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Semantic‐based urban growth prediction / Marvin Mc Cutchan in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - 1482 - 1503 Titre : Semantic‐based urban growth prediction Type de document : Article/Communication Auteurs : Marvin Mc Cutchan, Auteur ; Simge Özdal‐Oktay, Auteur ; Ioannis Giannopoulos, Auteur Année de publication : 2020 Article en page(s) : 1482 - 1503 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] dynamique spatiale [Termes descripteurs IGN] Europe (géographie politique) [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modélisation spatiale [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] organisation spatiale [Termes descripteurs IGN] OWL [Termes descripteurs IGN] prévision [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] urbanisation [Termes descripteurs IGN] ville durable Résumé : (Auteur) Urban growth is a spatial process which has a significant impact on the earth’s environment. Research on predicting this complex process makes it therefore especially fruitful for decision‐making on a global scale, as it enables the introduction of more sustainable urban development. This article presents a novel method of urban growth prediction. The method utilizes geospatial semantics in order to predict urban growth for a set of random areas in Europe. For this purpose, a feature space representing geospatial configurations was introduced which embeds semantic information. Data in this feature space was then used to perform deep learning, which ultimately enables the prediction of urban growth with high accuracy. The final results reveal that geospatial semantics hold great potential for spatial prediction tasks. Numéro de notice : A2020-766 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12655 date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1111/tgis.12655 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96657 [article]

Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city / Azam Raha Bahrehdar in Computers, Environment and Urban Systems, vol 84 (November 2020)  

Public [article]  inComputers, Environment and Urban Systems > vol 84 (November 2020) . - n° 101524 Titre : Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city Type de document : Article/Communication Auteurs : Azam Raha Bahrehdar, Auteur ; Benjamin Adams, Auteur ; Ross S. Purves, Auteur Année de publication : 2020 Article en page(s) : n° 101524 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] image Flickr [Termes descripteurs IGN] Londres [Termes descripteurs IGN] mesure de similitude [Termes descripteurs IGN] métadonnées [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] orthoimage géoréférencée [Termes descripteurs IGN] perception [Termes descripteurs IGN] segmentation sémantique Résumé : (auteur) In his classic book “The Image of the City” Kevin Lynch used empirical work to show how different elements of the city were perceived: such as paths, landmarks, districts, edges, and nodes. Streets, by providing paths from which cities can be experienced, were argued to be one of the key elements of cities. Despite this long standing empirical basis, and the importance of Lynch's model in policy associated areas such as planning, work with user generated content has largely ignored these ideas. In this paper, we address this gap, using streets to aggregate filtered user generated content related to more than 1 million images and 60,000 individuals and explore similarity between more than 3000 streets in London across three dimensions: user behaviour, time and semantics. To perform our study we used two different sources of user generated content: (1) a collection of metadata attached to Flickr images and (2) street network of London from OpenStreetMap. We first explore global patterns in the distinctiveness and spatial autocorrelation of similarity using our three dimensions, establishing that the semantic and user dimensions in particular allow us to explore the city in different ways. We then used a Processing tool to interactively explore individual patterns of similarity across these four dimensions simultaneously, presenting results here for four selected and contrasting locations in London. Before drilling into the data to interpret in more detail, the identified patterns demonstrate that streets are natural units capturing perception of cities not only as paths but also through the emergence of other elements of the city proposed by Lynch including districts, landmarks and edges. Our approach also demonstrates how user generated content can be captured, allowing bottom-up perception from citizens to flow into a representation. Numéro de notice : A2020-710 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101524 date de publication en ligne : 05/08/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101524 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96255 [article]

Worldwide detection of informal settlements via topological analysis of crowdsourced digital maps / Satej Soman in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 11 (November 2020) . - n° 685 Titre : Worldwide detection of informal settlements via topological analysis of crowdsourced digital maps Type de document : Article/Communication Auteurs : Satej Soman, Auteur ; Anni Beukes, Auteur ; Cooper Nederhood, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 685 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] carte numérique [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] information topologique [Termes descripteurs IGN] infrastructure [Termes descripteurs IGN] Liberia [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] Sierra Leone [Termes descripteurs IGN] urbanisme Résumé : (auteur) The recent growth of high-resolution spatial data, especially in developing urban environments, is enabling new approaches to civic activism, urban planning and the provision of services necessary for sustainable development. A special area of great potential and urgent need deals with urban expansion through informal settlements (slums). These neighborhoods are too often characterized by a lack of connections, both physical and socioeconomic, with detrimental effects to residents and their cities. Here, we show how a scalable computational approach based on the topological properties of digital maps can identify local infrastructural deficits and propose context-appropriate minimal solutions. We analyze 1 terabyte of OpenStreetMap (OSM) crowdsourced data to create worldwide indices of street block accessibility and local cadastral maps and propose infrastructure extensions with a focus on 120 Low and Middle Income Countries (LMICs) in the Global South. We illustrate how the lack of physical accessibility can be identified in detail, how the complexity and costs of solutions can be assessed and how detailed spatial proposals are generated. We discuss how these diagnostics and solutions provide a multiscalar set of new capabilities—from individual neighborhoods to global regions—that can coordinate local community knowledge with political agency, technical capability, and further research. Numéro de notice : A2020-729 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9110685 date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.3390/ijgi9110685 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96336 [article]

