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Termes IGN > géomatique > données localisées > données localisées numériques > données laser > données lidar
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Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
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Titre : Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests Type de document : Article/Communication Auteurs : Sruthi M. Krishna Moorthy, Auteur ; Kim Calders, Auteur ; Matheus B. Vicari, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3057 - 3070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] atmosphère terrestre
[Termes IGN] canopée
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] faisceau laser
[Termes IGN] feuille (végétation)
[Termes IGN] foresterie
[Termes IGN] forêt de feuillus
[Termes IGN] forêt tropicale
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] précision de la classification
[Termes IGN] Python (langage de programmation)
[Termes IGN] semis de points
[Termes IGN] transfert radiatifRésumé : (auteur) Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package. Numéro de notice : A2020-232 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947198 Date de publication en ligne : 31/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94970
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3057 - 3070[article]Mapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics / E. Banzhaf in Geocarto international, vol 35 n° 6 ([01/05/2020])
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Titre : Mapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics Type de document : Article/Communication Auteurs : E. Banzhaf, Auteur ; H. Kollai, Auteur ; A. Kindler, Auteur Année de publication : 2020 Article en page(s) : pp 623 - 640 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse d'image orientée objet
[Termes IGN] base de données orientée objet
[Termes IGN] base de données urbaines
[Termes IGN] bati
[Termes IGN] bien-être collectif
[Termes IGN] cartographie urbaine
[Termes IGN] développement durable
[Termes IGN] données lidar
[Termes IGN] écosystème urbain
[Termes IGN] gestion urbaine
[Termes IGN] orthophotographie
[Termes IGN] population urbaine
[Termes IGN] santé
[Termes IGN] télédétectionRésumé : (auteur) Mapping urban structures is a vital prerequisite for urban planners to enhance their database for a liveable city dedicated to sustainable development. Therefore, it is significant to measure urban grey and green structures at the scale of local districts to understand the urban structure and residential needs for urban ecosystem services. For a detailed analysis we exploit digital orthophotos (DOP), LiDAR data, and vital statistics. We use remote sensing techniques to create an Object-based Image Analysis (OBIA) that differentiates grey and green structures with high precision and at refined scale. This spatial information is linked with allocated population and health-related indicators to identify built-up types with highest population densities and local districts with deficits in the provision of different green structures. Our results show the share of built-up structures and the contribution of green structures to urban ecosystem services, human health and well-being at local district level. Numéro de notice : A2020-202 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1524514 Date de publication en ligne : 23/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1524514 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94877
in Geocarto international > vol 35 n° 6 [01/05/2020] . - pp 623 - 640[article]Method for extraction of airborne LiDAR point cloud buildings based on segmentation / Maohua Liu in Plos one, vol 15 n° 5 (May 2020)
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Titre : Method for extraction of airborne LiDAR point cloud buildings based on segmentation Type de document : Article/Communication Auteurs : Maohua Liu, Auteur ; Yue Shao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 0232778 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] bati
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de points
[Termes IGN] segmentationRésumé : (auteur) The LiDAR technology is a means of urban 3D modeling in recent years, and the extraction of buildings is a key step in urban 3D modeling. In view of the complexity of most airborne LiDAR building point cloud extraction algorithms that need to combine multiple feature parameters, this study proposes a building point cloud extraction method based on the combination of the Point Cloud Library (PCL) region growth segmentation and the histogram. The filtered LiDAR point cloud is segmented by using the PCL region growth method, and then the local normal vector and direction cosine are calculated for each cluster after segmentation. Finally, the histogram is generated to effectively separate the building point cloud from the non-building.Two sets of airborne LiDAR data in the south and west parts of Tokushima, Japan, are used to test the feasibility of the proposed method. The results are compared with those of the commercial software TerraSolid and the K-means algorithm. Results show that the proposed extraction algorithm has lower type I and II errors and better extraction effect than that of the TerraSolid and the K-means algorithm. Numéro de notice : A2020-832 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1371/journal.pone.0232778 Date de publication en ligne : 29/05/2020 En ligne : https://doi.org/10.1371/journal.pone.0232778 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97666
in Plos one > vol 15 n° 5 (May 2020) . - n° 0232778[article]Outlier detection and robust plane fitting for building roof extraction from LiDAR data / Emon Kumar Dey in International Journal of Remote Sensing IJRS, vol 41 n° 16 (01-10 May 2020)
