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Geographic named entity recognition by employing natural language processing and an improved BERT model / Liufeng Tao in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 12 (December 2022) . - n° 598 Titre : Geographic named entity recognition by employing natural language processing and an improved BERT model Type de document : Article/Communication Auteurs : Liufeng Tao, Auteur ; Zhong Xie, Auteur ; Dexin Xu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 598 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Chine [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] données issues des réseaux sociaux [Termes IGN] données publiques [Termes IGN] jeu de données [Termes IGN] reconnaissance de caractères [Termes IGN] reconnaissance de noms [Termes IGN] test de performance [Termes IGN] toponyme [Termes IGN] traitement du langage naturel Résumé : (auteur) Toponym recognition, or the challenge of detecting place names that have a similar referent, is involved in a number of activities connected to geographical information retrieval and geographical information sciences. This research focuses on recognizing Chinese toponyms from social media communications. While broad named entity recognition methods are frequently used to locate places, their accuracy is hampered by the many linguistic abnormalities seen in social media posts, such as informal sentence constructions, name abbreviations, and misspellings. In this study, we describe a Chinese toponym identification model based on a hybrid neural network that was created with these linguistic inconsistencies in mind. Our method adds a number of improvements to a standard bidirectional recurrent neural network model to help with location detection in social media messages. We demonstrate the results of a wide-ranging evaluation of the performance of different supervised machine learning methods, which have the natural advantage of avoiding human design features. A set of controlled experiments with four test datasets (one constructed and three public datasets) demonstrates the performance of supervised machine learning that can achieve good results on the task, significantly outperforming seven baseline models. Numéro de notice : A2022-945 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11120598 Date de publication en ligne : 28/11/2022 En ligne : https://doi.org/10.3390/ijgi11120598 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102178 [article]

Exploring the strategy goals and strategy drivers of national mapping, cadastral, and land registry authorities / Erik Hämäläinen in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 3 (March 2022) . - n° 164 Titre : Exploring the strategy goals and strategy drivers of national mapping, cadastral, and land registry authorities Type de document : Article/Communication Auteurs : Erik Hämäläinen, Auteur ; Pauliina Krigsholm, Auteur Année de publication : 2022 Article en page(s) : n° 164 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cadastre étranger [Termes IGN] base de données foncières [Termes IGN] cadastre étranger [Termes IGN] cadastre numérique [Termes IGN] cartographie cadastrale [Termes IGN] données publiques [Termes IGN] Europe (géographie politique) [Termes IGN] organisme cartographique national [Termes IGN] sécurité informatique [Termes IGN] système d'information foncière Résumé : (auteur) In many western countries, publicly led mapping activities and recording information of land parcels and buildings and the related rights, restrictions, and responsibilities have established their roles as important pillars of a functioning society. National mapping, cadastral, and land registry authorities as public agencies responsible for conducting these tasks are in a key position in shaping the development of the whole land administration sector. Most of these authorities have formulated their purposes, directions, and goals in the form of strategies. There is, however, a lack of understanding of the type of goals these authorities pursue through their strategies and why. Using an online questionnaire as a method, this study explores the strategy goals as well as the strategy drivers of national mapping, cadastral, and land registry authorities. We find that the strategy goals converge to a great extent and relate particularly to digitalization, data properties, customers and needs of society, and organizational development. Further, we observe that the strategy work of these authorities is most often driven by changes in the customer needs and by changes in the government’s policies. The contribution of the study lies in providing an overview of how national mapping, cadastral, and land registry authorities frame their near-future development and in highlighting that albeit the goals, for the most part, align with the qualities of a good, neutral land administration system, the authorities show low tendency to pursue transformative or paradigmatic changes through their strategies. Numéro de notice : A2022-183 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11030164 Date de publication en ligne : 23/02/2022 En ligne : https://doi.org/10.3390/ijgi11030164 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99884 [article]

Exploring data fusion for multi-object detection for intelligent transportation systems using deep learning / Amira Mimouna (2022)  

