Centre de documentation
scientifique

Dépouillements

Automatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)  

Public [article]  inGeodetski vestnik > vol 64 n° 4 (December 2020 - February 2021) . - pp 545 - 561 Titre : Automatic building footprint extraction from UAV images using neural networks Type de document : Article/Communication Auteurs : Zoran Kokeza, Auteur ; Miroslav Vujasinović, Auteur ; Miro Govedarica, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 545 - 561 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] cartographie cadastrale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection du bâti [Termes IGN] empreinte [Termes IGN] image à haute résolution [Termes IGN] image captée par drone [Termes IGN] image RVB [Termes IGN] modèle numérique de surface [Termes IGN] orthoimage [Termes IGN] zone d'intérêt Résumé : (Auteur) Up-to-date cadastral maps are crucial for urban planning. Creating those maps with the classical geodetic methods is expensive and time-consuming. Emerge of Unmanned Aerial Vehicles (UAV) made a possibility for quick acquisition of data with much more details than it was possible before. The topic of the research refers to the challenges of automatic extraction of building footprints on high-resolution orthophotos. The objectives of this study were as follows: (1) to test the possibility of using different publicly available datasets (Tanzania, AIRS and Inria) for neural network training and then test the generalisation capability of the model on the Area Of Interest (AOI); (2) to evaluate the effect of the normalised digital surface model (nDSM) on the results of neural network training and implementation. Evaluation of the results shown that the models trained on the Tanzania (IoU 36.4%), AIRS (IoU 64.4%) and Inria (IoU 7.4%) datasets doesn't satisfy the requested accuracy to update cadastral maps in study area. Much better results are achieved in the second part of the study, where the training of the neural network was done on tiles (256x256) of the orthophoto of AOI created from data acquired using UAV. A combination of RGB orthophoto with nDSM resulted in a 2% increase of IoU, achieving the final IoU of over 90%. Numéro de notice : A2020-777 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2020.04.545-561 Date de publication en ligne : 26/10/2020 En ligne : http://www.geodetski-vestnik.com/en/2020-4 Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96706 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2020041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Intercomparisons of precipitable water vapour derived from radiosonde, GPS and sunphotometer observations / Shaoqi Gong in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)  

Public [article]  inGeodetski vestnik > vol 64 n° 4 (December 2020 - February 2021) . - pp 562 - 577 Titre : Intercomparisons of precipitable water vapour derived from radiosonde, GPS and sunphotometer observations Type de document : Article/Communication Auteurs : Shaoqi Gong, Auteur ; Wenqin Chen, Auteur ; Cunjie Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 562 - 577 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] analyse comparative [Termes IGN] atmosphère terrestre [Termes IGN] coefficient de corrélation [Termes IGN] photomètre [Termes IGN] photométrie [Termes IGN] positionnement par GNSS [Termes IGN] précipitation [Termes IGN] radiosondage [Termes IGN] station d'observation [Termes IGN] valeur aberrante [Termes IGN] vapeur d'eau Résumé : (Auteur) The atmospheric precipitable water vapour (PWV) plays a crucial role in the hydrological cycle and energy transfer on a global scale. Radiosonde (RS), sunphotometer (SP) and GPS (as well as broader GNSS) receivers have gradually been the principal instruments for ground-based PWV observation. This study first co-locates the observation stations configured the three instruments in the globe and in three typical latitudinal climatic regions respectively, then the PWV data from the three instruments are matched each other according to the observing times. After the outliers are removed from the matched data pairs, the PWV intercomparisons for any two instruments are performed. The results show that the PWV estimates from any two instruments have a good agreement with very high correlation coefficients. The latitude and climate have no significant influence on the PWV measurements from the three instruments, indicating that the instruments are very stable and depend on their performance. The PWV differences of any two instruments display the normal distribution, indicating non-systematic biases among the two PWV datasets. The relative differences between SP and GPS are the smallest, the middle between SP and RS, and those between GPS and RS are the largest. This study will be useful to promote GPS (GNSS) and SP PWV to be a substitute for RS PWV as a benchmark because of their high temporal resolutions. Numéro de notice : A2020-778 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2020.04.562-577 En ligne : http://www.geodetski-vestnik.com/en/2020-4 Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96709 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2020041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Possibility to determine highly precise geoid for Egypt territory / Moamen Awad Habib Gad in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)  

Public [article]  inGeodetski vestnik > vol 64 n° 4 (December 2020 - February 2021) . - pp 578-593 Titre : Possibility to determine highly precise geoid for Egypt territory Type de document : Article/Communication Auteurs : Moamen Awad Habib Gad, Auteur ; Oleg Odalovic, Auteur ; Sofija Naod, Auteur Année de publication : 2020 Article en page(s) : pp 578-593 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] champ de pesanteur terrestre [Termes IGN] collocation par moindres carrés [Termes IGN] Egypte [Termes IGN] géoïde local [Termes IGN] modèle de géopotentiel local [Termes IGN] point d'appui [Termes IGN] précision centimétrique Résumé : (Auteur) This paper presents an attempt to consider whether it is possible to determine a geoid at the centimetre level in the territory of Egypt based on recently available global and local gravity field data. The paper has two main objectives. Firstly, the paper overviews previously published geoid solutions, while the second objective investigates the performance of the recent global geopotential models (GGM) in Egypt. The existing geoid solutions have illustrated that there is an insufficient distribution of data which is sampled inconsistently. At this time, data deficiency still exists, and to overcome it, we have selected a "data window" and applied the Least Square Collocation (LSC) technique. The outcome from LSC was interesting and acceptable, and we obtained a "sample" geoid that has a standard deviation of 11 cm for the external control points. Numéro de notice : A2020-779 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2020.04.578-593 En ligne : http://www.geodetski-vestnik.com/en/2020-4 Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96715 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2020041	RAB	Revue	Centre de documentation	En réserve L003	Disponible