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HGAT-VCA: Integrating high-order graph attention network with vector cellular automata for urban growth simulation / Xuefeng Guan in Computers, Environment and Urban Systems, vol 99 (January 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 99 (January 2023) . - n° 101900 Titre : HGAT-VCA: Integrating high-order graph attention network with vector cellular automata for urban growth simulation Type de document : Article/Communication Auteurs : Xuefeng Guan, Auteur ; Weiran Xing, Auteur ; Jingbo Li, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101900 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] adjacence [Termes IGN] attention (apprentissage automatique) [Termes IGN] automate cellulaire [Termes IGN] changement d'utilisation du sol [Termes IGN] croissance urbaine [Termes IGN] étalement urbain [Termes IGN] hétérogénéité spatiale [Termes IGN] modèle de simulation [Termes IGN] Queensland (Australie) [Termes IGN] réseau neuronal de graphes [Termes IGN] voisinage (relation topologique) [Termes IGN] zone tampon Résumé : (auteur) Since urban growth results from frequent spatial interaction between urban units, adequate representation of spatial interaction is important for urban growth modeling. Among urban growth models, vector-based cellular automata (VCA) excels at expressing spatial interaction with realistic entities, and has accordingly been used extensively in recent studies. However, two issues with VCA modeling still remain: 1) inefficient manual selection of interaction targets with various neighborhood configurations; 2) inaccurate quantification of interaction intensity due to ignorance of spatial heterogeneity in entity interaction. To address these two limitations, this study proposed a novel VCA model with high-order graph attention network (HGAT-VCA). In this model, a graph structure is first built from the topology adjacency relationship between cadastral parcels. In terms of the HGAT components, the original 1st-order parcel neighborhood is extended to high-order to capture the distant dependency, while graph attention is applied to quantify the heterogeneous interaction intensity between parcels. Finally, the conversion probability obtained by HGAT is integrated with VCA to simulate urban land use change. Land use data from the Moreton Bay Region in Queensland, Australia from 2005 to 2009 are selected to verify the proposed HGAT-VCA model. Experimental results illustrate that HGAT-VCA outperforms four classical CA models and achieves the highest simulation accuracy (e.g., the increase of FoM is about 40.7%). In addition, extensive neighborhood configuration experiments show that with HGAT only tuning discrete topological order can generate similar accuracy results compared with the repetitive buffer-based neighborhood configuration, and this can significantly improve the calibration efficiency of VCA models. Numéro de notice : A2023-031 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101900 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101900 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102163 [article]

The re-invention of the Goori cultural landscape: Telling the country: Mapping two pockets / Paul Memmott in Cartographica, Vol 57 n° 1 (Spring 2022)  

Public [article]  inCartographica > Vol 57 n° 1 (Spring 2022) . - pp 65-79 Titre : The re-invention of the Goori cultural landscape: Telling the country: Mapping two pockets Type de document : Article/Communication Auteurs : Paul Memmott, Auteur ; Ray Kerkhove, Auteur ; Alex Bond, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 65-79 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Brisbane (Australie) [Termes IGN] communication cartographique [Termes IGN] corpus [Termes IGN] culture [Termes IGN] droit foncier [Termes IGN] ethnologie [Termes IGN] ontologie [Termes IGN] patrimoine culturel [Termes IGN] période coloniale [Termes IGN] Queensland (Australie) [Vedettes matières IGN] Cartologie Résumé : (auteur) This article analyzes the authors’ map of the Aboriginal geography of St Lucia and Long Pocket, two riverine suburbs of Brisbane, upstream of the central business district, and containing two of the University of Queensland’s campuses. The map is a prism into the wider “Goori” Aboriginal society of the early 1800s. The map was generated by two Aboriginal scholars and an anthropologist using a practice-based ontological approach and by historians using early textual sources. The map juxtaposes a geopolitical edge against contemporary metropolitan mapping, providing a foundation of First Nations geography to underlie and undermine the power of colonial and postcolonial cartography. Numéro de notice : A2022-246 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3138/cart-2021-0022 Date de publication en ligne : 15/03/2022 En ligne : https://doi.org/10.3138/cart-2021-0022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100189 [article]

