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Multi-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR / Zhenyang Hui in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)
[article]
Titre : Multi-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR Type de document : Article/Communication Auteurs : Zhenyang Hui, Auteur ; Penggen Cheng, Auteur ; Bisheng Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103028 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de groupement
[Termes IGN] classification par nuées dynamiques
[Termes IGN] détection automatique
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données matricielles
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] optimisation (mathématiques)
[Termes IGN] Pinophyta
[Termes IGN] segmentation d'image
[Termes IGN] segmentation multi-échelle
[Termes IGN] semis de pointsRésumé : (auteur) To obtain satisfying results of individual tree detection from LiDAR points, parameters using traditional methods usually need to be adjusted by trials and errors. When encountering complex forest environments, the detection accuracy cannot be satisfied. To resolve this, a multi-level self-adaptive individual tree detection method was presented in this paper. The proposed method can be seen as a hybrid model, which combined the strength of both raster-based and point-based methods. Raster-based strategy was first used for achieving initial trees detection results, while the point-based strategy was adopted for optimizing the clustered trees. In the proposed method, crown width scales were estimated automatically. Meanwhile, multi-scales segmented results were fused together to take advantage of segmented results of both larger and small scales. Six different coniferous forests were adopted for testing. Experimental result shows that this study achieved the lowest omission and commission errors comparing with other three classical approaches. Meanwhile, the average F1 score in this paper is 0.84, which is much highest out of other methods. Numéro de notice : A2022-784 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103028 Date de publication en ligne : 24/09/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103028 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101887
in International journal of applied Earth observation and geoinformation > vol 114 (November 2022) . - n° 103028[article]Point2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds / Li Li in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)
[article]
Titre : Point2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Li Li, Auteur ; Nan Song, Auteur ; Fei Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 17 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modélisation 3D
[Termes IGN] Perceptron multicouche
[Termes IGN] primitive géométrique
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de points
[Termes IGN] toitRésumé : (auteur) Three-dimensional (3D) building roof reconstruction from airborne LiDAR point clouds is an important task in photogrammetry and computer vision. To automatically reconstruct the 3D building models at Level of Detail 2 (LoD-2) from airborne LiDAR point clouds, the data-driven approaches usually need to be performed in two steps: geometric primitive extraction and roof structure inference. Obviously, the traditional approaches are not end-to-end, the accumulated errors in different stages cannot be avoided and the final 3D roof models may not be optimal. In addition, the results of 3D roof models largely depend on the accuracy of geometric primitives (planes, lines, etc.). To solve these problems, we present a deep learning-based approach to directly reconstruct building roofs from airborne LiDAR point clouds, named Point2Roof. In our method, we start by extracting the deep features for each input point using PointNet++. Then, we identify a set of candidate corner points from the input point clouds using the extracted deep features. In addition, we also regress the offset for each candidate corner point to refine their locations. After that, these candidates are clustered into a set of initial vertices, and we further refine their locations to obtain the final accurate vertices. Finally, we propose a Paired Point Attention (PPA) module to predict the true model edges from an exhaustive set of candidate edges between the vertices. Unlike traditional roof modeling approaches, the proposed Point2Roof is end-to-end. However, due to the lack of a building reconstruction dataset, we construct a large-scale synthetic dataset to verify the effectiveness and robustness of the proposed Point2Roof. The experimental results conducted on the synthetic benchmark demonstrate that the proposed Point2Roof significantly outperforms the traditional roof modeling approaches. The experiments also show that the network trained on the synthetic dataset can be applied to the real point clouds after fine-tuning the trained model on a small real dataset. The large-scale synthetic dataset, the small real dataset and the source code of our approach are publicly available in https://github.com/Li-Li-Whu/Point2Roof. Numéro de notice : A2022-745 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.027 Date de publication en ligne : 10/09/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101728
