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Vine canopy reconstruction and assessment with terrestrial Lidar and aerial imaging / Igor Petrovic in Remote sensing, vol 14 n° 22 (November-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 22 (November-2 2022) . - n° 5894 Titre : Vine canopy reconstruction and assessment with terrestrial Lidar and aerial imaging Type de document : Article/Communication Auteurs : Igor Petrovic, Auteur ; Matej Sečnik, Auteur ; Marko Hočevar, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5894 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] analyse comparative [Termes IGN] couvert végétal [Termes IGN] défoliation [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] échantillonnage de données [Termes IGN] épandage [Termes IGN] lasergrammétrie [Termes IGN] pas d'échantillonnage au sol [Termes IGN] photogrammétrie aérienne [Termes IGN] Slovénie [Termes IGN] viticulture Résumé : (auteur) For successful dosing of plant protection products, the characteristics of the vine canopies should be known, based on which the spray amount should be dosed. In the field experiment, we compared two optical experimental methods, terrestrial lidar and aerial photogrammetry, with manual defoliation of some selected vines. Like those of other authors, our results show that both terrestrial lidar and aerial photogrammetry were able to represent the canopy well with correlation coefficients around 0.9 between the measured variables and the number of leaves. We found that in the case of aerial photogrammetry, significantly more points were found in the point cloud, but this depended on the choice of the ground sampling distance. Our results show that in the case of aerial UAS photogrammetry, subdividing the vine canopy segments to 5 × 5 cm gives the best representation of the volume of vine canopies. Numéro de notice : A2022-881 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14225894 Date de publication en ligne : 21/11/2022 En ligne : https://doi.org/10.3390/rs14225894 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102203 [article]

Simulation-driven 3D forest growth forecasting based on airborne topographic LiDAR data and shading / Štefan Kohek in International journal of applied Earth observation and geoinformation, vol 111 (July 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 111 (July 2022) . - n° 102844 Titre : Simulation-driven 3D forest growth forecasting based on airborne topographic LiDAR data and shading Type de document : Article/Communication Auteurs : Štefan Kohek, Auteur ; Borut Žalik, Auteur ; Damjan Strnad, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102844 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse de sensibilité [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] dissymétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] houppier [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle numérique de terrain [Termes IGN] modélisation de la forêt [Termes IGN] ombre [Termes IGN] semis de points [Termes IGN] Slovénie Résumé : (auteur) Reliable forest growth forecasting requires detailed tree data for forest simulation, while manual on-site collection of relevant data is work-intensive and unfeasible in larger forests. This paper proposes a complete methodology for fully automated forest growth simulation that relies primarily on airborne topographic Light Detection And Ranging (LiDAR) point clouds of individual trees. The proposed method estimates tree parameters and performs growth of individual trees based on an individual-based forest growth simulator, named BWINPro. In addition, competition and detailed asymmetric tree crown growth are modeled regarding the shading of tree crowns, which is estimated from the surrounding environment and neighbor trees. The result of the proposed approach is a new point cloud for subsequent analyses. The proposed method was validated by comparing canopy height models derived from the point clouds of the simulated trees with canopy height models derived from more recent ground truth point clouds. The results demonstrate the efficacy of the proposed method which achieves a 9.4% higher accuracy than the averaged linear regression model and, in the case of datasets with more distinct self-standing trees, where a tree crown boundary plays major role, a 4.1% higher accuracy than the directly fitted linear regression model. Numéro de notice : A2022-552 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102844 Date de publication en ligne : 04/06/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102844 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101156 [article]

Revising cadastral data on land boundaries using deep learning in image-based mapping / Bujar Fetai in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 5 (May 2022) . - n° 298 Titre : Revising cadastral data on land boundaries using deep learning in image-based mapping Type de document : Article/Communication Auteurs : Bujar Fetai, Auteur ; Dejan Grigillo, Auteur ; Anka Lisec, Auteur Année de publication : 2022 Article en page(s) : n° 298 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] cadastre étranger [Termes IGN] cartographie cadastrale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de contours [Termes IGN] données cadastrales [Termes IGN] limite cadastrale [Termes IGN] point d'appui [Termes IGN] Slovénie Résumé : (auteur) One of the main concerns of land administration in developed countries is to keep the cadastral system up to date. The goal of this research was to develop an approach to detect visible land boundaries and revise existing cadastral data using deep learning. The convolutional neural network (CNN), based on a modified architecture, was trained using the Berkeley segmentation data set 500 (BSDS500) available online. This dataset is known for edge and boundary detection. The model was tested in two rural areas in Slovenia. The results were evaluated using recall, precision, and the F1 score—as a more appropriate method for unbalanced classes. In terms of detection quality, balanced recall and precision resulted in F1 scores of 0.60 and 0.54 for Ponova vas and Odranci, respectively. With lower recall (completeness), the model was able to predict the boundaries with a precision (correctness) of 0.71 and 0.61. When the cadastral data were revised, the low values were interpreted to mean that the lower the recall, the greater the need to update the existing cadastral data. In the case of Ponova vas, the recall value was less than 0.1, which means that the boundaries did not overlap. In Odranci, 21% of the predicted and cadastral boundaries overlapped. Since the direction of the lines was not a problem, the low recall value (0.21) was mainly due to overly fragmented plots. Overall, the automatic methods are faster (once the model is trained) but less accurate than the manual methods. For a rapid revision of existing cadastral boundaries, an automatic approach is certainly desirable for many national mapping and cadastral agencies, especially in developed countries. Numéro de notice : A2022-357 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11050298 Date de publication en ligne : 04/05/2022 En ligne : https://doi.org/10.3390/ijgi11050298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100562 [article]

