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Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data / Yi-Chun Lin in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 6 (December 2022) . - n° 100023 Titre : Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data Type de document : Article/Communication Auteurs : Yi-Chun Lin, Auteur ; Ayman Habib, Auteur Année de publication : 2022 Article en page(s) : n° 100023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] autoroute [Termes IGN] couplage GNSS-INS [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] lidar mobile [Termes IGN] pont [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau routier [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (auteur) Emerging mobile LiDAR mapping systems exhibit great potential as an alternative for mapping urban environments. Such systems can acquire high-quality, dense point clouds that capture detailed information over an area of interest through efficient field surveys. However, automatically recognizing and semantically segmenting different components from the point clouds with efficiency and high accuracy remains a challenge. Towards this end, this study proposes a semantic segmentation framework to simultaneously classify bridge components and road infrastructure using mobile LiDAR point clouds while providing the following contributions: 1) a deep learning approach exploiting graph convolutions is adopted for point cloud semantic segmentation; 2) cross-labeling and transfer learning techniques are developed to reduce the need for manual annotation; and 3) geometric quality control strategies are proposed to refine the semantic segmentation results. The proposed framework is evaluated using data from two mobile mapping systems along an interstate highway with 27 highway bridges. With the help of the proposed cross-labeling and transfer learning strategies, the deep learning model achieves an overall accuracy of 84% using limited training data. Moreover, the effectiveness of the proposed framework is verified through test covering approximately 42 miles along the interstate highway, where substantial improvement after quality control can be observed. Numéro de notice : A2022-814 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.ophoto.2022.100023 Date de publication en ligne : 24/10/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101975 [article]

Modelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information / Shaohui Zhang in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 114 (November 2022) . - n° 103072 Titre : Modelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information Type de document : Article/Communication Auteurs : Shaohui Zhang, Auteur ; Cédric Vega , Auteur ; Christine Deleuze, Auteur ; Sylvie Durrieu, Auteur ; Pierre Barbillon, Auteur ; Olivier Bouriaud , Auteur ; Jean-Pierre Renaud, Auteur Année de publication : 2022 Article en page(s) : n° 103072 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données lidar [Termes IGN] empreinte [Termes IGN] gestion forestière [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier local [Termes IGN] inventaire forestier national (données France) [Termes IGN] modèle numérique de terrain [Termes IGN] modélisation de la forêt [Termes IGN] placette d'échantillonnage [Termes IGN] Sologne (France) [Termes IGN] variogramme [Termes IGN] volume en bois Résumé : (auteur) The French National Forest Inventory provides detailed forest information up to large national and regional scales. Forest inventory for small areas of interest within a large population is equally important for decision making, such as for local forest planning and management purposes. However, sampling these small areas with sufficient ground plots is often not cost efficient. In response, small area estimation has gained increasing popularity in forest inventory. It consists of a set of techniques that enables predictions of forest attributes of subpopulation with the help of auxiliary information that compensates for the small field samples. Common sources of auxiliary information usually come from remote sensing technology, such as airborne laser scanning and satellite imagery. The newly launched NASA’s Global Ecosystem Dynamics Investigation (GEDI), a full waveform Lidar instrument, provides an unprecedented opportunity of collecting large-scale and dense forest sample plots given its sampling frequency and spatial coverage. However, the geolocation uncertainty associated with GEDI footprints create important challenges for their use for small area estimations. In this study, we designed a process that provides NFI measurements at plot level with GEDI auxiliary information from nearby footprints. We demonstrated that GEDI RH98 is equivalent to NFI dominant height at plot level. We stressed the importance of pairing NFI plots with nearby GEDI footprints, based on not only the distance in between but also their similarities, i.e., forest heights and forest types. Subsequently, these NFI-GEDI pairs were used for small area estimations following a two-phase sampling scheme. We showcased that, with an adequate sample size, small area estimation with GEDI auxiliary data can improve the accuracy of forest volume estimates. Numéro de notice : A2022-786 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.103072 Date de publication en ligne : 22/10/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103072 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101890 [article]

Benchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest / Daniel Kükenbrink in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 113 (September 2022) . - n° 102999 Titre : Benchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest Type de document : Article/Communication Auteurs : Daniel Kükenbrink, Auteur ; Mauro Marty, Auteur ; Ruedi Bösch, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102999 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] caméra à bas coût [Termes IGN] cartographie et localisation simultanées [Termes IGN] détection d'arbres [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt tempérée [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lidar mobile [Termes IGN] lidar topographique [Termes IGN] photogrammétrie terrestre [Termes IGN] semis de points [Termes IGN] série temporelle [Termes IGN] structure-from-motion [Termes IGN] Zurich (Suisse) Résumé : (auteur) National forest inventories (NFI) are important for the assessment of the state and development of forests. Traditional NFIs often rely on statistical sampling approaches as well as expert assessment which may suffer from observer bias and may lack robustness for time series analysis. Over the course of the last decade, close-range remote sensing techniques such as terrestrial and mobile laser scanning became ever more established for the assessment of three-dimensional (3D) forest structure. With the ongoing trend to make the systems smaller, easier to use and more efficient, the pathway is being opened for an operational inclusion of such devices within the framework of an NFI to support the traditional field assessment. Close-range remote sensing could potentially speed up field inventory work as well as increase the area in which certain parameters are assessed. Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices. Among the many parameters evaluated in traditional NFIs, the focus of the performance evaluation of this study is set on the automatic tree detection and DBH extraction. The results showed that TLS delivers the highest tree detection rate (TDR) of up to 94.6% under leaf-off and up to 82% under leaf-on conditions and a relative RMSE (rRMSE) for the DBH extraction between 2.5 and 9%, depending on the undergrowth complexity. The tested PLS system (leaf-on) achieved a TDR of up to 80% with an rRMSE between 3.7 and 5.8%. The tested UAVLS systems showed lowest TDR of less than 77% under leaf-off and less than 37% under leaf-on conditions. The novel GoPro approach achieved a TDR of up to 53% under leaf-on conditions. The reduced TDR can be explained by the reduced area coverage due to the chosen circular acquisition path taken with the GoPro approach. The DBH extraction performance on the other hand is comparable to those of the LiDAR devices with an rRMSE between 2 and 9%. Further benchmarks are needed in order to fully assess the applicability of these systems in the framework of an NFI. Especially the robustness under varying forest conditions (seasonality) and over a broader range of forest types and canopy structure has to be evaluated. Numéro de notice : A2022-787 Affiliation des auteurs : IGN (1940-2011) Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102999 Date de publication en ligne : 05/09/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102999 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101893 [article]

Using multi-temporal tree inventory data in eucalypt forestry to benchmark global high-resolution canopy height models. A showcase in Mato Grosso, Brazil / Adrián Pascual in Ecological Informatics, vol 70 (September 2022)  

Public [article]  inEcological Informatics > vol 70 (September 2022) Titre : Using multi-temporal tree inventory data in eucalypt forestry to benchmark global high-resolution canopy height models. A showcase in Mato Grosso, Brazil Type de document : Article/Communication Auteurs : Adrián Pascual, Auteur ; Frederico Tupinambá-Simões, Auteur ; Tiago de Conto, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte forestière [Termes IGN] Eucalyptus (genre) [Termes IGN] forêt tropicale [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] hauteur des arbres [Termes IGN] incertitude des données [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Mato Grosso [Termes IGN] modèle numérique de surface de la canopée [Vedettes matières IGN] Inventaire forestier Mots-clés libres : E. urograndis  E. urophylla x E. grandis, E. urophylla and E. camaldulensis x E. grandis Résumé : (auteur) The global monitoring of forest structure worldwide is increasingly being supported by refined and enhanced satellite mission datasets. Forest canopy height is a global metric to characterise and monitor dynamics in forest ecosystems worldwide. Satellite mapping missions as NASA's Global Ecosystem Dynamics Investigation (GEDI) are creating opportunities to refine global forest canopy height models adding forest structural information to time-series satellite imagery. A recent global canopy height model presented by Lang et al., (2022) using GEDI and 10-m Sentinel-2 and the map from Potapov et al., (2020) using GEDI and Landsat are both tested in this study using multi-temporal tree-level data collected over eucalypt plantations in Brazil. Our results at plot-level showed Lang et al., (2022)’s estimates of canopy height came short compared to 2020 maximum and mean tree height records in the plots, 7.6 and 3.6 m, respectively, but adding CHM standard deviation improves the agreement of ground records for maximum tree height. Higher errors were computed for the plots in 2019 using the Potapov's 30-m CHM: 14.2 and 9.5 m, respectively. Averaged stand values were more similar between the three sources tested. We report improvement from the 30-m CHM to the 10-m, but still height saturation problems were observed when accounting for height differences in tall eucalypt trees. As more global products for forest height and biomass are becoming available to users, more validation exercises as presented in this study are needed to assess the suitability of CHM products to forestry needs, and facilitate the uptake and actionability of the next generation of global height and biomass products. We provide recommendations and insights on the use of GEDI laser data for global mapping and on the potential of commercial forestry areas to benchmark the accuracy of satellite mapping missions focusing on tree height estimation in the tropics. Numéro de notice : A2022-615 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ecoinf.2022.101748 En ligne : https://doi.org/10.1016/j.ecoinf.2022.101748 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101370 [article]

