Centre de documentation
scientifique

Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities / Pavlos Tsagkis in Sustainable Cities and Society, vol 89 (February 2023)  

Public [article]  inSustainable Cities and Society > vol 89 (February 2023) . - n° 104337 Titre : Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities Type de document : Article/Communication Auteurs : Pavlos Tsagkis, Auteur ; Efthimios Bakogiannis, Auteur ; Alexandros Nikitas, Auteur Année de publication : 2023 Article en page(s) : n° 104337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] Corine (base de données) [Termes IGN] croissance urbaine [Termes IGN] données localisées libres [Termes IGN] étalement urbain [Termes IGN] Grèce [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface [Termes IGN] modèle orienté agent [Termes IGN] OpenStreetMap [Termes IGN] planification urbaine [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Urban development if not planned and managed adequately can be unsustainable. Urban growth models have been a powerful toolkit to help tackling this challenge. In this paper, we use a machine learning approach, to apply an urban growth model to five of the largest cities in Greece. Specifically, we first develop a methodology to collect, organise, handle and transform historical open spatial data, concerning various impact factors, into machine learning data. Such factors involve social, economic, biophysical, neighbouring-related and political driving forces, which must be transformed into tabular data. We also provide an artificial neural network (ANN) model and the methodology to train and evaluate it using goodness-of-fit metrics, which in turn provide the best weights of impact factors. Finally, we execute a prediction for 2030, presenting the results and output maps for each of the five case study cities. As our study is based on pan-European datasets, our model can be used for any area within Europe, using the open-source utility developed to support it. In this sense, our work provides local policy-makers and urban planners with an instrument that could help them analyse various future development scenarios and take the right decisions going forward. Numéro de notice : A2023-116 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.104337 Date de publication en ligne : 05/12/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102486 [article]

A participatory trail web map based on open source technologies / Joshua Gore in International journal of cartography, vol 8 n° 2 (July 2022)  

Public [article]  inInternational journal of cartography > vol 8 n° 2 (July 2022) . - pp 223 - 242 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 mapping Résumé : (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 [article]

An analysis of the spatial and temporal distribution of large‐scale data production events in OpenStreetMap / A. Yair Grinberger in Transactions in GIS, Vol 25 n° 2 (April 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 2 (April 2021) . - pp 622 - 641 Titre : An analysis of the spatial and temporal distribution of large‐scale data production events in OpenStreetMap Type de document : Article/Communication Auteurs : A. Yair Grinberger, Auteur ; Moritz Schott, Auteur ; Martin Raifer, Auteur ; Alexander Zipf, Auteur Année de publication : 2021 Article en page(s) : pp 622 - 641 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] distribution spatiale [Termes IGN] données localisées des bénévoles [Termes IGN] données localisées libres [Termes IGN] données spatiotemporelles [Termes IGN] extraction de données [Termes IGN] grande échelle [Termes IGN] OpenStreetMap [Termes IGN] qualité des données Résumé : (Auteur) Organized mapping activities within OpenStreetMap frequently lead to the production of massive amounts of data over a short period. In this article we utilize a novel procedure to identify such large‐scale data production events in the history of OpenStreetMap and analyze their patterns. We find that events account for a significant share of OpenStreetMap data and that organizational practices have shifted over time towards local knowledge‐based events and well‐organized data imports. However, regions in the “Global South” remain dependent on remote mapping events, pointing to uneven geographies of representation. We also find that events are frequently followed by periods of increased activity, with the exact nature of effects depending on contextual elements such as previous events. These findings portray organized activities as a significant and unique component which requires consideration when using OpenStreetMap data and analyzing their quality. Numéro de notice : A2021-360 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12746 Date de publication en ligne : 19/03/2021 En ligne : https://doi.org/10.1111/tgis.12746 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97624 [article]

Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology / Aura Salmivaara in Forestry, an international journal of forest research, vol 93 n° 5 (October 2020)  