in ISPRS Journal of photogrammetry and remote sensing > vol 193 (November 2022) . - pp 17 - 28[article]Terrain representation using orientation / Gene Trantham in Cartography and Geographic Information Science, vol 49 n° 6 (November 2022)
[article]
Titre : Terrain representation using orientation Type de document : Article/Communication Auteurs : Gene Trantham, Auteur ; Patrick Kennelly, Auteur Année de publication : 2022 Article en page(s) : pp 479 - 491 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique
[Termes IGN] données matricielles
[Termes IGN] estompage
[Termes IGN] modèle numérique de surface
[Termes IGN] ombre
[Termes IGN] orientation
[Termes IGN] représentation du relief
[Termes IGN] teinte hypsométriqueRésumé : (auteur) A terrain data model using orientation rather than elevation permits more efficient analysis and stores its data in a multi-band raster. Representation techniques from the computer graphics industry are readily adopted with this data model. A common data model for terrain surfaces–the raster digital elevation model (DEM)–carries with it limitations by emphasizing height. Derived products such as relief shading require additional processing to determine orientation, even though they are used more frequently than those relying on elevation (e.g. hypsometric tinting). We show some of the benefits of encoding and analyzing terrain based on surface orientation, an approach that uses normal vectors stored as multi-band raster, the data storage convention in the computer graphics industry. A change in the data model and the conceptualization of the surface’s defining characteristics allows relief shading methods to run faster than conventional tools. Processing efficiencies are especially useful for more advanced shading models that may employ hundreds of relief shading calculations. In addition, an orientation-focused approach to terrain permits cartographic techniques to parallel common computer graphics methods. This project explores one such method, normal-mapping, an effect that adds texture to conventional relief shading by perturbing surface normal vectors. Numéro de notice : A2022-844 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2022.2035256 Date de publication en ligne : 03/03/2022 En ligne : https://doi.org/10.1080/15230406.2022.2035256 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102072
in Cartography and Geographic Information Science > vol 49 n° 6 (November 2022) . - pp 479 - 491[article]Silvicultural experiment assessment using lidar data collected from an unmanned aerial vehicle / Diogo N. Cosenza in Forest ecology and management, vol 522 (October-15 2022)
[article]
Titre : Silvicultural experiment assessment using lidar data collected from an unmanned aerial vehicle Type de document : Article/Communication Auteurs : Diogo N. Cosenza, Auteur ; Jason Vogel, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120489 Langues : Anglais (eng) Descripteur : [Termes IGN] croissance végétale
[Termes IGN] données allométriques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] modélisation de la forêt
[Termes IGN] Pinus taeda
[Termes IGN] plantation forestière
[Termes IGN] sylviculture
[Vedettes matières IGN] ForesterieRésumé : (auteur) Collecting field data in silvicultural experiments can be challenging and time-consuming. Alternatively, unmanned aerial vehicles using laser scanners (UAV-lidar) can be used for cost-effective data collection in forest stands. This work aims to assess the capability of UAV-lidar to estimate biophysical forest attributes in silvicultural experiments. The showcase experiment refers to the IMPAC II (Intensive Management Practices Assessment Center II), a long-term project of 24 plots aiming to assess the effects of fertilization and weed control on forest growth and nutrient cycling in past and ongoing silvicultural treatments in a second rotation of loblolly pine (Pinus taeda L.) plantation at age 12 years. Treatment performances were assessed based on four biometric attributes related to forest productivity: Growing stock biomass (Mg ha−1), stem volume (m3 ha−1), dominant height (m), and leaf area index (LAI, m2 m−2). We used the area-based approach (ABA) and multiple linear models to characterize these forest attributes in the different silvicultural treatments and use their predictions to run the experiment analysis. Two groups of ALS-derived metrics were tested in the modeling, traditional metrics and a novel group of metrics based on plant area density (PAD) distribution. Models using PAD-based metrics increased the correlation between observed and predicted values (R2) from 0.27–0.40 to 0.50–0.85 when compared to the same models using traditional metrics, while the relative root mean square errors (RMSE%) of the predictions were reduced from 6–18% to 4–12%. Experiment analysis using UAV-lidar data and