Centre dedocumentation
scientifique

Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners / Artu Ellmann in Survey review, vol 54 n° 385 (July 2022)  

Public [article]  inSurvey review > vol 54 n° 385 (July 2022) . - pp 363 - 374 Titre : Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners Type de document : Article/Communication Auteurs : Artu Ellmann, Auteur ; Kaia Kütimets, Auteur ; Sander Varbla, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 363 - 374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arpentage [Termes IGN] cartographie et localisation simultanées [Termes IGN] données lidar [Termes IGN] Estonie [Termes IGN] géoréférencement [Termes IGN] industrie minière [Termes IGN] mine [Termes IGN] modélisation 3D [Termes IGN] schiste [Termes IGN] semis de points [Termes IGN] système de numérisation mobile [Termes IGN] télémètre laser terrestre Résumé : (auteur) Applicability of SLAM (simultaneous localization and mapping) technology for mine surveys and subsequent 3D modelling of post-extracted surfaces is assessed. The resulting surface geometry is validated via terrestrial laser scanner (TLS) acquired reference data. Typical discrepancies remained within 2 and 5 cm in horizontal and vertical directions, respectively. Discrepancies between TLS, SLAM-enabled handheld scanner and conventional surveying results are small and fully satisfy the contemporary accuracy requirements, yet evidence that the conventional mine survey results are affected by the subjectivity of the surveyors. The SLAM-enabled laser scanning hence appears to be the most suitable method for underground mining surveys. Numéro de notice : A2022-537 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1944545 Date de publication en ligne : 07/07/2021 En ligne : https://doi.org/10.1080/00396265.2021.1944545 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101093 [article]

Polyline simplification based on the artificial neural network with constraints of generalization knowledge / Jiawei Du in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)  

Public [article]  inCartography and Geographic Information Science > Vol 49 n° 4 (July 2022) . - pp 313 - 337 Titre : Polyline simplification based on the artificial neural network with constraints of generalization knowledge Type de document : Article/Communication Auteurs : Jiawei Du, Auteur ; Jichong Yin, Auteur ; Chengyi Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 313 - 337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] descripteur [Termes IGN] données maillées [Termes IGN] données vectorielles [Termes IGN] généralisation cartographique automatisée [Termes IGN] polyligne [Termes IGN] programmation par contraintes [Termes IGN] réseau neuronal artificiel [Termes IGN] simplification de contour [Vedettes matières IGN] Généralisation Résumé : (auteur) The present paper presents techniques for polyline simplification based on an artificial neural network within the constraints of generalization knowledge. The proposed method measures polyline shape characteristics that influence polyline simplification using abstracted descriptors and then introduces these descriptors into the artificial neural network as input properties. In total, 18 descriptors categorized into three types are presented in detail. In a second approach, map simplification principles are abstracted as controllers, imposed after the output layer of the trained artificial neural network to make the polyline simplification comply with these principles. This study worked with three controllers – a basic controller and two knowledge-based controllers. These descriptors and controllers abstracted from generalization knowledge were tested in experiments to determine their efficacy in polyline simplification based on the artificial neural network. The experimental results show that the utilization of abstracted descriptors and controllers can constrain the artificial neural network-based polyline simplification according to polyline shape characteristics and simplification principles. Numéro de notice : A2022-479 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : https://doi.org/10.1080/15230406.2021.2013944 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.1080/15230406.2021.2013944 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100885 [article]

An informal road detection neural network for societal impact in developing countries / Inger Fabris-Rotelli in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2022 (2022 edition) . - pp 267 - 274 Titre : An informal road detection neural network for societal impact in developing countries Type de document : Article/Communication Auteurs : Inger Fabris-Rotelli, Auteur ; Abraham Wannenburg, Auteur ; Gao Maribe, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 267 - 274 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] Afrique du sud (état) [Termes IGN] apprentissage profond [Termes IGN] données étiquetées d'entrainement [Termes IGN] extraction du réseau routier [Termes IGN] image satellite [Termes IGN] impact social [Termes IGN] pays en développement [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Roads found in informal settlements arise out of convenience, and are often not recorded or maintained by authorities. This complicates service delivery, sustainable development and crisis mitigation, including management and tracking of COVID-19. We, therefore, aim to extract informal roads in remote sensing images. Existing techniques aiming at the extraction of formal roads are not suitable for the problem due to the complex physical and spectral properties of informal roads. The only existing approaches for informal roads, namely (Nobrega et al., 2006, Thiede et al., 2020), do not consider neural networks as a solution. Neural networks show promise in overcoming these complexities. However, they require a large amount of data to learn, which is currently not available due to the expensive and time-consuming nature of collecting such data. This paper implements a neural network to extract informal roads from a data set digitised by this research group. Data quality is assessed by calculating validity completeness, homogeneity and the V-measure, a measure of consistency, in order to evaluate the overall usability of the dataset for neural network informal road detection. We implement the GANs-UNet model that obtained the highest F1-score in a 2020 review paper (Abdollahi et al., 2020) on the state-of-the-art deep learning models used to extract formal roads. The results indicate that the model is able to extract informal roads successfully in the presence of appropriate training data. Numéro de notice : A2022-424 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-4-2022-267-2022 Date de publication en ligne : 18/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2022-267-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100729 [article]

