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Impacts of spatiotemporal resolution and tiling on SLEUTH model calibration and forecasting for urban areas with unregulated growth patterns / Damilola Eyelade in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 5 (May 2022) . - pp 1037 - 1058 Titre : Impacts of spatiotemporal resolution and tiling on SLEUTH model calibration and forecasting for urban areas with unregulated growth patterns Type de document : Article/Communication Auteurs : Damilola Eyelade, Auteur ; Keith C. Clarke, Auteur ; Ighodalo Ijagbone, Auteur Année de publication : 2022 Article en page(s) : pp 1037 - 1058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] changement d'utilisation du sol [Termes IGN] croissance urbaine [Termes IGN] dalle [Termes IGN] données spatiotemporelles [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle de simulation [Termes IGN] modélisation spatiale [Termes IGN] Nigéria [Termes IGN] OpenStreetMap Résumé : (auteur) The SLEUTH model provides a framework for understanding land use evolution around urban areas. Calibration of SLEUTH’s behavioral coefficients can be impacted by scale and nonlinear transitions due to the SLEUTH land use deltatron module’s assumption of linear Markov change probabilities. This study attempted to establish what spatial resolution and temporal scale produces the most accurate forecasts given the linear change assumption. The impact of tiling the input data was also examined. To determine these, SLEUTH was calibrated at four spatial and three temporal scales for Ibadan, Nigeria using both untiled and tiled data. Calibration results were evaluated using accuracy metrics including Figure of Merit (FOM) and mean uncertainty. The best mix of calibration metrics (FOM 0.26) and mean uncertainty (11.64) was achieved at 30 m resolution and an intermediate temporal interval. Tiling input data led to overfitting, allowing good model fit within individual tiles but a reduction in trend recognition across land use types. Subsequently, a 2040 projection that is as accurate as possible, and scientifically justifiable given the available data, was produced. The findings provide a framework for understanding the effect of spatiotemporal scale on SLEUTH inputs that require tiling particularly for urban areas in the global south. Numéro de notice : A2022-347 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2011292 Date de publication en ligne : 16/12/2021 En ligne : https://doi.org/10.1080/13658816.2021.2011292 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100531 [article]

Detecting 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)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102732 Titre : Detecting 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 Type de document : Article/Communication Auteurs : Andreas Rienow, Auteur ; Jan Schweighöfer, Auteur ; Torben Dedring, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102732 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] anthropisation [Termes IGN] Antilles (îles des) [Termes IGN] carte thématique [Termes IGN] changement d'occupation du sol [Termes IGN] détection de changement [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] éclairage public [Termes IGN] image Sentinel [Termes IGN] image Terra-MODIS [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] tempête [Termes IGN] utilisation du sol Résumé : (auteur) Two months after the hurricanes Irma and Maria hit Barbuda, the construction of a new international airport led to accusations of degrading the Codrington Lagoon National Park and contravening the conventions of the Ramsar Program. Scientists have analyzed the aftermath with respect to historical legacies, disaster capitalism, manifestation of climate injustices and green gentrification. The main objective of this study was to quantify and allocate land use and land cover change (LULCC) in Barbuda before and after the 2017 Hurricane disasters. Remote sensing data and volunteered geographic information were analyzed to detect the potential changes in natural LULC so that human activities and the emergence of artificial surfaces could be detected. Human-induced LULCC occurred at different sites on the island, with decreased activities in Codrington, but increased and continued activities at Coco and Palmetto Points. With an accuracy of 97.1 %, we estimated a total increase of vegetated areas by 6.56 km2, and a simultaneous slight increase in roads and buildings with a total length of 249.67 km and a total area of 1.43 km2. The vegetation condition itself depict a steady decrease since 2017. New hotspots of human activity emerged on the island in the Codrington Lagoon National Park. Numéro de notice : A2022-233 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102732 Date de publication en ligne : 02/03/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102732 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100123 [article]

Exploring the association between street built environment and street vitality using deep learning methods / Yunqin Li in Sustainable Cities and Society, vol 79 (April 2022)  

Public [article]  inSustainable Cities and Society > vol 79 (April 2022) . - n° 103656 Titre : Exploring the association between street built environment and street vitality using deep learning methods Type de document : Article/Communication Auteurs : Yunqin Li, Auteur ; Nobuyoshi Yabuki, Auteur ; Tomohiro Fukuda, Auteur Année de publication : 2022 Article en page(s) : n° 103656 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] apprentissage profond [Termes IGN] attractivité (aménagement) [Termes IGN] bati [Termes IGN] image Streetview [Termes IGN] Japon [Termes IGN] morphologie urbaine [Termes IGN] OpenStreetMap [Termes IGN] piéton [Termes IGN] planification urbaine [Termes IGN] processus de hiérarchisation analytique [Termes IGN] régression linéaire [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] système d'information géographique [Termes IGN] urbanisme [Termes IGN] ville intelligente Résumé : (auteur) Street vitality has become an essential indicator for evaluating the attractiveness and potential of the sustainable development of urban blocks, and it can be reflected by the type and the frequency of people's pedestrian activities on the street. While it is recognized that street built environment features affect pedestrian behavior and street vitality, quantifying the impact of these characteristics remains inconclusive. This paper proposes an automated deep learning approach to quantitatively explore the association between the street built environment and street vitality. First, we established a deep learning model for street vitality classification for automatic evaluation of street vitality based on the volumes and activities of pedestrians in the street through multiple object tracking and scene classification. Then, we applied semantic segmentation to measure five selected vitality-related street built environment variables. Finally, a linear regression model was applied to evaluate the built environment variables’ significance and effects on street vitality. To verify our method's accuracy and applicability, we selected a commercial complex in Osaka as an illustrative example. The experimental results highlight that street width and transparency have significant positive effects on street vitality. Compared with traditional methods, our approach is feasible, reliable, transferable, and more efficient. Numéro de notice : A2022-266 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2021.103656 Date de publication en ligne : 10/01/2022 En ligne : https://doi.org/10.1016/j.scs.2021.103656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100271 [article]

Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis / Kai Zhang in Sustainable Cities and Society, vol 78 (March 2022)  

Public [article]  inSustainable Cities and Society > vol 78 (March 2022) . - n° 103598 Titre : Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis Type de document : Article/Communication Auteurs : Kai Zhang, Auteur ; Zhen Qian, Auteur ; Yue Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103598 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] apprentissage profond [Termes IGN] cartographie du bruit [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] distribution spatiale [Termes IGN] image Streetview [Termes IGN] lutte contre le bruit [Termes IGN] milieu urbain [Termes IGN] OpenStreetMap [Termes IGN] planification urbaine [Termes IGN] pollution acoustique [Termes IGN] trafic routier [Termes IGN] ville durable Résumé : (auteur) Road noise barriers (RNBs) are important urban infrastructures to relieve the harm of traffic noise pollution for citizens. Therefore, obtaining the spatial distribution characteristics of RNBs, such as precise positions and mileage, can be of great help for obtaining more accurate urban noise maps and assessing the quality of the urban living environment for sustainable urban development. However, an effective and efficient method for identifying RNBs and acquiring their attributes in large areas is scarce. This study constructs an ensemble classification model (ECM) to automatically identify RNBs at the city level based on Baidu Street View (BSV). Firstly, the bootstrap sampling method is proposed to build a street view image-based train set, where the effect of imbalanced categories of samples was reduced by adding confusing negative samples. Secondly, two state-of-the-art deep learning models, ResNet and DenseNet, are ensembled to construct an ECM based on the bagging framework. Finally, a post-processing method has been proposed based on geospatial analysis to eliminate street view images (SVIs) that are misclassified as RNBs. This study takes Suzhou, China as the study area to validate the proposed method. The model achieved an accuracy and F1-score of 0.98 and 0.90, respectively. The total mileage of the RNBs in Suzhou was 178,919 m. The results demonstrated the performance of the proposed RNBs identification framework. The significance of obtaining RNBs attributes for accelerating sustainable urban development has been demonstrated through the case of photovoltaic noise barriers (PVNBs). Numéro de notice : A2022-241 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.scs.2021.103598 Date de publication en ligne : 20/12/2021 En ligne : https://doi.org/10.1016/j.scs.2021.103598 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100167 [article]

Discovering transition patterns among OpenStreetMap feature classes based on the Louvain method / Yijiang Zhao in Transactions in GIS, vol 26 n° 1 (February 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 1 (February 2022) . - pp 236 - 258 Titre : Discovering transition patterns among OpenStreetMap feature classes based on the Louvain method Type de document : Article/Communication Auteurs : Yijiang Zhao, Auteur ; Wentao Yang, Auteur ; Yizhi Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 236 - 258 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] Açores, archipel des [Termes IGN] algorithme glouton [Termes IGN] données localisées des bénévoles [Termes IGN] étiquette [Termes IGN] géobalise [Termes IGN] Indiana (Etats-Unis) [Termes IGN] OpenStreetMap [Termes IGN] réseau routier Résumé : (auteur) Numerous studies have shown that OpenStreetMap (OSM) data can achieve high positional quality. However, the thematic attributes of OSM objects can be modified several times, which has a large impact on semantic heterogeneity. Identifying transition patterns within OSM feature classes is an important preliminary step for the tag recommendation algorithm, which can reduce the number of modifications and enhance the efficiency of OSM data updates. In this article, we propose an approach for discovering transition patterns among OSM feature classes. We first produced the transition matrix of feature classes and then developed a graph. Next, the Louvain method for community detection was utilized to cluster the feature classes. OSM data from Indiana, USA, and the Azores, Portugal, were used for our experiments. Some transition patterns were discovered: (1) many feature classes with the most transitions are the same in both datasets and most transitions occur in road-related feature classes; (2) people tend to tag general classes if they are unsure of the specific classes of tagged objects; and (3) most class transitions occurred as a result of volunteers improving the specificity and precision of feature classes. Moreover, consistently confusing concept pairs were identified. Numéro de notice : A2022-178 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12843 Date de publication en ligne : 08/10/2021 En ligne : https://doi.org/10.1111/tgis.12843 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99835 [article]

GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules / Xuke Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)  

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An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web / Abdulkadir Memduhoglu in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)  

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Attention-based vandalism detection in OpenStreetMap / Nicolas Tempelmeier (2022)  

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Generating geographical location descriptions with spatial templates: a salient toponym driven approach / Mark M. Hall in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)  

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Photogrammetric 3D mobile mapping of rail tracks / Philipp Glira in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)  

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The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data / Ana-Maria Olteanu-Raimond (2022)  

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Point-of-interest (POI) data validation methods: An urban case study / Lih Wei Yeow in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)  

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Impact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources / Yan Lin in Cartography and Geographic Information Science, vol 48 n° 6 (October 2021)  

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Assessment and prediction of urban growth for a mega-city using CA-Markov model / Veerendra Yadav in Geocarto international, vol 36 n° 17 ([15/09/2021])  

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Benford’s law and geographical information – the example of OpenStreetMap / Franz-Benjamin Mocnik in International journal of geographical information science IJGIS, vol 35 n° 9 (September 2021)  

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