Centre de documentation
scientifique

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 Projets : ARBRE / AgroParisTech (2007 -) 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- ) Autre URL associée : vers HAL 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]

Identify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 2060 - 2085 Titre : Identify urban building functions with multisource data: a case study in Guangzhou, China Type de document : Article/Communication Auteurs : Yingbin Deng, Auteur ; Renrong Chen, Auteur ; Yang Ji, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2060 - 2085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] approche hiérarchique [Termes IGN] batiment commercial [Termes IGN] bâtiment industriel [Termes IGN] bâtiment public [Termes IGN] Canton (Kouangtoung) [Termes IGN] données multisources [Termes IGN] empreinte [Termes IGN] exploration de données [Termes IGN] Extreme Gradient Machine [Termes IGN] figure géométrique [Termes IGN] image Gaofen [Termes IGN] logement [Termes IGN] point d'intérêt [Termes IGN] zone urbaine Résumé : (auteur) Building function type is an important parameter for urban planning and disaster management. However, existing identification methods do not always correctly recognize all building functions because of missing point of interest (POI) data in private areas. In this study, we proposed a hierarchical data-mining model to identify building function types using accessible auxiliary data, which was then applied to a case study. Residential building property was assessed to address missing residential POIs. The building functions were assigned to one of five different types, or a mixed-function type. Standard deviation and mean values extracted from remotely sensed images, distances to major roads, and building shape parameters were used to infer the function types of buildings without assigned function types. The proposed model was able to identify 65% of buildings not previously assigned as residential through the POI, with an overall accuracy of 87%. In addition, all buildings were successfully assigned a function type of residential, commercial, office, warehouse, public service, or mixed-function, with an overall accuracy of 85% for unclassified buildings. Our results demonstrated that missing POI data in private areas could be addressed by integration with multisource data using a simple method. Numéro de notice : A2022-739 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2046756 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2046756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101716 [article]

3D building reconstruction from single street view images using deep learning / Hui En Pang in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 112 (August 2022) . - n° 102859 Titre : 3D building reconstruction from single street view images using deep learning Type de document : Article/Communication Auteurs : Hui En Pang, Auteur ; Filip Biljecki, Auteur Année de publication : 2022 Article en page(s) : n° 102859 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] empreinte [Termes IGN] Helsinki [Termes IGN] image Streetview [Termes IGN] maillage [Termes IGN] morphologie urbaine [Termes IGN] précision géométrique (imagerie) [Termes IGN] reconstruction 3D du bâti [Termes IGN] segmentation d'image [Termes IGN] semis de points Résumé : (auteur) 3D building models are an established instance of geospatial information in the built environment, but their acquisition remains complex and topical. Approaches to reconstruct 3D building models often require existing building information (e.g. their footprints) and data such as point clouds, which are scarce and laborious to acquire, limiting their expansion. In parallel, street view imagery (SVI) has been gaining currency, driven by the rapid expansion in coverage and advances in computer vision (CV), but it has not been used much for generating 3D city models. Traditional approaches that can use SVI for reconstruction require multiple images, while in practice, often only few street-level images provide an unobstructed view of a building. We develop the reconstruction of 3D building models from a single street view image using image-to-mesh reconstruction techniques modified from the CV domain. We regard three scenarios: (1) standalone single-view reconstruction; (2) reconstruction aided by a top view delineating the footprint; and (3) refinement of existing 3D models, i.e. we examine the use of SVI to enhance the level of detail of block (LoD1) models, which are common. The results suggest that trained models supporting (2) and (3) are able to reconstruct the overall geometry of a building, while the first scenario may derive the approximate mass of the building, useful to infer the urban form of cities. We evaluate the results by demonstrating their usefulness for volume estimation, with mean errors of less than 10% for the last two scenarios. As SVI is now available in most countries worldwide, including many regions that do not have existing footprint and/or 3D building data, our method can derive rapidly and cost-effectively the 3D urban form from SVI without requiring any existing building information. Obtaining 3D building models in regions that hitherto did not have any, may enable a number of 3D geospatial analyses locally for the first time. Numéro de notice : A2022-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102859 Date de publication en ligne : 17/06/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102859 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101160 [article]

3D-GIS parametric modelling for virtual urban simulation using CityEngine / Ibrahim M. Badwi in Annals of GIS, vol 28 n° 3 (July 2022)  

