Centre de documentation
scientifique

Dépouillements

GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning / Samantha T. Arundel in Transactions in GIS, Vol 24 n° 3 (June 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 556 - 572 Titre : GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning Type de document : Article/Communication Auteurs : Samantha T. Arundel, Auteur ; Wenwen Li, Auteur ; Sizhe Wang, Auteur Année de publication : 2020 Article en page(s) : pp 556 - 572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] cartographie topographique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] collecte de données [Termes IGN] détection automatique [Termes IGN] détection d'objet [Termes IGN] géobalise [Termes IGN] toponyme [Termes IGN] United States Geological Survey Résumé : (Auteur) Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain. Numéro de notice : A2020-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12633 Date de publication en ligne : 08/05/2020 En ligne : https://doi.org/10.1111/tgis.12633 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95307 [article]

Mapping areas of asynchronous‐temporal interaction in animal‐telemetry data / Brendan A. Hoover in Transactions in GIS, Vol 24 n° 3 (June 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 573 - 586 Titre : Mapping areas of asynchronous‐temporal interaction in animal‐telemetry data Type de document : Article/Communication Auteurs : Brendan A. Hoover, Auteur ; Jennifer A. Miller, Auteur ; Jed A. Long, Auteur Année de publication : 2020 Article en page(s) : pp 573 - 586 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] comportement [Termes IGN] écologie [Termes IGN] habitat animal [Termes IGN] interaction spatiale [Termes IGN] maladie animale [Termes IGN] migration animale [Termes IGN] population animale [Termes IGN] Time-geography Résumé : (Auteur) Animal interactions are a crucial aspect of behavioral ecology that affect mating, territorial behavior, resource use, and disease spread. Commonly, animals will interact because of shared resources. Recent methods have used time geography to map landscape areas where interactions were possible. However, such methods do not identify areas of less direct interaction, like through smell or sight. These indirect or asynchronous interactions are also a crucial aspect of animal behavioral ecology and affect group behaviors such as leading/following hierarchies and joint resource use. Asynchronous interactions are difficult to map because they can occur in a synchronous space at asynchronous times, as well as in asynchronous spaces at a synchronous time. Here, we present a method termed the temporally asynchronous‐joint potential path area (ta‐jPPA) that maps areas of potential temporally asynchronous–spatially synchronous interactions. We used simulated data to statistically test ta‐jPPA and empirical data to demonstrate how ta‐jPPA can find patterns in habitat use. Numéro de notice : A2020-246 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12622 Date de publication en ligne : 05/05/2020 En ligne : https://doi.org/10.1111/tgis.12622 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95308 [article]

Sketch maps for searching in spatial data / Ali Zare Zardiny in Transactions in GIS, Vol 24 n° 3 (June 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 780 - 808 Titre : Sketch maps for searching in spatial data Type de document : Article/Communication Auteurs : Ali Zare Zardiny, Auteur ; Farshad Hakimpour, Auteur ; Mozhdeh Shahbazi, Auteur Année de publication : 2020 Article en page(s) : pp 780 - 808 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] analyse des correspondances [Termes IGN] appariement de données localisées [Termes IGN] carte thématique [Termes IGN] cartographie collaborative [Termes IGN] croquis topographique [Termes IGN] modèle sémantique de données [Termes IGN] niveau d'abstraction [Termes IGN] point d'intérêt Résumé : (Auteur) Much research has been conducted on the use of sketch maps to search in spatial databases, nevertheless, they have faced challenges, such as modeling of the data abstraction level, aggregated features in sketches, modeling of semantic aspects of data, data redundancy, and evaluation of the results. Considering these challenges, in this article a new solution is presented for searching in databases based on data matching. The main difference between this solution and the other approaches lies in the parameters introduced to match data and how to solve the matching problem. Using geometrical, topological, and semantic parameters in the matching, as well as performing the matching process in the two phases of partial and global, has resulted in an of about 78%. The evaluation process is performed based on the matching parameters and the matching procedure; finally, the result is acceptable compared to previous implementations. Numéro de notice : A2020-247 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12619 Date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.1111/tgis.12619 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95312 [article]

A probabilistic framework for improving reverse geocoding output / Zhengcong Yin in Transactions in GIS, Vol 24 n° 3 (June 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 656 - 680 Titre : A probabilistic framework for improving reverse geocoding output Type de document : Article/Communication Auteurs : Zhengcong Yin, Auteur ; Daniel W. Goldberg, Auteur ; Tracy A. Hammond, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 656 - 680 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] coordonnées GPS [Termes IGN] géocodage inverse [Termes IGN] géolocalisation [Termes IGN] intégrité topologique [Termes IGN] modèle stochastique Résumé : (auteur) Reverse geocoding, which transforms machine‐readable GPS coordinates into human‐readable location information, is widely used in a variety of location‐based services and analysis. The output quality of reverse geocoding is critical because it can greatly impact these services provided to end‐users. We argue that the output of reverse geocoding should be spatially close to and topologically correct with respect to the input coordinates, contain multiple suggestions ranked by a uniform standard, and incorporate GPS uncertainties. However, existing reverse geocoding systems often fail to fulfill these aims. To further improve the reverse geocoding process, we propose a probabilistic framework that includes: (1) a new workflow that can adapt all existing address models and unitizes distance and topology relations among retrieved reference data for candidate selections; (2) an advanced scoring mechanism that quantifies characteristics of the entire workflow and orders candidates according to their likelihood of being the best candidate; and (3) a novel algorithm that derives statistical surfaces for input GPS uncertainties and propagates such uncertainties into final output lists. The efficiency of the proposed approaches is demonstrated through comparisons to the four commercial reverse geocoding systems and through human judgments. We envision that more advanced reverse geocoding output ranking algorithms specific to different application scenarios can be built upon this work. Numéro de notice : A2020-444 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12623 Date de publication en ligne : 08/05/2020 En ligne : https://doi.org/10.1111/tgis.12623 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95507 [article]

NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages / Jimin Wang in Transactions in GIS, Vol 24 n° 3 (June 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 719 - 735 Titre : NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages Type de document : Article/Communication Auteurs : Jimin Wang, Auteur ; Yingjie Hu, Auteur ; Kenneth Joseph, Auteur Année de publication : 2020 Article en page(s) : pp 719 - 735 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] catastrophe naturelle [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] flux de travaux [Termes IGN] géolocalisation [Termes IGN] précision sémantique [Termes IGN] reconnaissance de noms [Termes IGN] réseau neuronal récurrent [Termes IGN] réseau social [Termes IGN] toponyme Résumé : (auteur) Social media messages, such as tweets, are frequently used by people during natural disasters to share real‐time information and to report incidents. Within these messages, geographic locations are often described. Accurate recognition and geolocation of these locations are critical for reaching those in need. This article focuses on the first part of this process, namely recognizing locations from social media messages. While general named entity recognition tools are often used to recognize locations, their performance is limited due to the various language irregularities associated with social media text, such as informal sentence structures, inconsistent letter cases, name abbreviations, and misspellings. We present NeuroTPR, which is a Neuro‐net ToPonym Recognition model designed specifically with these linguistic irregularities in mind. Our approach extends a general bidirectional recurrent neural network model with a number of features designed to address the task of location recognition in social media messages. We also propose an automatic workflow for generating annotated data sets from Wikipedia articles for training toponym recognition models. We demonstrate NeuroTPR by applying it to three test data sets, including a Twitter data set from Hurricane Harvey, and comparing its performance with those of six baseline models. Numéro de notice : A2020-445 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12627 Date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.1111/tgis.12627 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95508 [article]