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Enhanced trajectory estimation of mobile laser scanners using aerial images / Zille Hussnain in ISPRS Journal of photogrammetry and remote sensing, Vol 173 (March 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 173 (March 2021) . - pp 66 - 78 Titre : Enhanced trajectory estimation of mobile laser scanners using aerial images Type de document : Article/Communication Auteurs : Zille Hussnain, Auteur ; Sander J. Oude Elberink, Auteur ; M. George Vosselman, Auteur Année de publication : 2021 Article en page(s) : pp 66 - 78 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] appariement de points [Termes descripteurs IGN] atténuation du signal [Termes descripteurs IGN] balayage laser [Termes descripteurs IGN] canyon urbain [Termes descripteurs IGN] centrale inertielle [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] erreur [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] mesurage par GNSS [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] trajectoire [Termes descripteurs IGN] trajet multiple Résumé : (auteur) Multipath effects and signal obstruction by buildings in urban canyons can lead to inaccurate GNSS measurements and therefore errors in the estimated trajectory of Mobile Laser Scanning (MLS) systems; consequently, derived point clouds are distorted and lose spatial consistency. We obtain decimetre-level trajectory accuracy making use of corresponding points between the MLS data and aerial images with accurate exterior orientations instead of using ground control points. The MLS trajectory is estimated based on observation equations resulting from these corresponding points, the original IMU observations, and soft constraints on the pitch and yaw rotations of the vehicle. We analyse the quality of the trajectory enhancement under several conditions where the experiments were designed to test the influence of the number and quality of corresponding points and to test different settings for a B-spline representation of the vehicle trajectory. The method was tested on two independently acquired MLS datasets in Rotterdam by enhancing the trajectories and evaluating them using checkpoints. The RMSE values of the original GNSS/IMU based Kalman filter results at the checkpoints were 0.26 m, 0.30 m, and 0.47 m for the X-, Y- and Z-coordinates in the first dataset and 1.10 m, 1.51 m, and 1.81 m in the second dataset. The latter dataset was recorded with a lower quality IMU in an area with taller buildings. After trajectory adjustment these RMSE values were reduced to 0.09 m, 0.11 m, and 0.16 m for the first dataset and 0.12 m, 0.14 m, and 0.18 m for the second dataset. The results confirmed that, if sufficient tie points between the point cloud and aerial imagery are available, the method supports geo-referencing of MLS point clouds in urban canyons with a near-decimetre accuracy. Numéro de notice : A2021-102 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.005 date de publication en ligne : 17/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.005 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96877 [article]

A graph-based semi-supervised approach to classification learning in digital geographies / Pengyuan Liu in Computers, Environment and Urban Systems, vol 86 (March 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 86 (March 2021) . - n° 101583 Titre : A graph-based semi-supervised approach to classification learning in digital geographies Type de document : Article/Communication Auteurs : Pengyuan Liu, Auteur ; Stefano de Sabbata, Auteur Année de publication : 2021 Article en page(s) : n° 101583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse contextuelle [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification semi-dirigée [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] partage de données localisées [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] Time-geography Résumé : (auteur) As the distinction between online and physical spaces rapidly degrades, social media have now become an integral component of how many people's everyday experiences are mediated. As such, increasing interest has emerged in exploring how the content shared through those online platforms comes to contribute to the collaborative creation of places in physical space at the urban scale. Exploring digital geographies of social media data using methods such as qualitative coding (i.e., content labelling) is a flexible but complex task, commonly limited to small samples due to its impracticality over large datasets. In this paper, we propose a new tool for studies in digital geographies, bridging qualitative and quantitative approaches, able to learn a set of arbitrary labels (qualitative codes) on a small, manually-created sample and apply the same labels on a larger set. We introduce a semi-supervised, deep neural network approach to classify geo-located social media posts based on their textual and image content, as well as geographical and temporal aspects. Our innovative approach is rooted in our understanding of social media posts as augmentations of the time-space configurations that places are, and it comprises a stacked multi-modal autoencoder neural network to create joint representations of text and images, and a spatio-temporal graph convolution neural network for semi-supervised classification. The results presented in this paper show that our approach performs the classification of social media content with higher accuracy than traditional machine learning models as well as two state-of-art deep learning frameworks. Numéro de notice : A2021-024 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101583 date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101583 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96608 [article]