Public [article]  inForestry, an international journal of forest research > vol 93 n° 5 (October 2020) . - pp 662 - 674 Titre : Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology Type de document : Article/Communication Auteurs : Aura Salmivaara, Auteur ; Samuli Launiainen, Auteur ; Jari Perttunen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 662 - 674 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Environnement [Termes IGN] apprentissage automatique [Termes IGN] chemin forestier [Termes IGN] classification barycentrique [Termes IGN] dégradation des sols [Termes IGN] dommage [Termes IGN] données localisées libres [Termes IGN] exploitation forestière [Termes IGN] Finlande [Termes IGN] humidité du sol [Termes IGN] modèle dynamique [Termes IGN] modèle hydrographique Résumé : (auteur) Forest harvesting operations with heavy machinery can lead to significant soil rutting. Risks of rutting depend on the soil bearing capacity which has considerable spatial and temporal variability. Trafficability prediction is required in the selection of suitable operation sites for a given time window and conditions, and for on-site route optimization during the operation. Integrative tools are necessary to plan and carry out forest operations with minimal negative ecological and economic impacts. This study demonstrates a trafficability prediction framework that utilizes a spatial hydrological model and a wide range of spatial data. Trafficability was approached by producing a rut depth prediction map at a 16 × 16 m grid resolution, based on the outputs of a general linear mixed model developed using field data from Southern Finland, modelled daily soil moisture, spatial forest inventory and topography data, along with field measured rolling resistance and information on the mass transported through the grid cells. Dynamic rut depth prediction maps were produced by accounting for changing weather conditions through hydrological modelling. We also demonstrated a generalization of the rolling resistance coefficient, measured with harvester CAN-bus channel data. Future steps towards a nationwide prediction framework based on continuous data flow, process-based modelling and machine learning are discussed. Numéro de notice : A2020-790 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1093/forestry/cpaa010 Date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1093/forestry/cpaa010 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96559 [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 IGN] analyse d'image orientée objet [Termes IGN] apprentissage profond [Termes IGN] base de données urbaines [Termes IGN] carte d'occupation du sol [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données localisées des bénévoles [Termes IGN] données localisées libres [Termes IGN] Ile-de-France [Termes IGN] image Streetview [Termes IGN] image terrestre [Termes IGN] information géographique [Termes IGN] méthode heuristique [Termes IGN] OpenStreetMap [Termes 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]

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data / Sukhjit Singh Sehra in Transactions in GIS, Vol 24 n° 1 (February 2020)  

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L'Open Data secoue les sciences / Françoise de Blomac in SIG la lettre, n° 146 (avril 2013)

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Can free (and Open Source) software and data be used to underpin a self-paced tutorial on spatial databases? / Claire Ellul in Transactions in GIS, vol 16 n° 4 (August 2012)  

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The rise of OpenStreetMap / E. Van Rees in Geoinformatics, vol 15 n° 4 (01/06/2012)

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Towards a multiobjective view of cartographic design / N. Xiao in Cartography and Geographic Information Science, vol 39 n° 2 (April 2012)  

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INSPIRE et Open Data : convergences et divergences / Henri Pornon in Géomatique expert, n° 85 (01/03/2012)

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Une journée géomatique très inspirée en Nord-Pas-de-Calais / Françoise de Blomac in SIG la lettre, n° 133 (janvier 2012)

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Open data : comment éviter la grande pagaille / Françoise de Blomac in SIG la lettre, n° 132 (décembre 2011)

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vol 99 Hors série - 01/10/2011 - Cadastre 2.0 : Proceedings international FIG symposium & Commission 7 annual meeting, [actes], Insbruck, Austria, September 2011 (Bulletin de VGI, Österreichische Zeitschrift für Vermessung & GeoInformation) / Gerda Schennach

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The Ordnance Survey OpenData initiative / B. Lilley in Cartographic journal (the), vol 48 n° 3 (August 2011)  

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