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Evapotranspiration mapping of cotton fields in Brazil: comparison between SEBAL and FAO-56 method / Juan Vicente Liendro Moncada in Geocarto international, Vol 37 n° 17 ([20/08/2022])
[article]
Titre : Evapotranspiration mapping of cotton fields in Brazil: comparison between SEBAL and FAO-56 method Type de document : Article/Communication Auteurs : Juan Vicente Liendro Moncada, Auteur ; Tonny José Araújo da Silva, Auteur ; Jefferson Vieira José, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 5133 - 5149 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] carte thématique
[Termes IGN] corrélation
[Termes IGN] données météorologiques
[Termes IGN] évapotranspiration
[Termes IGN] Gossypium (genre)
[Termes IGN] GRASS
[Termes IGN] image Landsat-8
[Termes IGN] Mato Grosso
[Termes IGN] modèle de Monteith
[Termes IGN] phénologie
[Termes IGN] QGIS
[Termes IGN] régression logistique
[Termes IGN] système d'information géographiqueRésumé : (auteur) The objective was to compare the evapotranspiration of cotton (Gossypium sp. L.) estimated by the SEBAL model and the FAO-56 method, throughout the phenological cycle of the plant on eight fields located in the upper area of the Rio das Mortes basin, State of Mato Grosso—Brazil. Images from the Landsat 8 satellite were used under the Geographic Information Systems environment through the capabilities of the QGIS 3.6.2 and GRASS 7.6.1 software. The reference evapotranspiration was determined by the FAO Penman–Monteith method implementing the Ref-ET software and data from the Campo Verde meteorological station of INMET—Brazil. The R software was applied to the statistical analyses of correlation and regression. The dataset of the available stages of the cotton phenological cycle shows a strong positive correlation, with approximately 68% of the evapotranspiration variation of the SEBAL model related to the estimates of the FAO-56 method. Numéro de notice : A2022-700 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1920633 Date de publication en ligne : 06/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1920633 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101559
in Geocarto international > Vol 37 n° 17 [20/08/2022] . - pp 5133 - 5149[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)
[article]
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 pointsRésumé : (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
in International journal of applied Earth observation and geoinformation > vol 112 (August 2022) . - n° 102859[article]3D semantic scene completion: A survey / Luis Roldão in International journal of computer vision, vol 130 n° 8 (August 2022)
[article]
Titre : 3D semantic scene completion: A survey Type de document : Article/Communication Auteurs : Luis Roldão, Auteur ; Raoul de Charette, Auteur ; Anne Verroust-Blondet, Auteur Année de publication : 2022 Article en page(s) : pp 1978 - 2005 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] effet de profondeur cinétique
[Termes IGN] image RVB
[Termes IGN] reconstruction d'image
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] voxelRésumé : (auteur) Semantic scene completion (SSC) aims to jointly estimate the complete geometry and semantics of a scene, assuming partial sparse input. In the last years following the multiplication of large-scale 3D datasets, SSC has gained significant momentum in the research community because it holds unresolved challenges. Specifically, SSC lies in the ambiguous completion of large unobserved areas and the weak supervision signal of the ground truth. This led to a substantially increasing number of papers on the matter. This survey aims to identify, compare and analyze the techniques providing a critical analysis of the SSC literature on both methods and datasets. Throughout the paper, we provide an in-depth analysis of the existing works covering all choices made by the authors while highlighting the remaining avenues of research. SSC performance of the SoA on the most popular datasets is also evaluated and analyzed. Numéro de notice : A2022-593 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-021-01504-5 Date de publication en ligne : 06/06/2022 En ligne : http://dx.doi.org/10.1007/s11263-021-01504-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101296
in International journal of computer vision > vol 130 n° 8 (August 2022) . - pp 1978 - 2005[article]An automatic approach for tree species detection and profile estimation of urban street trees using deep learning and Google street view images / Kwanghun Choi in ISPRS Journal of photogrammetry and remote sensing, vol 190 (August 2022)
[article]
Titre : An automatic approach for tree species detection and profile estimation of urban street trees using deep learning and Google street view images Type de document : Article/Communication Auteurs : Kwanghun Choi, Auteur ; Wontaek LIM, Auteur ; Byungwoo Chang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 165 - 180 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] arbre urbain
