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Automatic and accurate extraction of road intersections from raster maps / Yao-Yi Chiang in Geoinformatica, vol 13 n° 2 (June 2009)  

Public [article]  inGeoinformatica > vol 13 n° 2 (June 2009) . - pp 121 - 157 Titre : Automatic and accurate extraction of road intersections from raster maps Type de document : Article/Communication Auteurs : Yao-Yi Chiang, Auteur ; C. Shahabi, Auteur ; Craig A. Knoblock, Auteur ; C.C. Chen, Auteur Année de publication : 2009 Article en page(s) : pp 121 - 157 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] carrefour [Termes IGN] carte numérique [Termes IGN] conflation [Termes IGN] connexité (topologie) [Termes IGN] données maillées [Termes IGN] extraction automatique [Termes IGN] extraction du réseau routier [Termes IGN] intégration de données [Termes IGN] réseau routier Résumé : (Auteur) Since maps are widely available for many areas around the globe, they provide a valuable resource to help understand other geospatial sources such as to identify roads or to annotate buildings in imagery. To utilize the maps for understanding other geospatial sources, one of the most valuable types of information we need from the map is the road network, because the roads are common features used across different geospatial data sets. Specifically, the set of road intersections of the map provides key information about the road network, which includes the location of the road junctions, the number of roads that meet at the intersections (i.e., connectivity), and the orientations of these roads. The set of road intersections helps to identify roads on imagery by serving as initial seed templates to locate road pixels. Moreover, a conflation system can use the road intersections as reference features (i.e., control point set) to align the map with other geospatial sources, such as aerial imagery or vector data. In this paper, we present a framework for automatically and accurately extracting road intersections from raster maps. Identifying the road intersections is difficult because raster maps typically contain much information such as roads, symbols, characters, or even contour lines. We combine a variety of image processing and graphics recognition methods to automatically separate roads from the raster map and then extract the road intersections. The extracted information includes a set of road intersection positions, the road connectivity, and road orientations. For the problem of road intersection extraction, our approach achieves over 95% precision (correctness) with over 75% recall (completeness) on average on a set of 70 raster maps from a variety of sources. Copyright Springer Numéro de notice : A2009-073 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-008-0046-3 En ligne : https://doi.org/10.1007/s10707-008-0046-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29703 [article]

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057-09021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Continuous K-nearest neighbor query for moving objects with uncertain velocity / Y. Huang in Geoinformatica, vol 13 n° 1 (March 2009)  

Public [article]  inGeoinformatica > vol 13 n° 1 (March 2009) . - pp 1 - 25 Titre : Continuous K-nearest neighbor query for moving objects with uncertain velocity Type de document : Article/Communication Auteurs : Y. Huang, Auteur ; C.C. Chen, Auteur ; C. Lee, Auteur Année de publication : 2009 Article en page(s) : pp 1 - 25 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données spatiotemporelles [Termes IGN] classification barycentrique [Termes IGN] objet mobile [Termes IGN] requête spatiale [Termes IGN] requête spatiotemporelle [Termes IGN] vitesse Résumé : (Auteur) One of the most important queries in spatio-temporal databases that aim at managing moving objects efficiently is the continuous K-nearest neighbor (CKNN) query. A CKNN query is to retrieve the K-nearest neighbors (KNNs) of a moving user at each time instant within a user-given time interval [t s , t e ]. In this paper, we investigate how to process a CKNN query efficiently. Different from the previous related works, our work relieves the past assumption, that an object moves with a fixed velocity, by allowing that the velocity of the object can vary within a known range. Due to the introduction of this uncertainty on the velocity of each object, processing a CKNN query becomes much more complicated. We will discuss the complications incurred by this uncertainty and propose a cost-effective P2 KNN algorithm to find the objects that could be the KNNs at each time instant within the given query time interval. Besides, a probability-based model is designed to quantify the possibility of each object being one of the KNNs. Comprehensive experiments demonstrate the efficiency and the effectiveness of the proposed approach. Copyright Springer Numéro de notice : A2009-003 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-007-0041-0 En ligne : https://doi.org/10.1007/s10707-007-0041-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29633 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-09011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automatically and accurately conflating raster maps with orthoimagery / C.C. Chen in Geoinformatica, vol 12 n° 3 (September - November 2008)  

