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An improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments / Zohreh Masoumi in Geocarto international, vol 36 n° 2 ([01/02/2021])  

Public [article]  inGeocarto international > vol 36 n° 2 [01/02/2021] . - pp 137 - 154 Titre : An improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments Type de document : Article/Communication Auteurs : Zohreh Masoumi, Auteur ; John L. Van Genderen, Auteur ; Sadeghi Niaraki Abolghasem, Auteur Année de publication : 2021 Article en page(s) : pp 137 - 154 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] gestion des itinéraires [Termes descripteurs IGN] informatique ubiquitaire [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] optimisation par colonie de fourmis [Termes descripteurs IGN] planification [Termes descripteurs IGN] recherche du chemin optimal Résumé : (auteur) One of the important issues in ubiquitous geographic information science (GIS) is designing user-centric path finding algorithms to meet user needs. Mostly, in a route planning problem, the user’s purpose is optimization of two or more objective functions simultaneously. Thus, the problem is a multi-objective problem. In the present study, having considered multi-objective optimization methods in path finding, we developed an algorithm for solving this problem using an improved multi-objective ant colony optimization (ACO) algorithm. Modifications are introduced for various components of the ant colony metaheuristics; specifically, for those associated with the ‘ant decision rule’. The proposed algorithm was tested on the studied network. The results demonstrate that the proposed approach has acceptable settings, repeatability and run time. In addition, one of the important research outputs is a pareto-front which allows the user to select the final path according to the desired priorities. Numéro de notice : A2021-081 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595176 date de publication en ligne : 11/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595176 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96823 [article]

A heuristic approach to the generalization of complex building groups in urban villages / Wenhao Yu in Geocarto international, vol 36 n° 2 ([01/02/2021])  

Public [article]  inGeocarto international > vol 36 n° 2 [01/02/2021] . - pp 155 - 179 Titre : A heuristic approach to the generalization of complex building groups in urban villages Type de document : Article/Communication Auteurs : Wenhao Yu, Auteur ; Qi Zhou, Auteur ; Rong Zhao, Auteur Année de publication : 2021 Article en page(s) : pp 155 - 179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] empreinte [Termes descripteurs IGN] généralisation du bâti [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] représentation multiple [Termes descripteurs IGN] triangulation de Delaunay [Termes descripteurs IGN] zone urbaine [Vedettes matières IGN] Généralisation Résumé : (auteur) The generalization of building footprints acts as the basis of multi-scale mapping. Most of the previous studies focus on the generalization of regular building clusters within a wide neighbourhood, but only few has concerned about the generalization of cluttered building clusters within the narrow space such as urban village. The buildings in urban villages show special characteristics in terms of individual properties and group properties, and thus their map generalization processes are often limited. This study proposes a framework to generalize the cluttered building clusters that allows for multi-scale mapping. It first adopts a heuristic method to group adjacent buildings based on the Delaunay triangulation model and then aggregates and simplifies each building group separately. Given that the aggregated buildings in urban villages often show cluttered alignments, our method further trims the jagged boundaries of building footprints by extracting the gap space between neighbouring buildings from the Delaunay triangulation model. Numéro de notice : A2021-084 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.159046 date de publication en ligne : 25/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1590463 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96843 [article]

Optimizing local geoid undulation model using GPS/levelling measurements and heuristic regression approaches / Mosbeh R. Kaloop in Survey review, vol 52 n° 375 (November 2020)  

