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Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data / Xiuyuan Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 1 - 12 Titre : Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data Type de document : Article/Communication Auteurs : Xiuyuan Zhang, Auteur ; Shihong Du, Auteur ; Zhijia Zheng, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 12 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] apprentissage semi-dirigé [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] Chine [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] scène urbaine Résumé : (Auteur) Urban functional zones are basic units of urban planning and resource allocation, and contribute to a wide range of urban studies and investigations. Existing studies on functional-zone mapping with very-high-resolution (VHR) satellite images focused much on feature representations and classification techniques, but ignored zone sampling which however was fundamental to automatic zone classifications. Functional-zone sampling is much complicated and can hardly be resolved by classical sampling methods, as functional zones are complex urban scenes which consist of heterogeneous land covers and have highly abstract categories. To resolve the issue, this study presents a novel sampling paradigm, i.e., heuristic sample learning (HSL). It first proposes a sparse topic model to select representative functional zones, then uses deep forest to select confusing zones, and finally embraces Chinese restaurant process to label these selected zones. The presented method collects both representative and confusing zone samples and identifies their categories accurately, which makes the functional-zone classification process robust and the classification results accurate. Experiments conducted in Beijing indicate that HSL is effective and efficient for functional-zone sampling and classifications. Compared to traditional manual sampling, HSL reduces the time cost by 55% and improves the classification accuracy by 11.3% on average; furthermore, HSL can reduce the variation in sampling and classification results caused by different proficiency of operators. Accordingly, HSL significantly contributes to functional-zone mapping and plays an important role in urban studies. Numéro de notice : A2020-061 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.005 date de publication en ligne : 13/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94577 [article]

Urban impervious surface estimation from remote sensing and social data / Yan Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 12 (December 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 12 (December 2018) . - pp 771 - 780 Titre : Urban impervious surface estimation from remote sensing and social data Type de document : Article/Communication Auteurs : Yan Yu, Auteur ; Jun Li, Auteur ; Changyu Zhu, Auteur ; Antonio J. Plaza, Auteur Année de publication : 2018 Article en page(s) : pp 771 - 780 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] base de données routières [Termes descripteurs IGN] Canton (Kouangtoung) [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] Google Maps [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] régression multiple [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] surface imperméable [Termes descripteurs IGN] zone urbaine Résumé : (auteur) We propose an inspiring approach for accurate impervious surface estimation based on the integration of remote sensing and social data. The proposed approach exploits the strengths of two kind of heterogeneous features, i.e., physical features and social features, where the former ones are derived by a morphological attribute profiles-guided spectral mixture analysis model using remote sensing imagery, and the latter ones are obtained from the normalized kernel density of point of interest and vector road datasets. These two features are then integrated using a multivariable linear regression model to estimate impervious surfaces. The proposed method has been tested in the main urban area of Guangzhou, China, in pixel level and parcel level, respectively. The obtained results, with the overall RMSE of 10.98% and 10.90% for pixel level and parcel level, respectively, demonstrate the good performance of integrating remote sensing imagery and social data for mapping of urban impervious surface. Numéro de notice : A2018-549 Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.12.771 date de publication en ligne : 01/12/2018 En ligne : https://doi.org/10.14358/PERS.84.12.771 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91622 [article]

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A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks / Lin Wan in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 11-12 (November - December 2018) . - pp 2225 - 2246 Titre : A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks Type de document : Article/Communication Auteurs : Lin Wan, Auteur ; Yuming Hong, Auteur ; Zhou Huang, Auteur ; Xia Peng, Auteur ; Ran Li, Auteur Année de publication : 2018 Article en page(s) : pp 2225 - 2246 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] calcul d'itinéraire [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] exploration de données géographiques [Termes descripteurs IGN] image Flickr [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] trajectoire Résumé : (Auteur) Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users’ location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user’s personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user’s latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns. Numéro de notice : A2018-523 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1458988 date de publication en ligne : 03/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1458988 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91348 [article]

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Aggregate keyword nearest neighbor queries on road networks / Pengfei Zhang in Geoinformatica [en ligne], vol 22 n° 2 (April 2018)  