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Optimising drone flight planning for measuring horticultural tree crop structure / Yu-Hsuan Tu in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 83 - 96 Titre : Optimising drone flight planning for measuring horticultural tree crop structure Type de document : Article/Communication Auteurs : Yu-Hsuan Tu, Auteur ; Stuart Phinn, Auteur ; Kasper Johansen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 83 - 96 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] correction d'image [Termes IGN] détection d'arbres [Termes IGN] distorsion d'image [Termes IGN] étalonnage de chambre métrique [Termes IGN] horticulture [Termes IGN] image captée par drone [Termes IGN] MicMac [Termes IGN] obturateur [Termes IGN] photogrammétrie aérienne [Termes IGN] plan de vol [Termes IGN] point d'appui [Termes IGN] qualité d'image [Termes IGN] Queensland (Australie) [Termes IGN] semis de points Résumé : (Auteur) In recent times, multi-spectral drone imagery has proved to be a useful tool for measuring tree crop canopy structure. In this context, establishing the most appropriate flight planning variable settings is an essential consideration due to their controls on the quality of the imagery and derived maps of tree and crop biophysical properties. During flight planning, variables including flight altitude, image overlap, flying direction, flying speed and solar elevation, require careful consideration in order to produce the most suitable drone imagery. Previous studies have assessed the influence of individual variables on image quality, but the interaction of multiple variables has yet to be examined. This study assesses the influence of several flight variables on measures of data quality in each processing step, i.e. photo alignment, point cloud densification, 3D model building, and ortho-mosaicking. The analysis produced a drone flight planning and image processing workflow that delivers accurate measurements of tree crops, including the tie point quality, densified point cloud density, and the measurement accuracy of height and plant projective cover derived from individual trees within a commercial avocado orchard. Results showed that flying along the hedgerow, at high solar elevation and with low image pitch angles improved the data quality. Optimal flying speed needs to be set to achieve the required forward overlap. The impacts of each image acquisition variable are discussed in detail and protocols for flight planning optimisation for three scenarios with different drone settings are suggested. Establishing protocols that deliver optimal image acquisitions for the collection of drone data over horticultural tree crops, will create greater confidence in the accuracy of subsequent algorithms and resultant maps of biophysical properties. Numéro de notice : A2020-044 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.006 Date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.006 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94524 [article]

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Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia / Neil Flood in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 15 p. Titre : Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia Type de document : Article/Communication Auteurs : Neil Flood, Auteur ; Fiona Watson, Auteur ; Lisa Collett, Auteur Année de publication : 2019 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] arbre (flore) [Termes IGN] arbuste [Termes IGN] bois sur pied [Termes IGN] carte de la végétation [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image à haute résolution [Termes IGN] image satellite [Termes IGN] méthode de Monte-Carlo [Termes IGN] mosaïque d'images [Termes IGN] Queensland (Australie) [Termes IGN] réseau neuronal convolutif [Termes IGN] texture d'image Résumé : (auteur) Convolutional neural networks offer a new approach to classifying high resolution imagery. We use the U-net neural network architecture to map the presence or absence of trees and large shrubs across the Australian state of Queensland. From a state-wide mosaic of 1 m resolution 3-band Earth-i imagery, a selection of 827 squares (1 km2) are manually labeled for the presence of trees or large shrubs, and these are used to train the neural network. The training is intended to capture the textures which are primary visual cues of such vegetation. The trained neural network has an accuracy on independent data of around 90%. The resulting map over the whole of Queensland (1.73 million km2) is intended to be manually checked, and edited where necessary, to provide a high quality map of woody vegetation extent to serve a range of government policy objectives. Numéro de notice : A2019-474 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.101897 Date de publication en ligne : 28/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.101897 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93635 [article]