An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, Vol 172 (February 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 172 (February 2021) . - pp 114 - 131 Titre : An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds Type de document : Article/Communication Auteurs : Fei Su, Auteur ; Haihong Zhu, Auteur ; Taoyi Chen, Auteur Année de publication : 2021 Article en page(s) : pp 114 - 131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] adjacence [Termes descripteurs IGN] appariement de graphes [Termes descripteurs IGN] balayage laser [Termes descripteurs IGN] bloc d'ancrage [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] méthode du maximum de vraisemblance (estimation) [Termes descripteurs IGN] noeud [Termes descripteurs IGN] objet 3D [Termes descripteurs IGN] orientation [Termes descripteurs IGN] positionnement en intérieur [Termes descripteurs IGN] semis de points Résumé : (auteur) Most of the existing 3D indoor object classification methods have shown impressive achievements on the assumption that all objects are oriented in the upward direction with respect to the ground. To release this assumption, great effort has been made to handle arbitrarily oriented objects in terrestrial laser scanning (TLS) point clouds. As one of the most promising solutions, anchor-based graphs can be used to classify freely oriented objects. However, this approach suffers from missing anchor detection since valid detection relies heavily on the completeness of an anchor’s point clouds and is sensitive to missing data. This paper presents an anchor-based graph method to detect and classify arbitrarily oriented indoor objects. The anchors of each object are extracted by the structurally adjacent relationship among parts instead of the parts’ geometric metrics. In the case of adjacency, an anchor can be correctly extracted even with missing parts since the adjacency between an anchor and other parts is retained irrespective of the area extent of the considered parts. The best graph matching is achieved by finding the optimal corresponding node-pairs in a super-graph with fully connecting nodes based on maximum likelihood. The performances of the proposed method are evaluated with three indicators (object precision, object recall and object F1-score) in seven datasets. The experimental tests demonstrate the effectiveness of dealing with TLS point clouds, RGBD point clouds and Panorama RGBD point clouds, resulting in performance scores of approximately 0.8 for object precision and recall and over 0.9 for chair precision and table recall. Numéro de notice : A2021-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.12.007 date de publication en ligne : 29/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.12.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96852 [article]

Extracting knowledge from legacy maps to delineate eco-geographical regions / Lin Yang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 250 - 272 Titre : Extracting knowledge from legacy maps to delineate eco-geographical regions Type de document : Article/Communication Auteurs : Lin Yang, Auteur ; Xinming Li, Auteur ; Qinye Yang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 250 - 272 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] carte ancienne [Termes descripteurs IGN] carte climatique [Termes descripteurs IGN] cartographie écologique [Termes descripteurs IGN] Chine [Termes descripteurs IGN] délimitation [Termes descripteurs IGN] données cartographiques [Termes descripteurs IGN] écorégion [Termes descripteurs IGN] extraction de données [Termes descripteurs IGN] logique floue [Termes descripteurs IGN] sous ensemble flou [Termes descripteurs IGN] zone tampon Résumé : (auteur) Legacy ecoregion maps contain knowledge on relationships between eco-region units and their environmental factors. This study proposes a method to extract knowledge from legacy area-class maps to formulate a set of fuzzy membership functions useful for regionalization. We develop a buffer zone approach to reduce the uncertainty of boundaries between eco-region units on area-class maps. We generate buffer zones with a Euclidean distance perpendicular to the boundaries, then the original eco-region units without buffer zones serve as the basic units to generate the probability density functions (PDF) of environmental variables. Then, we transform the PDFs to fuzzy membership functions for class-zones on the map. We demonstrate the proposed method with a climatic zone map of China. The results showed that the buffer zone approach effectively reduced the uncertainties of boundaries. A buffer distance of 10–15 km was recommended in this study. The climatic zone map generated based on the extracted fuzzy membership functions showed a higher spatial stratification heterogeneity (compared to the original map). Based on the fuzzy membership functions with climate data of 1961–2015, we also prepared an updated climatic zone map. This study demonstrates the prospects of using fuzzy membership functions to delineate area classes for regionalization purpose. Numéro de notice : A2021-025 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1806284 date de publication en ligne : 17/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1806284 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96692 [article]

Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, Vol 172 (February 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 172 (February 2021) . - pp 79 - 94 Titre : Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning Type de document : Article/Communication Auteurs : Maryam Pourshamsi, Auteur ; Junshi Xia, Auteur ; Naoto Yokoya, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 79 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bande L [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] Gabon [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] Rotation Forest classification [Termes descripteurs IGN] semis de points Résumé : (auteur) Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms including Random Forest (RFs), Rotation Forest (RoFs), Canonical Correlation Forest (CCFs) and Support Vector Machine (SVMs). Various PolSAR parameters are required as input variables to ensure a successful height retrieval across different forest heights ranges. The algorithms are trained with 5000 LiDAR samples (less than 1% of the full scene) and different polarimetric variables. To examine the dependency of the algorithm on input training samples, three different subsets are identified which each includes different features: subset 1 is quiet diverse and includes non-vegetated region, short/sparse vegetation (0–20 m), vegetation with mid-range height (20–40 m) to tall/dense ones (40–60 m); subset 2 covers mostly the dense vegetated area with height ranges 40–60 m; and subset 3 mostly covers the non-vegetated to short/sparse vegetation (0–20 m) .The trained algorithms were used to estimate the height for the areas outside the identified subset. The results were validated with independent samples of LiDAR-derived height showing high accuracy (with the average R2 = 0.70 and RMSE = 10 m between all the algorithms and different training samples). The results confirm that it is possible to estimate forest canopy height using PolSAR parameters together with a small coverage of LiDAR height as training data. Numéro de notice : A2021-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.008 date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.008 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96846 [article]

Building extraction from Lidar data using statistical methods / Haval Abdul-Jabbar Sadeq in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

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Combining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)  

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Extraction of street pole-like objects based on plane filtering from mobile LiDAR data / Jingming Tu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

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Fusion of ground penetrating radar and laser scanning for infrastructure mapping / Dominik Merkle in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

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Georeferencing with self-calibration for airborne full-waveform Lidar data using digital elevation model / Qinghua Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

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Geospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data / C.M. Bhatt in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)  

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RegNet: a neural network model for predicting regional desirability with VGI data / Wenzhong Shi in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)  

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Relation-constrained 3D reconstruction of buildings in metropolitan areas from photogrammetric point clouds / Yuan Li in Remote sensing, vol 13 n° 1 (January 2021)  

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Sherloc: a knowledge-driven algorithm for geolocating microblog messages at sub-city level / Laura Di Rocco in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)  

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Structure-from-motion-derived digital surface models from historical aerial photographs: A new 3D application for coastal dune monitoring / Edoardo Grottoli in Remote sensing, vol 13 n° 1 (January 2021)  

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The influence of sea-level changes on geodetic datums along the east coast of China / Yang Liu in Marine geodesy, vol 44 n° 1 (January 2021)  

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Automated labeling of schematic maps by optimization with knowledge acquired from existing maps / Tian Lan in Transactions in GIS, Vol 24 n° 6 (December 2020)  

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A data fusion-based framework to integrate multi-source VGI in an authoritative land use database / Lanfa Liu in International Journal of Digital Earth, vol inconnu ([01/12/2020])  

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Du drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique / Maxime Lafleur in XYZ, n° 165 (décembre 2020)

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Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data / Yi Qiang in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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Exploring the heterogeneity of human urban movements using geo-tagged tweets / Ding Ma in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data / Chaogui Kang in Transactions in GIS, Vol 24 n° 6 (December 2020)  

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Inclusion of GPS clock estimates for satellites Sentinel-3A/3B in DORIS geodetic solutions / Petr Štěpánek in Journal of geodesy, vol 94 n° 12 (December 2020)  

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MS-RRFSegNetMultiscale regional relation feature segmentation network for semantic segmentation of urban scene point clouds / Haifeng Luo in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

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Remote sensing in urban planning: Contributions towards ecologically sound policies? / Thilo Wellmann in Landscape and Urban Planning, vol 204 (December 2020)  

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