Public Titre : Exploring data fusion for multi-object detection for intelligent transportation systems using deep learning Type de document : Thèse/HDR Auteurs : Amira Mimouna, Auteur ; Abdelmalik Taleb-Ahmed, Directeur de thèse ; Najoua Essoukri Ben Amara, Directeur de thèse Editeur : Valenciennes : Université polytechnique Hauts-de-France Année de publication : 2022 Note générale : bibliographie Thèse de doctorat pour obtenir le grade de Docteur de l'Université polytechnique Hauts-de-France et l'INSA Hauts-de-France et l'Université de Sousse, spécialité Electronique, Acoustique et Télécommunications Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] détection d'objet [Termes IGN] données publiques [Termes IGN] entropie [Termes IGN] profil d'obstacle [Termes IGN] segmentation d'image [Termes IGN] système de transport intelligent [Termes IGN] trafic urbain [Termes IGN] transformation en ondelettes [Termes IGN] vision par ordinateur [Termes IGN] zone d'intérêt Index. décimale : THESE Thèses et HDR Résumé : (auteur) Building reliable environment perception systems is a crucial task for autonomous driving, especially in dense traffic areas. Researching in this field is evolving increasingly. However, we are at the beginning of a research pathway towards a future generation of intelligent transportation systems. In fact, challenging conditions in real-world driving circumstances, infrastructure monitoring, and accurate real-time system response, are the predominant concerns when developing such systems. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems have been mainly based on deep learning and the fusion of different modalities. In this context, firstly, we introduce OLIMP : A heterOgeneous MuLtimodal Dataset for Advanced EnvIronMent Perception . This is the first public, multimodal and synchronized dataset that includes Ultra Wide-Band (UWB) radar data, acoustic data, narrowband radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, including four categories: pedestrians, cyclists, cars and trams. The dataset presents various challenges related to dense urban traffic such as cluttered environments and differentweather conditions. To demonstrate the usefulness of the introduced dataset, we propose, afterwards, a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research. In short range settings, UWB radars represent a promising technology for building reliable obstacle detection systems as they are robust to environmental conditions. However, UWB radars suffer from a segmentation challenge: localizing relevant Regions Of Interests (ROIs) within its signals. Therefore, we put froward a segmentation approach to detect ROIs in an environment perception-dedicated UWB radar as a third contribution. Specifically, we implement a differential entropy analysis to detect ROIs. The obtained results show higher performance in terms of obstacle detection compared to state-of-theart techniques, as well as stable robustness even with low amplitude signals. Subsequently, we propose a novel framework that exploits Recurrent Neural Networks (RNNs) with UWB signals for multiple road obstacle detection as a deep learning-based approach. Features are extracted from the time-frequency domain using the discrete wavelet transform and are forwarded to the Long short-term memory (LSTM) network. The obtained results show that the LSTM-based system outperforms the other implemented related techniques in terms of obstacle detection. Note de contenu : 1- Introduction 2- Environment perception system: State of the art 3- OLIMP: A heterogeneous multimodal dataset for advanced environment perception 4- Multiple object detectors using UWB signals 5- Conclusions and perspectives Numéro de notice : 15289 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Electronique, Acoustique et Télécommunications : Université polytechnique Hauts-de-France : 2022 Organisme de stage : Institut d'électronique, de microélectronique et de nanotechnologie DOI : sans En ligne : https://hal.science/tel-03522730 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101520

Connecting family trees to construct a population-scale and longitudinal geo-social network for the U.S. / Caglar Koylu in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 12 (December 2021) . - pp 2380 - 2423 Titre : Connecting family trees to construct a population-scale and longitudinal geo-social network for the U.S. Type de document : Article/Communication Auteurs : Caglar Koylu, Auteur ; Diansheng Guo, Auteur ; Yuan Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2380 - 2423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] appariement sémantique [Termes IGN] collecte de données [Termes IGN] démographie [Termes IGN] dix-neuvième siècle [Termes IGN] données localisées des bénévoles [Termes IGN] données publiques [Termes IGN] Etats-Unis [Termes IGN] généalogie [Termes IGN] géocodage [Termes IGN] historique des données [Termes IGN] itération [Termes IGN] migration humaine [Termes IGN] mobilité humaine [Termes IGN] réseau social géodépendant [Termes IGN] système d'information historique Résumé : (auteur) We collected 92,832 user-contributed and publicly available family trees from rootsweb.com, including 250 million individuals who were born in North America and Europe between 1630 and 1930. We cleaned and connected the family trees to create a population-scale and longitudinal family tree dataset using a workflow of data collection and cleaning, geocoding, fuzzy record linkage and a relation-based iterative search for connecting trees and deduplication of records. Given the largest connected component of nearly 40 million individuals, and a total of 80 million individuals, we generated, to date, the largest population-scale and longitudinal geo-social network over centuries. We evaluated the representativeness of the family tree dataset for historical population demography and mobility by comparing the data to the 1880 Census. Our results showed that the family trees were biased towards males, the elderly, farmers, and native-born white segments of the population. Individuals were highly mobile – in our 1880 sample of parent-child pairs where both were born in the U.S., 47% were born in different states. Our findings agreed with prior studies that people migrated from East to West in horizontal bands, and the trend was reflected in the dialects and regional structure of the U.S. Numéro de notice : A2021-876 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1821885 Date de publication en ligne : 30/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1821885 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99139 [article]

Bretagne, la végétation cartographiée / Marielle Mayo in Géomètre, n° 2185 (novembre 2020)

Public [article]  inGéomètre > n° 2185 (novembre 2020) . - pp 46 - 49 Titre : Bretagne, la végétation cartographiée Type de document : Article/Communication Auteurs : Marielle Mayo, Auteur Année de publication : 2020 Article en page(s) : pp 46 - 49 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] 1:25.000 [Termes IGN] acquisition d'images [Termes IGN] aménagement régional [Termes IGN] ArcGIS [Termes IGN] BD ortho [Termes IGN] Bretagne [Termes IGN] carte de la végétation [Termes IGN] classification orientée objet [Termes IGN] données localisées [Termes IGN] données publiques [Termes IGN] IGN cité [Termes IGN] image infrarouge couleur [Termes IGN] image proche infrarouge [Termes IGN] modèle orienté objet [Termes IGN] phytoécologie Résumé : (Auteur) Une cartographie inédite de la végétation de Bretagne sera accessible en totalité en ligne en décembre. Produite par télédétection grâce à une méthode semi-automatisée innovante, elle répond aux nouveaux besoins des acteurs de la biodiversité et de l'aménagement du territoire. Numéro de notice : A2020-707 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96281 [article]

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