Public [article]  inGeoinformatica > vol 12 n° 3 (September - November 2008) . - pp 377 - 410 Titre : Automatically and accurately conflating raster maps with orthoimagery Type de document : Article/Communication Auteurs : C.C. Chen, Auteur ; Craig A. Knoblock, Auteur ; C. Shahabi, Auteur Année de publication : 2008 Article en page(s) : pp 377 - 410 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] appariement de points [Termes IGN] carrefour [Termes IGN] carte thématique [Termes IGN] conflation [Termes IGN] données maillées [Termes IGN] données vectorielles [Termes IGN] orthoimage [Termes IGN] réseau routier [Termes IGN] transformation élastique Résumé : (Auteur) Recent growth of geospatial information online has made it possible to access various maps and orthoimagery. Conflating these maps and imagery can create images that combine the visual appeal of imagery with the attribution information from maps. The existing systems require human intervention to conflate maps with imagery. We present a novel approach that utilizes vector datasets as “glue” to automatically conflate street maps with imagery. First, our approach extracts road intersections from imagery and maps as control points. Then, it aligns the two point sets by computing the matched point pattern. Finally, it aligns maps with imagery based on the matched pattern. The experiments show that our approach can conflate various maps with imagery, such that in our experiments on TIGER-maps covering part of St. Louis county, MO, 85.2% of the conflated map roads are within 10.8 m from the actual roads compared to 51.7% for the original and georeferenced TIGER-map roads. Copyright Springer Numéro de notice : A2008-285 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-007-0033-0 En ligne : https://doi.org/10.1007/s10707-007-0033-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29278 [article]

Exemplaires(1)

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057-08031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automatically conflating road vector data with orthoimagery / C.C. Chen in Geoinformatica, vol 10 n° 4 (December 2006)  

Public [article]  inGeoinformatica > vol 10 n° 4 (December 2006) . - pp 495 - 530 Titre : Automatically conflating road vector data with orthoimagery Type de document : Article/Communication Auteurs : C.C. Chen, Auteur ; Craig A. Knoblock, Auteur ; C. Shabani, Auteur Année de publication : 2006 Article en page(s) : pp 495 - 530 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] appariement de modèles conceptuels de données [Termes IGN] automatisation [Termes IGN] Californie (Etats-Unis) [Termes IGN] cohérence des données [Termes IGN] conflation [Termes IGN] données multisources [Termes IGN] données vectorielles [Termes IGN] fusion de données multisource [Termes IGN] intégration de données [Termes IGN] Montana (Etats-Unis) [Termes IGN] orthoimage [Termes IGN] point d'appui [Termes IGN] réseau routier Résumé : (Auteur) Recent growth of the geospatial information on the web has made it possible to easily access a wide variety of spatial data. The ability to combine various sets of geospatial data into a single composite dataset has been one of central issues of modern geographic information processing. By conflating diverse spatial datasets, one can support a rich set of queries that could have not been answered given any of these sets in isolation. However, automatically conflating geospatial data from different data sources remains a challenging task. This is because geospatial data obtained from various data sources may have different projections, different accuracy levels and different formats (e.g., raster or vector format), thus resulting in various positional inconsistencies. Most of the existing algorithms only deal with vector to vector data conflation or require human intervention to accomplish vector data to imagery conflation. In this paper, we describe a novel geospatial data fusion approach, named AMS-Conflation, which achieves automatic vector to imagery conflation. We describe an efficient technique to automatically generate control point pairs from the orthoimagery and vector data by exploiting the information from the vector data to perform localized image processing on the orthoimagery. We also evaluate a filtering technique to automatically eliminate inaccurate pairs from the generated control points. We show that these conflation techniques can automatically align the roads in orthoimagery, such that 75% of the conflated roads are within 3.6 meters from the real road axes compared to 35% for the original vector data for partial areas of the county of St. Louis, MO. Copyright Springer Numéro de notice : A2006-549 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-006-0344-6 En ligne : https://doi.org/10.1007/s10707-006-0344-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28272 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-06041	RAB	Revue	Centre de documentation	En réserve L003	Disponible