Modelling the effects of fundamental UAV flight parameters on LiDAR point clouds to facilitate objectives-based planning / Jeremy J. Sofonia in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 105 - 118 Titre : Modelling the effects of fundamental UAV flight parameters on LiDAR point clouds to facilitate objectives-based planning Type de document : Article/Communication Auteurs : Jeremy J. Sofonia, Auteur ; Stuart Phinn, Auteur ; Chris Roelfsema, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 105 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] balayage laser [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image captée par drone [Termes IGN] modèle de simulation [Termes IGN] plan de vol [Termes IGN] Queensland (Australie) [Termes IGN] semis de points Résumé : (Auteur) Utilised globally across a wide range of applications, the ability to assess and understand LiDAR system capabilities represents an essential component in developing informed decisions on instrument selection and the logistical planning processes associated with site-specific limitations, project objectives and UAV operations. This study employed the new SLAM-based CSIRO “Hovermap” LiDAR system within a purpose-built environment as a testbed to experimentally investigate the interactive effects of fundamental UAV flight parameters on key metrics of LiDAR point clouds. Assessed within a full factorial design at both Site- and Target-levels, the UAV input variables of Pattern, ground Speed and above ground Altitude (AGL) were tested against the point cloud response variables Density, GSD and Accuracy as measured by RMSE and cloud-to-mesh Euclidian distance (‘Deviation’). A novel approach is described wherein the trajectory file of each flight was examined to determine the observed values of the input and response variables, remove noise and facilitate a standardised basis of comparison. Several new terms are introduced including Sampling Effort Variable (SEV, s⋅m−2), Effective Scan Rate (ESR, pts⋅s−1) and Effective Density Rate (EDR, pts⋅m−2⋅s−1) as well as an alternate approach to describe Pattern (s⋅m−1) as a scalar quantity. Reporting significant effects with all response variables at both Site- and Target-levels, the Range of the LiDAR sensor, closely associated with Altitude, presented as the single most important factor. Interestingly, the combination of the independent variables as SEV and EDRpred (‘predicted’ EDR) showed the highest coefficient of determination in the Site-level prediction of Density (AdjR2 = 0.894) and GSD (AdjR2 = 0.978,), respectively, whilst Range best correlated with observed RMSE (AdjR2 = 0.948) and Deviation (AdjR2 = 0.963). Predictive models returned mixed results when evaluated at the Target-level and highlights the need for further investigation to achieve the maximum benefit of high-resolution UAV LiDAR. The results presented here confirm that the CSIRO Hovermap performance is robust and, although variable depending on UAV flight parameters, is predictable and demonstrates the potential value in understanding system performance in harmonised flight planning to achieve project-specific objectives. Numéro de notice : A2019-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.020 Date de publication en ligne : 28/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92443 [article]

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La propriété en 3D : état des lieux / Anonyme in Géomatique expert, n° 123 (juillet - août 2018)

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Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland, Australia / M. Lyons in ISPRS Journal of photogrammetry and remote sensing, vol 71 (July 2012)  

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Solutions for 4D cadastre - with a case study on utility networks / F. Döner in International journal of geographical information science IJGIS, vol 25 n° 6-7 (june - july 2011)  

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AAM modelling aids Brisbane floods crisis / J. Blackburn in GEO: Geoconnexion international, vol 10 n° 4 (april 2011)

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Technological aspects of a full 3D cadastral registration / Jantien E. Stoter in International journal of geographical information science IJGIS, vol 19 n° 6 (july 2005)  

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Reflectance modeling of semiarid woodlands / R.P. Pech in Remote sensing of environment, vol 23 n° 2 (01/11/1987)  

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Updating maps of climax vegetation cover with Landsat MSS data in Queensland, Australia / G.D. Kelly in Photogrammetric Engineering & Remote Sensing, PERS, vol 53 n° 6 Tome 1 (june 1987)  

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Habitat mapping by Landsat for aerial census of kangaroos / G.J.E. Hill in Remote sensing of environment, vol 21 n° 1 (01/02/1987)  

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