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Can machine learning improve small area population forecasts? A forecast combination approach / Irina Grossman in Computers, Environment and Urban Systems, vol 95 (July 2022)
[article]
Titre : Can machine learning improve small area population forecasts? A forecast combination approach Type de document : Article/Communication Auteurs : Irina Grossman, Auteur ; Kasun Bandara, Auteur ; Tom Wilson, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101806 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse en composantes principales
[Termes IGN] apprentissage automatique
[Termes IGN] Australie
[Termes IGN] démographie
[Termes IGN] Extreme Gradient Machine
[Termes IGN] infrastructure
[Termes IGN] lissage de données
[Termes IGN] modèle de simulation
[Termes IGN] modèle empirique
[Termes IGN] Nouvelle-Zélande
[Termes IGN] planification stratégique
[Termes IGN] pondération
[Termes IGN] série temporelleRésumé : (auteur) Generating accurate small area population forecasts is vital for governments and businesses as it provides better grounds for decision making and strategic planning of future demand for services and infrastructure. Small area population forecasting faces numerous challenges, including complex underlying demographic processes, data sparsity, and short time series due to changing geographic boundaries. In this paper, we propose a novel framework for small area forecasting which combines proven demographic forecasting methods, an exponential smoothing based algorithm, and a machine learning based forecasting technique. The proposed forecasting combination contains four base models commonly used in demographic forecasting, a univariate forecasting model specifically suitable for forecasting yearly data, and a globally trained Light Gradient Boosting Model (LGBM) that exploits the similarities between a collection of population time series. In this study, three forecast combination techniques are investigated to weight the forecasts generated by these base models. We empirically evaluate our method, by preparing small area population forecasts for Australia and New Zealand. The proposed framework is able to achieve competitive results in terms of forecasting accuracy. Moreover, we show that the inclusion of the LGBM model always improves the accuracy of combination models on both datasets, relative to combination models which only include the demographic models. In particular, the results indicate that the proposed combination framework decreases the prevalence of relatively poor forecasts, while improving the reliability of small area population forecasts. Numéro de notice : A2022-374 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101806 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101806 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100621
in Computers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101806[article]GANmapper: geographical data translation / Abraham Noah Wu in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)
[article]
Titre : GANmapper: geographical data translation Type de document : Article/Communication Auteurs : Abraham Noah Wu, Auteur ; Filip Biljecki, Auteur Année de publication : 2022 Article en page(s) : pp 1394 - 1422 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] apprentissage automatique
[Termes IGN] bâtiment
[Termes IGN] distance de Fréchet
[Termes IGN] empreinte
[Termes IGN] morphologie urbaine
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau routier
[Termes IGN] système d'information géographique
[Termes IGN] texture d'imageRésumé : (auteur) We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables the translation of one geospatial dataset to another with high fidelity and morphological accuracy. It may be especially useful in locations missing detailed and high-resolution data and those that are mapped with uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality of the results is influenced by the urban form and scale. In most cases, the experiments suggest promising performance as the method tends to truthfully indicate the locations, amount, and shape of buildings. The work has the potential to support several applications, such as energy, climate, and urban morphology studies in areas previously lacking required data or inpainting geospatial data in regions with incomplete data. Numéro de notice : A2022-493 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2041643 Date de publication en ligne : 08/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2041643 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100975
in International journal of geographical information science IJGIS > vol 36 n° 7 (juillet 2022) . - pp 1394 - 1422[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2022071 SL Revue Centre de documentation Revues en salle Disponible A participatory trail web map based on open source technologies / Joshua Gore in International journal of cartography, vol 8 n° 2 (July 2022)
[article]
Titre : A participatory trail web map based on open source technologies Type de document : Article/Communication Auteurs : Joshua Gore, Auteur ; Stefan Peters, Auteur ; Delene Weber, Auteur Année de publication : 2022 Article en page(s) : pp 223 - 242 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] approche participative
[Termes IGN] Australie méridionale (Australie)
[Termes IGN] base de données localisées
[Termes IGN] carte interactive
[Termes IGN] chemin rural
[Termes IGN] données localisées des bénévoles
[Termes IGN] données localisées libres
[Termes IGN] évaluation des données
[Termes IGN] qualité des données
[Termes IGN] randonnée
[Termes IGN] web mappingRésumé : (auteur) Interactive maps can be an important marketing tool for disseminating information about long distance walking trails and a way to value add to the recreation experience. Providing participatory functionality to maps by allowing users to share new information or refine existing information can greatly enhance user interest and improve the product. This research uses open source front and back end technologies to develop a single page, database driven participatory web map application for the Walk the Yorke Trail in South Australia. The development is based on and assessed through a user-centred design approach. The prototype participatory web map is assessed by cartographic experts, trail managers, and trail users. These evaluations indicate the validity of the design directions taken but highlight the need for information quality and quantity when encouraging participation from knowledgeable trail users, and the need for tools to ensure the continuing quality of further submitted information. Numéro de notice : A2022-921 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2020.1840320 Date de publication en ligne : 08/02/2021 En ligne : https://doi.org/10.1080/23729333.2020.1840320 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102461
in International journal of cartography > vol 8 n° 2 (July 2022) . - pp 223 - 242[article]Constraint-based evaluation of map images generalized by deep learning / Azelle Courtial in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)
[article]
Titre : Constraint-based evaluation of map images generalized by deep learning Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : n° 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] connexité (graphes)
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] montagne
[Termes IGN] programmation par contraintes