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Titre : Outlier detection and robust plane fitting for building roof extraction from LiDAR data Type de document : Article/Communication Auteurs : Emon Kumar Dey, Auteur ; Mohammad Awrangjeb, Auteur ; Bela Stantic, Auteur Année de publication : 2020 Article en page(s) : pp 6325 - 6354 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] semis de points
[Termes IGN] toit
[Termes IGN] valeur aberranteRésumé : (auteur) Individual roof plane extraction from Light Detection and Ranging (LiDAR) point-cloud data is a complex and difficult task because of unknown semantic characteristics and inharmonious behaviour of input data. Most of the existing state-of-the-art methods fail to detect small true roof planes with exact boundaries due to outliers, occlusions, complex building structures, and other inconsistent nature of LiDAR data. In this paper, we have presented an improved building detection and roof plane extraction method, which is less sensitive to the outliers and unlikely to generate spurious planes. For this, a robust outlier detection algorithm has been proposed in this paper along with a robust plane-fitting algorithm based on M-estimator SAmple Consensus (MSAC) for detecting individual roof planes. Using two benchmark datasets (Australian and International Society for Photogrammetry and Remote Sensing benchmark) with different numbers of buildings and sizes, trees and point densities, we have evaluated the proposed method. Experimental results show that the method removes outliers and vegetation almost accurately and offers a high success rate in terms of completeness and correctness (between 80% and 100% per-object) for both roof plane extraction and building detection. In most of the cases, the proposed method shows above 90% correctness. Numéro de notice : A2020-454 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1737339 Date de publication en ligne : 09/06/2020 En ligne : https://doi.org/10.1080/01431161.2020.1737339 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95543
in International Journal of Remote Sensing IJRS > vol 41 n° 16 (01-10 May 2020) . - pp 6325 - 6354[article]La télédétection aéroportée pour la gestion des territoires forestiers de montagne / Jean-Matthieu Monnet in Sciences, eaux & territoires, n° 33 (avril 2020)
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Titre : La télédétection aéroportée pour la gestion des territoires forestiers de montagne Type de document : Article/Communication Auteurs : Jean-Matthieu Monnet, Auteur ; Pierre Paccard, Auteur ; Catherine Riond, Auteur Année de publication : 2020 Article en page(s) : pp 64 - 69 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] abattage (sylviculture)
[Termes IGN] acquisition de données
[Termes IGN] diffusion de données
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt alpestre
[Termes IGN] France (végétation)
[Termes IGN] gestion forestière
[Termes IGN] télémétrie laser aéroportéRésumé : (Auteur) Le Programme national de la forêt et du bois 2016-2026 affiche comme objectif « d’augmenter les prélèvements de bois en France tout en assurant le renouvellement de la forêt ». Les forêts de montagne qui représentent environ un quart de la surface forestière pourraient contribuer de manière significative à cet objectif. Les contraintes d’accès et de topographie rendent cependant difficile la gestion de ces forêts. En s’appuyant sur la technologie Lidar aéroporté, il est désormais possible de cartographier à haute résolution, sur des territoires de la taille d’un parc naturel régional, les caractéristiques forestières (ressource, accessibilité) intéressant les gestionnaires. La généralisation de l’outil pose cependant des questions de coût d’acquisition des données et de droit de leur diffusion auprès des acteurs de la filière. Numéro de notice : A2020-182 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.14758/SET-REVUE.2020.3.12 Date de publication en ligne : 10/04/2020 En ligne : https://doi.org/10.14758/SET-REVUE.2020.3.12 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94956
in Sciences, eaux & territoires > n° 33 (avril 2020) . - pp 64 - 69[article]Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification / Congcong Wen in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)
PermalinkMonitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys / Ashutosh Tiwari in Geocarto international, vol 35 n° 5 ([01/04/2020])
PermalinkMultitemporal analysis of gully erosion in olive groves by means of digital elevation models obtained with aerial photogrammetric and LIDAR data / Tomás Fernández in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)
PermalinkUsing multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds / Zhou Guo in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)
PermalinkHow far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study / Enrico Borgogno Mondino in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)
Permalink3D laser scanning of the natural caves: Example of Škocjanske jame / Richard Walters in Geodetski vestnik, Vol 64 n° 1 (March - May 2020)
PermalinkAn improved RANSAC algorithm for extracting roof planes from airborne lidar data / Sibel Canaz Sevgen in Photogrammetric record, vol 35 n° 169 (March 2020)
PermalinkClassification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network / Yueguan Yan in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)
PermalinkA discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data / Qingwang Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)
PermalinkGeneration of digital terrain model for forest areas using a new particle swarm optimization on LiDAR data / Behnaz Bigdeli in Survey review, vol 52 n° 371 (March 2020)
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