Full-waveform classification and segmentation-based signal detection of single-wavelength bathymetric LiDAR / Xue Ji in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 8 (August 2022) . - n° 4208714 Titre : Full-waveform classification and segmentation-based signal detection of single-wavelength bathymetric LiDAR Type de document : Article/Communication Auteurs : Xue Ji, Auteur ; Bisheng Yang, Auteur ; Yuan Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4208714 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme de Levenberg-Marquardt [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du signal [Termes IGN] forme d'onde pleine [Termes IGN] Hainan (Chine) [Termes IGN] lidar bathymétrique [Termes IGN] optimisation par essaim de particules Résumé : (auteur) Single-wavelength bathymetric light detection and ranging (LiDAR) (532 nm) can provide seamless meter- and submeter-scale digital elevation model (DEMs) of both the terrestrial surface and seafloor. However, mixed terrestrial and bathymetric surfaces obtained by this sensor are challenging for full-waveform (FW) signal detection. This study addresses the issues in two FW mixed surfaces: accurate classification of terrestrial and nonterrestrial waveforms from the original waveforms without auxiliary information and flexible detection of peaks based on a new FW theoretical model. A novel FW signal detection model (FWSD) for single-wavelength bathymetric LiDAR is proposed without complex feature extraction and iterative procedure through waveform classification and segmentation. The raw FWs are divided into five categories for subsequent signal detection using a convolutional neural network that merges local descriptors with contextual information. The signal detection task is then split into FW segment recognition and peak extraction using a new FW model, which integrates a leapfrog sliding window FW segmentation, an improved extreme learning machine (ELM) algorithm for FW segment recognition, and a flexible signal detection framework. To search for the optimal initial parameters for ELM, a self-annealing particle swarm optimization (SAPSO) algorithm is introduced, and the output weight is adjusted by online sequence to improve its generalization. When combined with the Richardson–Lucy deconvolution (RLD) algorithm, FWSD can be adapted to deal with shallow water waveforms. Finally, a test demonstration with an airborne dataset shows that FWSD has higher detection efficiency and higher accuracy than Levenberg–Marquardt algorithm optimized generalized Gaussian model (LM-GGM) and RLD algorithm. Numéro de notice : A2022-661 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3198168 Date de publication en ligne : 11/08/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3198168 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101517 [article]

How can Sentinel-2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters? / Katja Kuhwald in Remote sensing in ecology and conservation, vol 8 n° 3 (June 2022)  

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Individual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads / Raul de Paula Pires in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)  

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Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data / Cheng-Chun Lee in Computers, Environment and Urban Systems, vol 93 (April 2022)  

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PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data / Qi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)  

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Development of a single-wavelength airborne bathymetric LiDAR: System design and data processing / Kai Guo in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)  

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Traffic sign three-dimensional reconstruction based on point clouds and panoramic images / Minye Wang in Photogrammetric record, vol 37 n° 177 (March 2022)  

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3D stem modelling in tropical forest: towards improved biomass and biomass change estimates / Sébastien Bauwens (2022)  

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Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)  

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Application of a hand-held LiDAR scanner for the urban cadastral detail survey in digitized cadastral area of Taiwan urban city / Shih-Hong Chio in Remote sensing, vol 13 n° 24 (December-2 2021)  

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Atelier LiDAR mobile & aéroporté / Pierre Assali in XYZ, n° 169 (décembre 2021)

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