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scientifique

Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review / Sahar S. Matin in Geocarto international, Vol 37 n° 21 ([01/10/2022])  

Public [article]  inGeocarto international > Vol 37 n° 21 [01/10/2022] . - pp 6186 - 6212 Titre : Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review Type de document : Article/Communication Auteurs : Sahar S. Matin, Auteur ; Biswajeet Pradhan, Auteur Année de publication : 2022 Article en page(s) : pp 6186 - 6212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] cartographie automatique [Termes IGN] cartographie thématique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déformation d'édifice [Termes IGN] détection de changement [Termes IGN] dommage matériel [Termes IGN] données lidar [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] secours d'urgence [Termes IGN] séisme Résumé : (auteur) Assessing the extent and level of building damages is crucial to support post-earthquake rescue and relief activities. There is a large body of literature proposing novel frameworks for automating earthquake-induced building damage mapping using high-resolution remote sensing images. Yet, its deployment in real-world scenarios is largely limited to the manual interpretation of images. Although manual interpretation is costly and labor-intensive, it is preferred over automatic and semi-automatic building damage mapping frameworks such as machine learning and deep learning because of its reliability. Therefore, this review paper explores various automatic and semi-automatic building damage mapping techniques with a quest to understand the pros and cons of different methodologies to narrow the gap between research and practice. Further, the research gaps and opportunities are identified for the future development of real-world scenarios earthquake-induced building damage mapping. This review can serve as a guideline for researchers, decision-makers, and practitioners in the emergency management service domain. Numéro de notice : A2022-719 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1933213 Date de publication en ligne : 07/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1933213 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101651 [article]

Multisource forest inventories: A model-based approach using k-NN to reconcile forest attributes statistics and map products / Ankit Sagar in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 192 (October 2022) . - pp 175 - 188 Titre : Multisource forest inventories: A model-based approach using k-NN to reconcile forest attributes statistics and map products Type de document : Article/Communication Auteurs : Ankit Sagar , Auteur ; Cédric Vega , Auteur ; Olivier Bouriaud , Auteur ; Christian Piedallu, Auteur ; Jean-Pierre Renaud, Auteur Année de publication : 2022 Projets : LUE / Université de Lorraine, ARBRE / AgroParisTech, DEEPSURF / Pironon, Jacques Article en page(s) : pp 175 - 188 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification barycentrique [Termes IGN] données allométriques [Termes IGN] données lidar [Termes IGN] image Landsat-8 [Termes IGN] inventaire forestier national (données France) [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Forest map products are widely used and have taken benefit from progresses in the multisource forest inventory approaches, which are meant to improve the precision of forest inventory estimates at high spatial resolution. However, estimating errors of pixel-wise predictions remains difficult, and reconciling statistical outcomes with map products is still an open and important question. We address this problem using an original approach relying on a model-based inference framework and k-nearest neighbours (k-NN) models to produce pixel-wise estimations and related quality assessment. Our approach takes advantage of the resampling properties of a model-based estimator and combines it with geometrical convex-hull models to measure respectively the precision and accuracy of pixel predictions. A measure of pixel reliability was obtained by combining precision and accuracy. The study was carried out over a 7,694 km2 area dominated by structurally complex broadleaved forests in centre of France. The targeted forest attributes were growing stock volume, basal area and growing stock volume increment. A total of 819 national forest inventory plots were combined with auxiliary data extracted from a forest map, Landsat 8 images, and 3D point clouds from both airborne laser scanning and digital aerial photogrammetry. k-NN models were built independently for both 3D data sources. Both selected models included 5 auxiliary variables, and were generated using 5 neighbours, and most similar neighbours distance measure. The models showed relative root mean square error ranging from 35.7% (basal area, digital aerial photogrammetry) in calibration to 63.4% (growing stock volume increment, airborne laser scanning) in the validation set. At pixel level, we found that a minimum of 86.4% of the predictions were of high precision as their bootstrapped coefficient of variation fall below calibration’s relative root mean square error. The amount of extrapolation varied from 4.3% (digital aerial photogrammetry) to 6.3% (airborne laser scanning). A relationship was found between extrapolation and k-NN distance, opening new opportunities to correct extrapolation errors. At the population level, airborne laser scanning and digital aerial photogrammetry performed similarly, offering the possibility to use digital aerial photogrammetry for monitoring purposes. The proposed method provided consistent estimates of forest attributes and maps, and also provided spatially explicit information about pixel predictions in terms of precision, accuracy and reliability. The method therefore produced high resolution outputs, significant for either decision making or forest management purposes. Numéro de notice : A2022-629 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101495 [article]

Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics / Jakob Wernicke in Remote sensing of environment, vol 279 (15 September 2022)  

Public [article]  inRemote sensing of environment > vol 279 (15 September 2022) . - n° 113134 Titre : Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics Type de document : Article/Communication Auteurs : Jakob Wernicke, Auteur ; Christian Torsten Seltmann, Auteur ; Ralf Wenzel, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113134 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Allemagne [Termes IGN] analyse comparative [Termes IGN] canopée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fusion d'images [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] inventaire forestier étranger (données) [Termes IGN] semis de points [Termes IGN] stratification Résumé : (auteur) Knowledge about the forest canopy stratification is of essential importance for forest management and planning. Collecting structural information (e.g. natural regeneration) still depends on cost and labour intensive forest inventories with a coarse spatio-temporal resolution. Remote sensing partly overcomes these limitations and particularly active sensors of type light detection and ranging (LiDAR) have proven their great potential of separating forest strata. The applicability of LiDAR metrics for the differentiation of the spruce dominated forest strata in Central Germany has not been tested yet. Additionally, studying the potential of Sentinel-2 metrics for the classification of forest strata is lacking too. In this study, we investigated the capabilities of six different classification approaches for the differentiation of five forest strata that are typical for the study region. Reference data were derived from forest inventory measurements surveyed on a dense 200 × 200 m grid. The six classification approaches were trained with fused and un-fused LiDAR and Sentinel-2 inferred metrics. The classification results were compared using the overall mean accuracy, sensitivity and specificity via receivers operating characteristics of multi-class problems. We were interested in the classification abilities of Sentinel-2 metrics due to the obvious advantages of Sentinel-2 based metrics (free of charge, high spatio-temporal coverage). We assumed that the canopy structure determines the reflection on stand level and thus might facilitate the classification of different canopy strata. Beforehand, it was important to examine the influence of distinctly imbalanced and collinear reference data on the classification results. We found that the Random Forest classifier most accurately separated the five forest strata with a mean overall accuracy of 83.3% (Kappa = 76.2%). These values were achieved from balanced training data and the classification capability was confirmed by classification results from an independent test data set. Fused predictors of active (LiDAR) and passive (Sentinel-2) remote sensing revealed no substantial improvement in the classification accuracy due to the dominant role of LiDAR metrics. Herein, we identified that especially the height variability, top height, portion of LiDAR-returns between 2 m and 10 m and the standard deviation of the return number between the 25th and 50th height percentile, predominately contributed to the classification accuracy. Classification results purely based on Sentinel-2 metrics revealed a rather small overall mean accuracy of 54.7%. The metrics (e.g. median, variance, entropy) were derived from Sentinel-2 indices, covering the visible and near to short infrared spectrum. Variable importance computations unraveled a detectable but minor contribution of MSI, TCG, NDVI to the classification result. Finally, our data driven observations illustrated serious drawbacks associated to data imbalance, collinearity and autocorrelation and presented practical guidance to cope with these issues. Numéro de notice : A2022-510 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113134 Date de publication en ligne : 28/06/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101047 [article]

Exploring tree growth allometry using two-date terrestrial laser scanning / Tuomas Yrttimaa in Forest ecology and management, vol 518 (15 August 2022)  