[Termes IGN] détection automatique
[Termes IGN] détection d'arbres
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] gestion forestière durable
[Termes IGN] image Streetview
[Termes IGN] inventaire de la végétation
[Termes IGN] segmentation sémantique
[Termes IGN] SéoulRésumé : (auteur) Tree species and canopy structural profile (‘tree profile’) are among the most critical environmental factors in determining urban ecosystem services such as climate and air quality control from urban trees. To accurately characterize a tree profile, the tree diameter, height, crown width, and height to the lowest live branch must be all measured, which is an expensive and time-consuming procedure. Recent advances in artificial intelligence aids to efficiently and accurately measure the aforementioned tree profile parameters. This can be particularly helpful if spatially extensive and accurate street-level images provided by Google (‘streetview’) or Kakao (‘roadview’) are utilized. We focused on street trees in Seoul, the capital city of South Korea, and suggested a novel approach to create a tree profile and inventory based on deep learning algorithms. We classified urban tree species using the YOLO (You Only Look Once), one of the most popular deep learning object detection algorithms, which provides an uncomplicated method of creating datasets with custom classes. We further utilized semantic segmentation algorithm and graphical analysis to estimate tree profile parameters by determining the relative location of the interface of tree and ground surface. We evaluated the performance of the model by comparing the estimated tree heights, diameters, and locations from the model with the field measurements as ground truth. The results are promising and demonstrate the potential of the method for creating urban street tree profile inventory. In terms of tree species classification, the method showed the mean average precision (mAP) of 0.564. When we used the ideal tree images, the method also reported the normalized root mean squared error (NRMSE) for the tree height, diameter at breast height (DBH), and distances from the camera to the trees as 0.24, 0.44, and 0.41. Numéro de notice : A2022-503 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.06.004 Date de publication en ligne : 22/06/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.06.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101001
in ISPRS Journal of photogrammetry and remote sensing > vol 190 (August 2022) . - pp 165 - 180[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2022081 SL Revue Centre de documentation Revues en salle Disponible 081-2022083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Change detection in street environments based on mobile laser scanning: A fuzzy spatial reasoning approach / Joachim Gehrung in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)
[article]
Titre : Change detection in street environments based on mobile laser scanning: A fuzzy spatial reasoning approach Type de document : Article/Communication Auteurs : Joachim Gehrung, Auteur ; Marcus Hebel, Auteur ; Michael Arens, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection automatique
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] Inférence floue
[Termes IGN] information sémantique
[Termes IGN] logique floue
[Termes IGN] milieu urbain
[Termes IGN] représentation spatiale
[Termes IGN] semis de points
[Termes IGN] voxelRésumé : (auteur) Automated change detection based on urban mobile laser scanning data is the foundation for a whole range of applications such as building model updates, map generation for autonomous driving and natural disaster assessment. The challenge with mobile LiDAR data is that various sources of error, such as localization errors, lead to uncertainties and contradictions in the derived information. This paper presents an approach to automatic change detection using a new category of generic evidence grids that addresses the above problems. Said technique, referred to as fuzzy spatial reasoning, solves common problems of state-of-the-art evidence grids and also provides a method of inference utilizing fuzzy Boolean reasoning. Based on this, logical operations are used to determine changes and combine them with semantic information. A quantitative evaluation based on a hand-annotated version of the TUM-MLS data set shows that the proposed method is able to identify confirmed and changed elements of the environment with F1-scores of 0.93 and 0.89. Numéro de notice : A2022-663 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100019 En ligne : https://doi.org/10.1016/j.ophoto.2022.100019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101524
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