Public [article]  inGeoinformatica [en ligne] > vol 22 n° 2 (April 2018) . - pp 237 - 268 Titre : Aggregate keyword nearest neighbor queries on road networks Type de document : Article/Communication Auteurs : Pengfei Zhang, Auteur ; Huaizhong Lin, Auteur ; Yunjun Gao, Auteur ; Dongming Lu, Auteur Année de publication : 2018 Article en page(s) : pp 237 - 268 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes descripteurs IGN] langage naturel (informatique) [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] requête spatiale [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] système d'information géographique Résumé : (Auteur) Given a group Q of query points and a set P of points of interest (POIs), aggregate nearest neighbor (ANN) queries find a POI p from P that achieves the smallest aggregate distance. Specifically, the aggregate distance is defined over the set of distances between p and all query points in Q$\mathcal {Q}$. Existing studies on ANN query mainly consider the spatial proximity, whereas the textual similarity has received considerable attention recently. In this work, we utilize user-specified query keywords to capture textual similarity. We study the aggregate keyword nearest neighbor (AKNN) queries, finding the POI that has the smallest aggregate distance and covers all query keywords. Nevertheless, existing methods on ANN query are either inapplicable or inefficient when applied to the AKNN query. To answer our query efficiently, we first develop a dual-granularity (DG) indexing schema. It preserves abstracts of the road network by a tree structure, and preserves detailed network information by an extended adjacency list. Then, we propose a minimal first search (MFS) algorithm. It traverses the tree and explores the node with the minimal aggregate distance iteratively. This method suffers from false hits arising from keyword tests. Thus, we propose the collaborative filtering technique, which performs keywords test by multiple keyword bitmaps collectively rather than by only one. Extensive experiments on both real and synthetic datasets demonstrate the superiority of our algorithms and optimizing strategies. Numéro de notice : A2018-364 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-017-0315-0 date de publication en ligne : 29/12/2017 En ligne : https://doi.org/10.1007/s10707-017-0315-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90726 [article]

Graph-based matching of points-of-interest from collaborative geo-datasets / Tessio Novack in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 3 (March 2018) Titre : Graph-based matching of points-of-interest from collaborative geo-datasets Type de document : Article/Communication Auteurs : Tessio Novack, Auteur ; Robin Peters, Auteur ; Alexander Zipf, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] appariement de points [Termes descripteurs IGN] conflation [Termes descripteurs IGN] Foursquare [Termes descripteurs IGN] Londres [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] similitude Résumé : (Auteur) Several geospatial studies and applications require comprehensive semantic information from points-of-interest (POIs). However, this information is frequently dispersed across different collaborative mapping platforms. Surprisingly, there is still a research gap on the conflation of POIs from this type of geo-dataset. In this paper, we focus on the matching aspect of POI data conflation by proposing two matching strategies based on a graph whose nodes represent POIs and edges represent matching possibilities. We demonstrate how the graph is used for (1) dynamically defining the weights of the different POI similarity measures we consider; (2) tackling the issue that POIs should be left unmatched when they do not have a corresponding POI on the other dataset and (3) detecting multiple POIs from the same place in the same dataset and jointly matching these to the corresponding POI(s) from the other dataset. The strategies we propose do not require the collection of training samples or extensive parameter tuning. They were statistically compared with a “naive”, though commonly applied, matching approach considering POIs collected from OpenStreetMap and Foursquare from the city of London (England). In our experiments, we sequentially included each of our methodological suggestions in the matching procedure and each of them led to an increase in the accuracy in comparison to the previous results. Our best matching result achieved an overall accuracy of 91%, which is more than 10% higher than the accuracy achieved by the baseline method. Numéro de notice : A2018-103 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7030117 En ligne : https://doi.org/10.3390/ijgi7030117 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89518 [article]

Suivi d’objet par capteurs visuels et inertiels sur systèmes embarqués / Imane Salhi (2018)    

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Popularity-aware collective keyword queries in road networks / Sen Zhao in Geoinformatica [en ligne], vol 21 n° 3 (July - September 2017)  

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Extracting urban functional regions from points of interest and human activities on location-based social networks / Song Gao in Transactions in GIS, vol 21 n° 3 (June 2017)  

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Information extraction and visualization from twitter considering spatial structure / Hideyuki Fujita in Cartographica, vol 52 n° 2 (Summer 2017)  

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Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data / Yao Yao in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

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Recherche des objets mobiles dans les réseaux routiers : Une approche basée sur l’analyse formelle de concepts / Hafedh Ferchichi in Revue internationale de géomatique, vol 27 n° 2 (avril - juin 2017)  

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Assessing crowdsourced POI quality: combining methods based on reference data, history, and spatial relations / Guillaume Touya in ISPRS International journal of geo-information, vol 6 n° 3 (March 2017)    

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Adaptive and optimal combination of local features for image retrieval / Neelanjan Bhowmik (2017)    

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Une deuxième itération du processus photogrammétrique pour améliorer la précision de mise en place des images / Truong Giang Nguyen (2017)  

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Exploiting location-aware social networks for efficient spatial query processing / Liang Tang in Geoinformatica [en ligne], vol 21 n° 1 (January - March 2017)  

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