[Termes IGN] qualité des données
[Termes IGN] rendu réaliste
[Termes IGN] route
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Deep learning techniques have recently been experimented for map generalization. Although promising, these experiments raise new problems regarding the evaluation of the output images. Traditional map generalization evaluation cannot directly be applied to the results in a raster format. Additionally, the internal evaluation used by deep learning models is mostly based on the realism of images and the accuracy of pixels, and none of these criteria is sufficient to evaluate a generalization process. Finally, deep learning processes tend to hide the causal mechanisms and do not always guarantee a result that follows cartographic principles. In this article, we propose a method to adapt constraint-based evaluation to the images generated by deep learning models. We focus on the use case of mountain road generalization, and detail seven raster-based constraints, namely, clutter, coalescence reduction, smoothness, position preservation, road connectivity preservation, noise absence, and color realism constraints. These constraints can contribute to current studies on deep learning-based map generalization, as they can help guide the learning process, compare different models, validate these models, and identify remaining problems in the output images. They can also be used to assess the quality of training examples. Numéro de notice : A2022-449 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-022-00104-2 Date de publication en ligne : 07/05/2022 En ligne : http://dx.doi.org/10.1007/s41651-022-00104-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100646
in Journal of Geovisualization and Spatial Analysis > vol 6 n° 1 (June 2022) . - n° 13[article]Detecting interchanges in road networks using a graph convolutional network approach / Min Yang in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)
[article]
Titre : Detecting interchanges in road networks using a graph convolutional network approach Type de document : Article/Communication Auteurs : Min Yang, Auteur ; Chenjun Jiang, Auteur ; Xiongfeng Yan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1119 - 1139 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse vectorielle
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification semi-dirigée
[Termes IGN] détection d'objet
[Termes IGN] échangeur routier
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modélisation
[Termes IGN] noeud
[Termes IGN] Pékin (Chine)
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Detecting interchanges in road networks benefit many applications, such as vehicle navigation and map generalization. Traditional approaches use manually defined rules based on geometric, topological, or both properties, and thus can present challenges for structurally complex interchange. To overcome this drawback, we propose a graph-based deep learning approach for interchange detection. First, we model the road network as a graph in which the nodes represent road segments, and the edges represent their connections. The proposed approach computes the shape measures and contextual properties of individual road segments for features characterizing the associated nodes in the graph. Next, a semi-supervised approach uses these features and limited labeled interchanges to train a graph convolutional network that classifies these road segments into an interchange and non-interchange segments. Finally, an adaptive clustering approach groups the detected interchange segments into interchanges. Our experiment with the road networks of Beijing and Wuhan achieved a classification accuracy >95% at a label rate of 10%. Moreover, the interchange detection precision and recall were 79.6 and 75.7% on the Beijing dataset and 80.6 and 74.8% on the Wuhan dataset, respectively, which were 18.3–36.1 and 17.4–19.4% higher than those of the existing approaches based on characteristic node clustering. Numéro de notice : A2022-404 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2024195 Date de publication en ligne : 11/03/2022 En ligne : https://doi.org/10.1080/13658816.2021.2024195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100716
in International journal of geographical information science IJGIS > vol 36 n° 6 (June 2022) . - pp 1119 - 1139[article]Towards the automated large-scale reconstruction of past road networks from historical maps / Johannes H. Uhl in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkAnalysis of massive imports of open data in Openstreetmap database: a study case for France / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)PermalinkRailway lidar semantic segmentation with axially symmetrical convolutional learning / Antoine Manier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)Permalink3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation / Heyang Thomas Li in The Visual Computer, vol 38 n° 5 (May 2022)PermalinkIndividual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads / Raul de Paula Pires in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)PermalinkAn exact statistical method for analyzing co-location on a street network and its computational implementation / Wataru Morioka in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)PermalinkDetecting land use and land cover change on Barbuda before and after the Hurricane Irma with respect to potential land grabbing: A combined volunteered geographic information and multi sensor approach / Andreas Rienow in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)PermalinkA graph attention network for road marking classification from mobile LiDAR point clouds / Lina Fang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)PermalinkMining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction / Jincai Huang in Transactions in GIS, vol 26 n° 2 (April 2022)PermalinkComparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)PermalinkDynamic linkage between urbanization, electrical power consumption, and suitability analysis using remote sensing and GIS techniques / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkGIS-based employment availabilities by mode of transport in Kuwait / S. Alkheder in Applied geomatics, vol 14 n° 1 (March 2022)PermalinkIdentification de relations spatiales par apprentissage profond sur des graphes / Azelle Courtial in Cartes & Géomatique, n° 247-248 (mars-juin 2022)PermalinkLiDAR-based method for analysing landmark visibility to pedestrians in cities: case study in Kraków, Poland / Krystian Pyka in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)PermalinkRoad network generalization method constrained by residential areas / Zheng Lyu in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)PermalinkSculpting, cutting, expanding, and contracting the map / Nick Lally in Cartographica, Vol 57 n° 1 (Spring 2022)PermalinkUnravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata / Aditya Tafta Nugraha in Computers, Environment and Urban Systems, vol 92 (March 2022)PermalinkDiscovering transition patterns among OpenStreetMap feature classes based on the Louvain method / Yijiang Zhao in Transactions in GIS, vol 26 n° 1 (February 2022)PermalinkLes risques-réseaux : une matrice des défaillances des réseaux urbains interdépendants / Nabil Touili in Belgeo, vol 2022 n° 1 (2022)PermalinkSNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows / Qiliang Liu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)Permalink