Machine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians / Achituv Cohen in Transactions in GIS, Vol 24 n° 5 (October 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1264-1279 Titre : Machine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians Type de document : Article/Communication Auteurs : Achituv Cohen, Auteur ; Sagi Dalyot, Auteur Année de publication : 2020 Article en page(s) : pp 1264-1279 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] gestion des itinéraires [Termes descripteurs IGN] handicap [Termes descripteurs IGN] individu non-voyant [Termes descripteurs IGN] itinéraire piétionnier [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] navigation pédestre [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] trafic routier Résumé : (Auteur) Navigation and orientation while walking in urban spaces pose serious challenges for blind pedestrians, sometimes even on a daily basis. Research shows the practicability of computerized weighted network route planning algorithms based on OpenStreetMap mapping data for calculating customized routes for blind pedestrians. While data about pedestrians and vehicle traffic flow at different times throughout the day influence the route choices of blind pedestrians, such data do not exist in OpenStreetMap. Quantifying the correlation between spatial structure and traffic flow could be used to fill this gap. As such, we investigated machine‐learning methods to develop a computerized model for predicting pedestrian traffic flow levels, with the objective of enriching the OpenStreetMap database. This article presents prediction results by implementing six machine‐learning algorithms based on parameters relating to the geometrical and topological configuration of streets in OpenStreetMap, as well as points‐of‐interest such as public transportation and shops. The Random Forest algorithm produced the best results, whereby 95% of the testing data were successfully predicted. These results indicate that machine‐learning algorithms can accurately generate necessary temporal data, which when combined with the available crowdsourced open mapping data could augment the reliability of route planning algorithms for blind pedestrians. Numéro de notice : A2020-700 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12674 date de publication en ligne : 04/08/2020 En ligne : https://doi.org/10.1111/tgis.12674 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96210 [article]

OpenStreetMap quality assessment using unsupervised machine learning methods / Kent T. Jacobs in Transactions in GIS, Vol 24 n° 5 (October 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1280-1298 Titre : OpenStreetMap quality assessment using unsupervised machine learning methods Type de document : Article/Communication Auteurs : Kent T. Jacobs, Auteur ; Scott W. Mitchell, Auteur Année de publication : 2020 Article en page(s) : pp 1280-1298 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage non-dirigé [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] Canada [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] estimation de précision [Termes descripteurs IGN] fiabilité des données [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] Ottawa [Termes descripteurs IGN] qualité des données Résumé : (Auteur) The reliability and quality of volunteered geographic information (VGI) continue to be pressing concerns. Many VGI projects lack standard geospatial data quality assurance procedures, and the reliability of contributors remains in question. Traditional approaches rely on comparing VGI to an “authoritative” or “gold standard” dataset to assess quality. This study investigates VGI quality by analysing the OpenStreetMap (OSM) database in Ottawa‐Gatineau, focusing on historical map features and contributor data to gain an understanding of how users are contributing to the database, and their ability to do so accurately. Unsupervised machine learning analyses expose a cluster of experienced contributors classified as “OSM validators/experts”, which are then further used to attribute data quality. They are identified through a combination of strong contribution loadings associated with the use and experience of advanced OSM editors, and weaker loadings associated with feature creation and frequency of contributions leading to further correction. Limitations are discussed with implications for future work. Numéro de notice : A2020-701 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12680 date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1111/tgis.12680 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96224 [article]

OSMWatchman: Learning how to detect vandalized contributions in OSM using a Random Forest classifier / Quy Thy Truong in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)  

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Using OpenStreetMap data and machine learning to generate socio-economic indicators / Daniel Feldmeyer in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)  

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Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience / Yingwei Yan in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

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A regression model of spatial accuracy prediction for Openstreetmap buildings / Ibrahim Maidaneh Abdi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-4 (August 2020)  

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Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique / Hao Li in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Los Angeles as a digital place: The geographies of user‐generated content / Andrea Ballatore in Transactions in GIS, Vol 24 n° 4 (August 2020)  

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Developing shopping and dining walking indices using POIs and remote sensing data / Yingbin Deng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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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)  

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

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Crowdsource mapping of target buildings in hazard: the utilization of smartphone technologies and geographic services / Mohammad H. Vahidnia in Applied geomatics, vol 12 n° 1 (April 2020)  

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