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Investigating the quality of reverse geocoding services using text similarity techniques and logistic regression analysis / Batuhan Kilic in Cartography and Geographic Information Science, Vol 47 n° 4 (July 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 4 (July 2020) . - pp 336 - 349 Titre : Investigating the quality of reverse geocoding services using text similarity techniques and logistic regression analysis Type de document : Article/Communication Auteurs : Batuhan Kilic, Auteur ; Fatih Gülgen, Auteur Année de publication : 2020 Article en page(s) : pp 336 - 349 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] exploration de texte [Termes descripteurs IGN] géocodage inverse [Termes descripteurs IGN] géocodage par adresse postale [Termes descripteurs IGN] logique binaire [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] régression [Termes descripteurs IGN] similitude sémantique Résumé : (auteur) Location, usually defined by postal address information or geographic coordinate values, is one of the leading themes in geography. Famous global mapping services such as ArcGIS Online, Bing Maps, Google Maps, or Yandex Maps can provide users with address information of any geographic coordinates using reverse geocoding. The accuracy of retrieved addresses is quite essential for a service user. Several researchers have evaluated the accuracy of the process based on the positional errors between the retrieved and actual addresses. This article proposes a different assessment based on text similarity algorithms. In this study, the authors examine the outcomes of 15 different text similarity algorithms by comparing them with the reference data. They benefit from the binary logistic regression to evaluate the results. At the end of the case study, they conclude that the soft-term frequency/inverse document frequency algorithm is the most appropriate to measure the quality of postal addresses of all tested services. The Jaccard algorithm also produces successful results only for Google and Bing Maps services. Moreover, the study allows the reader to assess the results of reverse geocoding derived from the global map platforms that serve in the test region. Numéro de notice : A2020-339 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1746198 date de publication en ligne : 20/04/2020 En ligne : https://doi.org/10.1080/15230406.2020.1746198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95214 [article]

Reestimating a minimum acceptable geocoding hit rate for conducting a spatial analysis / Alvaro Briz-Redon in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 7 (July 2020) . - pp 1283 - 1305 Titre : Reestimating a minimum acceptable geocoding hit rate for conducting a spatial analysis Type de document : Article/Communication Auteurs : Alvaro Briz-Redon, Auteur ; Francisco Martinez-Ruiz, Auteur ; Francisco Montes, Auteur Année de publication : 2020 Article en page(s) : pp 1283 - 1305 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] appariement automatique [Termes descripteurs IGN] criminalité [Termes descripteurs IGN] géocodage [Termes descripteurs IGN] géopositionnement [Termes descripteurs IGN] infraction [Termes descripteurs IGN] simulation [Termes descripteurs IGN] taux d'échantillonnage Résumé : (auteur) Geocoding consists in converting a textual description of a location into coordinates. Hence, geocoding a dataset of events has to be carried out before performing a spatial analysis of some data. Automated procedures are necessary to geocode large datasets of events, but they can produce errors. Therefore, it is natural to ask oneself what is the minimum percentage of events that should be geocoded. An 85% of success was established 15 years ago as the first estimate of a minimum acceptable rate, becoming a reference for many spatial analysts. In this paper, the goal is reestimating a minimum acceptable geocoding match rate through the same procedure that was employed for computing the first estimate while accounting for some spatial factors that could possibly influence this estimation: intensity, clustering and aggregation levels. Several statistical techniques and the presence of nonrandomly distributed errors are also explored in this context. The results indicate that variations in intensity, clustering and aggregation levels lead to different minimum acceptable geocoding match rates. Furthermore, specific techniques such as cluster detection seem to be especially sensitive to the existence of non-geocoded data. Therefore, the highly approved 85% geocoding rate may need to be raised. Numéro de notice : A2020-303 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1703994 date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1080/13658816.2019.1703994 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95143 [article]