Public [article]  inForest ecology and management > vol 518 (15 August 2022) . - n° 120303 Titre : Exploring tree growth allometry using two-date terrestrial laser scanning Type de document : Article/Communication Auteurs : Tuomas Yrttimaa, Auteur ; Ville Luoma, Auteur ; Ninni Saarinen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120303 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] allométrie [Termes IGN] croissance des arbres [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt boréale [Termes IGN] houppier [Termes IGN] semis de points [Termes IGN] série temporelle [Termes IGN] surface terrière [Termes IGN] volume en bois Résumé : (auteur) Tree growth is a physio-ecological phenomena of high interest among researchers across disciplines. Observing changes in tree characteristics has conventionally required either repeated measurements of the characteristics of living trees, retrospective measurements of destructively sampled trees, or modelling. The use of close-range sensing techniques such as terrestrial laser scanning (TLS) has enabled non-destructive approaches to reconstruct the three-dimensional (3D) structure of trees and tree communities in space and time. This study aims at improving the understanding of tree allometry in general and interactions between tree growth and its neighbourhood in particular by using two-date point clouds. We investigated how variation in the increments in basal area at the breast height (Δg1.3), basal area at height corresponding to 60% of tree height (Δg06h), and volume of the stem section below 50% of tree height (Δv05h) can be explained with TLS point cloud-based attributes characterizing the spatiotemporal structure of a tree crown and crown neighbourhood, entailing the competitive status of a tree. The analyses were based on 218 trees on 16 sample plots whose 3D characteristics were obtained at the beginning (2014, T1) and at the end of the monitoring period (2019, T2) from multi-scan TLS point clouds using automatic point cloud processing methods. The results of this study showed that, within certain tree communities, strong relationships (|r| > 0.8) were observed between increments in the stem dimensions and the attributes characterizing crown structure and competition. Most often, attributes characterizing the competitive status of a tree, and the crown structure at T1, were the most important attributes to explain variation in the increments of stem dimensions. Linear mixed-effect modelling showed that single attributes could explain up to 35–60% of the observed variation in Δg1.3, Δg06h and Δv05h, depending on the tree species. This tree-level evidence of the allometric relationship between stem growth and crown dynamics can further be used to justify landscape-level analyses based on airborne remote sensing technologies to monitor stem growth through the structure and development of crown structure. This study contributes to the existing knowledge by showing that laser-based close-range sensing is a feasible technology to provide 3D characterization of stem and crown structure, enabling one to quantify structural changes and the competitive status of trees for improved understanding of the underlying growth processes. Numéro de notice : A2022-484 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2022.120303 Date de publication en ligne : 22/05/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120303 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100899 [article]

3D semantic scene completion: A survey / Luis Roldão in International journal of computer vision, vol 130 n° 8 (August 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 8 (August 2022) . - pp 1978 - 2005 Titre : 3D semantic scene completion: A survey Type de document : Article/Communication Auteurs : Luis Roldão, Auteur ; Raoul de Charette, Auteur ; Anne Verroust-Blondet, Auteur Année de publication : 2022 Article en page(s) : pp 1978 - 2005 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] effet de profondeur cinétique [Termes IGN] image RVB [Termes IGN] reconstruction d'image [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] voxel Résumé : (auteur) Semantic scene completion (SSC) aims to jointly estimate the complete geometry and semantics of a scene, assuming partial sparse input. In the last years following the multiplication of large-scale 3D datasets, SSC has gained significant momentum in the research community because it holds unresolved challenges. Specifically, SSC lies in the ambiguous completion of large unobserved areas and the weak supervision signal of the ground truth. This led to a substantially increasing number of papers on the matter. This survey aims to identify, compare and analyze the techniques providing a critical analysis of the SSC literature on both methods and datasets. Throughout the paper, we provide an in-depth analysis of the existing works covering all choices made by the authors while highlighting the remaining avenues of research. SSC performance of the SoA on the most popular datasets is also evaluated and analyzed. Numéro de notice : A2022-593 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-021-01504-5 Date de publication en ligne : 06/06/2022 En ligne : http://dx.doi.org/10.1007/s11263-021-01504-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101296 [article]

Automated extraction and validation of Stone Pine (Pinus pinea L.) trees from UAV-based digital surface models / Asli Ozdarici-Ok in Geo-spatial Information Science, vol 25 n° inconnu ([01/08/2022])  

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Change detection in street environments based on mobile laser scanning: A fuzzy spatial reasoning approach / Joachim Gehrung in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)  

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Characterizing the calibration domain of remote sensing models using convex hulls / Jean-Pierre Renaud in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)  

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Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM / Jiehua Cai in Engineering Geology, vol 305 (August 2022)  

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Filtering airborne LIDAR data by using fully convolutional networks / Abdullah Varlik in Survey review, vol inconnu ([01/08/2022])  

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Improvement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier / Mahmoud Mohamed in Geocarto international, vol inconnu n° inconnu ([01/08/2022])  

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Predicting vegetation stratum occupancy from airborne LiDAR data with deep learning / Ekaterina Kalinicheva in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)    

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Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners / Artu Ellmann in Survey review, vol 54 n° 385 (July 2022)  

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Detection of GNSS no-line of sight signals using LiDAR sensors for intelligent transportation systems / Tarek Hassan in Survey review, vol 54 n° 385 (July 2022)  

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Lidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks / Aurélien Brun in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

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