Public [article]  inSurvey review > vol 52 n° 375 (November 2020) . - pp 544 - 554 Titre : Optimizing local geoid undulation model using GPS/levelling measurements and heuristic regression approaches Type de document : Article/Communication Auteurs : Mosbeh R. Kaloop, Auteur ; Ahmed Zaki, Auteur ; Hamad Al-Ajami, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 544 - 554 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes descripteurs IGN] anomalie de pesanteur [Termes descripteurs IGN] géoïde local [Termes descripteurs IGN] Koweit [Termes descripteurs IGN] méthode fondée sur le noyau [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] modèle de géopotentiel [Termes descripteurs IGN] nivellement avec assistance GPS [Termes descripteurs IGN] processus gaussien [Termes descripteurs IGN] régression [Termes descripteurs IGN] régression multivariée par spline adaptative Résumé : (auteur) This study investigates to use GPS/Levelling measurements of Kuwait and four heuristic regression methods including Least Square Support Vector Regression (LSSVR), Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), and Multivariate Adaptive Regression Splines (MARS) for modelling local geoid undulation. The accuracy of the models was compared by geoid undulation of gravitational observations and Global Geopotential Models (GGMs). The results show that the KRR model is suitable for Kuwait geoid model, its error of percentage is 0.018 and 0.124% relative to gravity and GPS/Levelling geoid undulation models, respectively. Furthermore, the comparison of KRR model with GGMs models signifies its accuracy. Numéro de notice : A2020-688 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2019.1665615 date de publication en ligne : 16/09/2019 En ligne : https://doi.org/10.1080/00396265.2019.1665615 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96221 [article]

Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data / Shivangi Srivastava in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 6 (June 2020) . - pp 1117 - 1136 Titre : Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data Type de document : Article/Communication Auteurs : Shivangi Srivastava, Auteur ; John E. Vargas-Muñoz, Auteur ; Sylvain Lobry, Auteur ; Devis Tuia, Auteur Année de publication : 2020 Article en page(s) : pp 1117 - 1136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données urbaines [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données localisées libres [Termes descripteurs IGN] Ile-de-France [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] image terrestre [Termes descripteurs IGN] information géographique [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] réseau social Résumé : (auteur) We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization. Numéro de notice : A2020-269 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542698 date de publication en ligne : 18/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542698 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95041 [article]

Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods / Rocio Nahime Torres in Applied geomatics, vol 12 n° 2 (June 2020)  

Public [article]  inApplied geomatics > vol 12 n° 2 (June 2020) . - pp 225 – 246 Titre : Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods Type de document : Article/Communication Auteurs : Rocio Nahime Torres, Auteur Année de publication : 2020 Article en page(s) : pp 225 – 246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données altimétriques [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] figuré du terrain [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] montagne [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] sommet (relief) [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) Landform detection and analysis from Digital Elevation Models (DEM) of the Earth has been boosted by the availability of high-quality public data sets. Current landform identification methods apply heuristic algorithms based on predefined landform features, fine tuned with parameters that may depend on the region of interest. In this paper, we investigate the use of Deep Learning (DL) models to identify mountain summits based on features learned from data examples. We train DL models with the coordinates of known summits found in public databases and apply the trained models to DEM data obtaining as output the coordinates of candidate summits. We introduce two formulations of summit recognition (as a classification or a segmentation task), describe the respective DL models, compare them with heuristic methods quantitatively, illustrate qualitatively their performances, and discuss the challenges of training DL methods for landform recognition with highly unbalanced and noisy data sets. Numéro de notice : A2020-560 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00295-2 date de publication en ligne : 24/12/2019 En ligne : https://doi.org/10.1007/s12518-019-00295-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95870 [article]

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)  

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A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods / Yongjiu Feng in International journal of geographical information science IJGIS, vol 34 n° 1 (January 2020)  

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GeoTxt: A scalable geoparsing system for unstructured text geolocation / Morteza Karimzadeh in Transactions in GIS, vol 23 n° 1 (February 2019)  

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A context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks / Shaohua Wang in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)  

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A Topology-inferred graph-based heuristic algorithm for map simplification / QiuLei Guo in Transactions in GIS, vol 20 n° 5 (October 2016)  

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The Visvalingam algorithm: metrics, measures and heuristics / Mahes Visvalingam in Cartographic journal (the), Vol 53 n° 3 (August 2016)  

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Spatial optimization for regionalization problems with spatial interaction: a heuristic approach / K. Kim in International journal of geographical information science IJGIS, vol 30 n° 3-4 (March - April 2016)  

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A linear indexing approach to mass movements susceptibility mapping. The case of the Chefchaouen province (Morocco) / Mohamed Mastere in Revue internationale de géomatique, vol 25 n° 2 (juin - août 2015)  

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TARS: traffic-aware route search / Roy Levin in Geoinformatica, vol 18 n° 3 (July 2014)  

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Active learning in the spatial domain for remote sensing image classification / André Stumpf in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)  

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