PAD-based model predictors led to the same results as those using field observations: i) fertilization was the most effective treatment for enhancing stand attributes, especially in terms of biomass, stem volume, and LAI; ii) weed control alone provided marginal improvements in the stands; iii) actively retreating stands in both first and second rotation led to increased growth when compared to the carryover effects. UAV-lidar using PAD-based metrics was effective in characterizing enhanced silvicultural treatments and might benefit studies involving understory assessment. Numéro de notice : A2022-314 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2022.120489 En ligne : https://doi.org/10.1016/j.foreco.2022.120489 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102250
in Forest ecology and management > vol 522 (October-15 2022) . - n° 120489[article]Location-enabled digital twins – understanding the role of NMCAs in a European context / Claire Ellul in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol X-4/W2 (October 2022)
[article]
Titre : Location-enabled digital twins – understanding the role of NMCAs in a European context Type de document : Article/Communication Auteurs : Claire Ellul, Auteur ; Jantien E. Stoter, Auteur ; Bénédicte Bucher , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Conférence : 3D GeoInfo 2022, ISPRS 17th 3D GeoInfo Conference 19/10/2022 21/10/2022 Sydney Australie OA ISPRS Annals Article en page(s) : n° 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] données localisées numériques
[Termes IGN] interopérabilité
[Termes IGN] jumeau numérique
[Termes IGN] organisme cartographique nationalRésumé : (auteur) Digital Twins are realistic digital representation of physical objects and are differentiated from traditional models by the live connection between the digital and the physical worlds, often enabled by sensors. They provide insights into the physical world for decision makers, for example via simulation, and can be used to directly alter the physical world without manual intervention. While they have their origins in manufacturing, they are increasingly being used within the built environment, by both public and private sectors. Increasingly city-wide and National Digital Twins are also being considered, to underpin local, municipal and central government decision making. For these emerging Digital Twins, location data such as that provided by National Mapping and Cadastral Agencies (NMCAs) has the potential to underpin Digital Twin modelling. It thus becomes important for NMCAs to better understand Digital Twins in order to determine whether current data offerings can meet this new demand and how best to support the various activities. As a first stage investigation under the auspices of EuroSDR, this paper explores challenges and opportunities for NMCAs and others working in the location sector, presenting the result of an international survey and workshop on these topics. We conclude that there is significant overlap with existing challenges within the geospatial community and those required to better support Digital Twins - e.g. interoperability and data management and governance. Additionally, the opportunity for a broader understanding and uptake of location data offered by Digital Twins should not be ignored. Numéro de notice : A2022-579 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-X-4-W2-2022-53-2022 En ligne : https://doi.org/10.5194/isprs-annals-X-4-W2-2022-53-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102844
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol X-4/W2 (October 2022) . - n° 53[article]Raster-based method for building selection in the multi-scale representation of two-dimensional maps / Yilang Shen in Geocarto international, vol 37 n° 22 ([10/10/2022])PermalinkChallenges 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])PermalinkCorrecting laser scanning intensity recorded in a cave environment for high-resolution lithological mapping: A case study of the Gouffre Georges, France / Michaela Nováková in Remote sensing of environment, vol 280 (October 2022)PermalinkDetecting overmature forests with airborne laser scanning (ALS) / Marc Fuhr in Remote sensing in ecology and conservation, vol 8 n° 5 (October 2022)PermalinkMultisource 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)PermalinkNovel algorithm based on geometric characteristics for tree branch skeleton extraction from LiDAR point cloud / Jie Yang in Forests, vol 13 n° 10 (October 2022)Permalink3D LiDAR aided GNSS/INS integration fault detection, localization and integrity assessment in urban canyons / Zhipeng Wang in Remote sensing, vol 14 n° 18 (September-2 2022)PermalinkForest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics / Jakob Wernicke in Remote sensing of environment, vol 279 (September-15 2022)PermalinkBenchmarking 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)PermalinkEstimating carbon stocks and biomass expansion factors of urban greening trees using terrestrial laser scanning / Linlin Wu in Forests, vol 13 n° 9 (september 2022)Permalink