How much do we learn from addresses? On the syntax, semantics and pragmatics of addressing systems / Ali Javidaneh in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 27 p. Titre : How much do we learn from addresses? On the syntax, semantics and pragmatics of addressing systems Type de document : Article/Communication Auteurs : Ali Javidaneh, Auteur ; Farid Karimipour, Auteur ; Negar Alinaghi, Auteur Année de publication : 2020 Article en page(s) : 27 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] adresse postale [Termes descripteurs IGN] analyse sémantique [Termes descripteurs IGN] appariement d'adresses [Termes descripteurs IGN] cognition [Termes descripteurs IGN] géocodage par adresse postale [Termes descripteurs IGN] modèle orienté agent [Termes descripteurs IGN] représentation mentale spatiale [Termes descripteurs IGN] structure syntaxique Résumé : (auteur) An address is a specification that refers to a unique location on Earth. While there has been a considerable amount of research on the syntactic structure of addressing systems in order to evaluate and improve their quality, aspects of semantics and pragmatics have been less explored. An address is primarily associated by humans to the elements of their spatial mental representations, but may also influence their spatial knowledge and activities through the level of detail it provides. Therefore, it is not only important how addressing components are structured, but it is also of interest to study their meaning as well as the pragmatics in relation to an interpreting agent. This article studies three forms of addresses (i.e., structured as in Austria, semi-formal as in Japan, and descriptive as in Iran) under the principles of semiotics (i.e., through levels of syntax, semantics, and pragmatics). Syntax is discussed through formal definitions of the addressing systems, while semantics and pragmatics are assessed through an agent-based model to explore how they influence spatial knowledge acquisition and growth. Numéro de notice : A2020-302 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050317 date de publication en ligne : 11/05/2020 En ligne : https://doi.org/10.3390/ijgi9050317 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95142 [article]

Geocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 125 - 136 Titre : Geocoding of trees from street addresses and street-level images Type de document : Article/Communication Auteurs : Daniel Laumer, Auteur ; Nico Lang, Auteur ; Natalie Van Doorn, Auteur Année de publication : 2020 Article en page(s) : pp 125 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse des correspondances [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] géocodage par adresse postale [Termes descripteurs IGN] image panoramique [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] inventaire [Termes descripteurs IGN] service écosystémique [Termes descripteurs IGN] zone urbaine Résumé : (auteur) We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous appearance of different tree instances in the images, ambiguous tree positions if viewed from multiple images and occlusions. To solve this assignment problem, we (i) detect trees in Google street-view panoramas using deep learning, (ii) combine multi-view detections per tree into a single representation, (iii) and match detected trees with given trees per street address with a global optimization approach. Experiments for trees in 5 cities in California, USA, show that we are able to assign geographic coordinates to 38% of the street trees, which is a good starting point for long-term studies on the ecosystem services value of street trees at large scale. Numéro de notice : A2020-124 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.001 date de publication en ligne : 21/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94749 [article]

A deep learning architecture for semantic address matching / Yue Lin in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 3 (March 2020) . - pp 559 - 576 Titre : A deep learning architecture for semantic address matching Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Mengjun Kang, Auteur ; Yuyang Wu, Auteur Année de publication : 2020 Article en page(s) : pp 559 - 576 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] appariement d'adresses [Termes descripteurs IGN] appariement sémantique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] géocodage par adresse postale [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] inférence sémantique [Termes descripteurs IGN] repésentation vectorielle [Termes descripteurs IGN] réseau de neurones profond [Termes descripteurs IGN] Shenzhen [Termes descripteurs IGN] similitude sémantique [Termes descripteurs IGN] traitement du langage naturel Résumé : (auteur) Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a large amount of unstructured address data. Traditional address matching methods mainly focus on the literal similarity of address records and are therefore not applicable to the unstructured address data. In this study, we introduce an address matching method based on deep learning to identify the semantic similarity between address records. First, we train the word2vec model to transform the address records into their corresponding vector representations. Next, we apply the enhanced sequential inference model (ESIM), a deep text-matching model, to make local and global inferences to determine if two addresses match. To evaluate the accuracy of the proposed method, we fine-tune the model with real-world address data from the Shenzhen Address Database and compare the outputs with those of several popular address matching methods. The results indicate that the proposed method achieves a higher matching accuracy for unstructured address records, with its precision, recall, and F1 score (i.e., the harmonic mean of precision and recall) reaching 0.97 on the test set. Numéro de notice : A2020-106 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1681431 date de publication en ligne : 24/10/2019 En ligne : https://doi.org/10.1080/13658816.2019.1681431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94702 [article]

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