Partitions of normalised multiple regression equations for datum transformations / Andrew Carey Ruffhead in Boletim de Ciências Geodésicas, vol 28 n° 1 ([01/03/2022])  

Public [article]  inBoletim de Ciências Geodésicas > vol 28 n° 1 [01/03/2022] Titre : Partitions of normalised multiple regression equations for datum transformations Type de document : Article/Communication Auteurs : Andrew Carey Ruffhead, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] Australie occidentale (Australie) [Termes IGN] Grande-Bretagne [Termes IGN] régression multiple [Termes IGN] Slovénie [Termes IGN] transformation de coordonnées Résumé : (auteur) Multiple regression equations (MREs) provide an empirical direct method of transforming coordinates between geodetic datums. Since they offer a means of modelling distortions, they are capable of a more accurate fit to datum-shift datasets than more basic direct methods. MRE models of datum shifts traditionally consist of polynomials based on relative latitude and longitude. However, the limited availability of low-power terms often leads to high-power terms being included, and these are a potential cause of instability. This paper introduces three variations based on simple partitions and 2 or 4 smoothly conjoined polynomials. The new types are North/South, East/West and Four-Quadrant. They increase the availability of low-order terms, enabling distortions to be modelled with fewer side effects. Case studies in Great Britain, Slovenia and Western Australia provide examples of partitioned MREs that are more accurate than conventional MREs with the same number of terms. Numéro de notice : A2022-684 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans En ligne : https://revistas.ufpr.br/bcg/article/view/86199/46467 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101548 [article]

Building detection with convolutional networks trained with transfer learning / Simon Šanca in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)  

Public [article]  inGeodetski vestnik > vol 65 n° 4 (December 2021 - February 2022) . - pp 559 - 576 Titre : Building detection with convolutional networks trained with transfer learning Type de document : Article/Communication Auteurs : Simon Šanca, Auteur ; Krištof Oštir, Auteur ; Alen Mangafić, Auteur Année de publication : 2021 Article en page(s) : pp 559 - 576 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification automatique d'objets [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] données cadastrales [Termes IGN] image aérienne [Termes IGN] image infrarouge couleur [Termes IGN] image proche infrarouge [Termes IGN] image RVB [Termes IGN] orthoimage couleur [Termes IGN] segmentation d'image [Termes IGN] Slovénie Résumé : (Auteur) Building footprint detection based on orthophotos can be used to update the building cadastre. In recent years deep learning methods using convolutional neural networks have been increasingly used around the world. We present an example of automatic building classification using our datasets made of colour near-infrared orthophotos (NIR-R-G) and colour orthophotos (R-G-B). Building detection using pretrained weights from two large scale datasets Microsoft Common Objects in Context (MS COCO) and ImageNet was performed and tested. We applied the Mask Region Convolutional Neural Network (Mask R-CNN) to detect the building footprints. The purpose of our research is to identify the applicability of pre-trained neural networks on the data of another colour space to build a classification model without re-learning. Numéro de notice : A2021-930 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.15292/geodetski-vestnik.2021.04.559-593 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.04.559-593 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99409 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2021041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

The willingness of volonteers to report changes on topographic maps / Mihaela Triglav Cekada in Geodetski vestnik, vol 65 n° 3 (September - November 2021)  

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Fluvial gravel bar mapping with spectral signal mixture analysis / Liza Stančič in European journal of remote sensing, vol 54 sup 1 (2021)  

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Predicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops / Nina Kranjec in Geodetski vestnik, vol 65 n° 2 (June - August 2021)  

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Tree height growth modelling using LiDAR-derived topography information / Milan Kobal in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)  

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3D laser scanning of the natural caves: Example of Škocjanske jame / Richard Walters in Geodetski vestnik, Vol 64 n° 1 (March - May 2020)  

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Delineation of vacant building land using orthophoto and lidar data object classification / Dejan Jenko in Geodetski vestnik, vol 63 n° 3 (September - November 2019)  

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Fuzzy modelling of growth potential in forest development simulation / Damjan Strnad in Ecological Informatics, vol 48 (November 2018)  

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Accurate area determination in the cadaster: case study of Slovenia / Sandi Berk in Cartography and Geographic Information Science, Vol 45 n° 1 (January 2018)  

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Use of unsupervised classification for the determination of prevailing land use typology / Miha Konjar in Geodetski vestnik, vol 61 n° 4 (December 2017 - February 2018)  

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Phytosociological analysis of alpine swards and heathlands (pioneer patches) on ridges and peaks in the Julian Alps (NW Slovenia) / Igor Dakskobler in Hacquetia, vol 16 n° 1 (January 2017)  

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