Dater les documents cartographiques / Jean-Luc Arnaud in e-Perimetron, vol 17 n° 1 (avril 2022)  

Public [article]  ine-Perimetron > vol 17 n° 1 (avril 2022) . - pp 1 - 18 Titre : Dater les documents cartographiques Type de document : Article/Communication Auteurs : Jean-Luc Arnaud, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 18 Note générale : bibliographie Langues : Français (fre) Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie ancienne [Termes IGN] cartographie militaire [Termes IGN] datation [Termes IGN] dépôt de la guerre [Termes IGN] document cartographique [Termes IGN] échelle cartographique [Termes IGN] édition cartographique [Termes IGN] histoire [Termes IGN] précision [Termes IGN] Service Géographique de l'Armée [Termes IGN] utilisateur Résumé : (auteur) Cet article examine les modes de datation de la production cartographique française depuis la fin du XVIIIe siècle. Il est composé de sept chapitres thématiques qui envisagent la multiplicité des pratiques des éditeurs et montrent qu’en fonction de l’usage envisagé pour chaque document et de son niveau de précision, les enjeux portés par la datation prennent des formes différentes. Numéro de notice : A2022-294 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : sans En ligne : http://www.e-perimetron.org/Vol_17_1/Arnaud.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100346 [article]

Detecting individuals' spatial familiarity with urban environments using eye movement data / Hua Liao in Computers, Environment and Urban Systems, vol 93 (April 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101758 Titre : Detecting individuals' spatial familiarity with urban environments using eye movement data Type de document : Article/Communication Auteurs : Hua Liao, Auteur ; Wendi Zhao, Auteur ; Changbo Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse visuelle [Termes IGN] apprentissage automatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] navigation pédestre [Termes IGN] oculométrie [Termes IGN] service fondé sur la position [Termes IGN] zone urbaine [Vedettes matières IGN] Géovisualisation Résumé : (auteur) The spatial familiarity of environments is an important high-level user context for location-based services (LBS). Knowing users' familiarity level of environments is helpful for enabling context-aware LBS that can automatically adapt information services according to users' familiarity with the environment. Unlike state-of-the-art studies that used questionnaires, sketch maps, mobile phone positioning (GPS) data, and social media data to measure spatial familiarity, this study explored the potential of a new type of sensory data - eye movement data - to infer users' spatial familiarity of environments using a machine learning approach. We collected 38 participants' eye movement data when they were performing map-based navigation tasks in familiar and unfamiliar urban environments. We trained and cross-validated a random forest classifier to infer whether the users were familiar or unfamiliar with the environments (i.e., binary classification). By combining basic statistical features and fixation semantic features, we achieved a best accuracy of 81% in a 10-fold classification and 70% in the leave-one-task-out (LOTO) classification. We found that the pupil diameter, fixation dispersion, saccade duration, fixation count and duration on the map were the most important features for detecting users' spatial familiarity. Our results indicate that detecting users' spatial familiarity from eye tracking data is feasible in map-based navigation and only a few seconds (e.g., 5 s) of eye movement data is sufficient for such detection. These results could be used to develop context-aware LBS that adapt their services to users' familiarity with the environments. Numéro de notice : A2022-121 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101758 Date de publication en ligne : 21/01/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101758 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99663 [article]

Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation / Yingjie Hu in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

Permalink

Graph neural network based model for multi-behavior session-based recommendation / Bo Yu in Geoinformatica [en ligne], vol 26 n° 2 (April 2022)  

Permalink

Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea / Yang Xu in Computers, Environment and Urban Systems, vol 92 (March 2022)  

Permalink

Pourquoi la forêt française a besoin d’un traitement de fond / Guillaume Decocq in The Conversation France, vol 2022 ([10/02/2022])  

Permalink

Quickly locating POIs in large datasets from descriptions based on improved address matching and compact qualitative representations / Ruozhen Cheng in Transactions in GIS, vol 26 n° 1 (February 2022)  

Permalink

ATLANTIS : Une ontologie pour représenter les Instructions nautiques / Helen Mair Rawsthorne (2022)  

Permalink

Atlas of global change risk of population and economic systems / Peijun Shi (2022)  

Permalink

Automatic identification of addresses: A systematic literature review / Paula Cruz in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

Permalink

Contextual location recommendation for location-based social networks by learning user intentions and contextual triggers / Seyyed Mohammadreza Rahimi in Geoinformatica [en ligne], vol 26 n° 1 (January 2022)  

Permalink

Cross-dataset learning for generalizable land use scene classification / Dimitri Gominski (2022)  

Permalink