual scene. Subsequently, we apply VLS to investigate the detectability of such erosion rills from airborne laser scanning at multiple subsequent points in time. This enables us to test if, e.g., a certain flying altitude is appropriate in a disaster response setting for the detection of areas exposed to immediate danger. To ensure a successful transfer, the spatial resolution and the accuracy of the input dataset are much higher than the accuracy and resolution that are being simulated. Furthermore, the investigated change form is detected as significant in the input data. We, therefore, conclude the model of the dynamic scene derived from real TLS data to be an appropriate substitution for reality. Numéro de notice : A2022-437 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-79-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100746 [article]