Public [article]  inAnnals of GIS > vol 28 n° 3 (July 2022) . - pp 325 - 341 Titre : 3D-GIS parametric modelling for virtual urban simulation using CityEngine Type de document : Article/Communication Auteurs : Ibrahim M. Badwi, Auteur ; Hisham M. Ellaithy, Auteur ; Hidi E. Youssef, Auteur Année de publication : 2022 Article en page(s) : pp 325 - 341 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] base de données localisées [Termes IGN] Bâti-3D [Termes IGN] CityEngine [Termes IGN] données localisées 2D [Termes IGN] Egypte [Termes IGN] empreinte [Termes IGN] espace vert [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] réseau routier [Termes IGN] SIG 3D [Termes IGN] système d'information urbain [Termes IGN] urbanisme Résumé : (auteur) Modelling and visualization of three-dimensional (3D) models for cities is a great challenge for computer software and graphics. Recently, 3D city modelling has grown due to advances in applications accompanying the information technology revolution. 3D Geographic Information Systems (3D-GIS) have evolved enormously due to the availability of large-scale 3D modelling techniques. These technologies have become very important in representing large cities and conducting various analyses in the city’s virtual environment to support urban decision-making. CityEngine is one of the most recent 3D-GIS modelling applications. CityEngine can be described as parametric modelling using Procedural Modelling (PM) to create 3D urban elements through macros and routines. This paper highlights the importance of 3D Procedural Modelling (PM) of cities in the GIS environment using ESRI CityEngine and presents a parametric concept for designing urban spaces. This issue has been addressed in three respects. First, discuss the concept and strength of parametric design. Second, the concept of procedural modelling and its power to generate complex 3D models using a set of rules is discussed. Finally, CityEngine was evaluated through a real-world case study of a neighbourhood in the new city of Beni-Suef, Egypt. The results confirm the effectiveness of CityEngine as a 3D-GIS modelling software that generates dynamic 3D models from 2D spatial data. While the results are promising, it is important to investigate more complex cases. The CityEngine modelling approach enables comprehensive urban analyses such as sequence vision, façade studies, urban fabric and character, and statistical operations based on attribute database. Numéro de notice : A2022-641 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2022.2037019 Date de publication en ligne : 03/03/2022 En ligne : https://doi.org/10.1080/19475683.2022.2037019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101449 [article]

GANmapper: geographical data translation / Abraham Noah Wu in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 7 (juillet 2022) . - pp 1394 - 1422 Titre : GANmapper: geographical data translation Type de document : Article/Communication Auteurs : Abraham Noah Wu, Auteur ; Filip Biljecki, Auteur Année de publication : 2022 Article en page(s) : pp 1394 - 1422 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes IGN] apprentissage automatique [Termes IGN] bâtiment [Termes IGN] distance de Fréchet [Termes IGN] empreinte [Termes IGN] morphologie urbaine [Termes IGN] réseau antagoniste génératif [Termes IGN] réseau routier [Termes IGN] système d'information géographique [Termes IGN] texture d'image Résumé : (auteur) We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables the translation of one geospatial dataset to another with high fidelity and morphological accuracy. It may be especially useful in locations missing detailed and high-resolution data and those that are mapped with uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality of the results is influenced by the urban form and scale. In most cases, the experiments suggest promising performance as the method tends to truthfully indicate the locations, amount, and shape of buildings. The work has the potential to support several applications, such as energy, climate, and urban morphology studies in areas previously lacking required data or inpainting geospatial data in regions with incomplete data. Numéro de notice : A2022-493 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2041643 Date de publication en ligne : 08/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2041643 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100975 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2022071	SL	Revue	Centre de documentation	Revues en salle	Disponible

Artificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data / Saeideh Sahebi Vayghan in Geocarto international, vol 37 n° 10 ([01/06/2022])  

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3D building model simplification method considering both model mesh and building structure / Jiangfeng She in Transactions in GIS, vol 26 n° 3 (May 2022)  

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City3D: Large-scale building reconstruction from airborne LiDAR point clouds / Jin Huang in Remote sensing, vol 14 n° 9 (May-1 2022)  

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Automated 3D reconstruction of LoD2 and LoD1 models for All 10 million buildings of the Netherlands / Ravi Peters in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)  

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Building footprint extraction in Yangon city from monocular optical satellite image using deep learning / Hein Thura Aung in Geocarto international, vol 37 n° 3 ([01/02/2022])  

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Footprint size design of large-footprint full-waveform LiDAR for forest and topography applications: A theoretical study / Xuebo Yang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)  

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A deep multi-modal learning method and a new RGB-depth data set for building roof extraction / Mehdi Khoshboresh Masouleh in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)  

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A novel method based on deep learning, GIS and geomatics software for building a 3D city model from VHR satellite stereo imagery / Massimiliano Pepe in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)  

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Fast unsupervised multi-scale characterization of urban landscapes based on Earth observation data / Claire Teillet in Remote sensing, vol 13 n° 12 (June-2 2021)  

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3D reconstruction of bridges from airborne laser scanning data and cadastral footprints / Steffen Goebbels in Journal of Geovisualization and Spatial Analysis, vol 5 n° 1 (June 2021)  

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