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Network-constrained bivariate clustering method for detecting urban black holes and volcanoes / Qiliang Liu in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 1903 - 1929 Titre : Network-constrained bivariate clustering method for detecting urban black holes and volcanoes Type de document : Article/Communication Auteurs : Qiliang Liu, Auteur ; Zhihui Wu, Auteur ; Min Deng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1903 - 1929 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse bivariée [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] circulation urbaine [Termes descripteurs IGN] contour [Termes descripteurs IGN] détection d'anomalie [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] Pékin (Chine) [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] réseau de contraintes [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] sécurité publique [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] trajectoire [Termes descripteurs IGN] trou noir [Termes descripteurs IGN] voisinage (topologie) [Termes descripteurs IGN] volcan [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Urban black holes and volcanoes are typical traffic anomalies that are useful for optimizing urban planning and maintaining public safety. It is still challenging to detect arbitrarily shaped urban black holes and volcanoes considering the network constraints with less prior knowledge. This study models urban black holes and volcanoes as bivariate spatial clusters and develops a network-constrained bivariate clustering method for detecting statistically significant urban black holes and volcanoes with irregular shapes. First, an edge-expansion strategy is proposed to construct the network-constrained neighborhoods without the time-consuming calculation of the network distance between each pair of objects. Then, a network-constrained spatial scan statistic is constructed to detect urban black holes and volcanoes, and a multidirectional optimization method is developed to identify arbitrarily shaped urban black holes and volcanoes. Finally, the statistical significance of multiscale urban black holes and volcanoes is evaluated using Monte Carlo simulation. The proposed method is compared with three state-of-the-art methods using both simulated data and Beijing taxicab spatial trajectory data. The comparison shows that the proposed method can detect urban black holes and volcanoes more accurately and completely and is useful for detecting spatiotemporal variations of traffic anomalies. Numéro de notice : A2020-511 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1720027 date de publication en ligne : 27/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1720027 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95665 [article]

Geo-environment risk assessment in Zhengzhou City, China / Chuanming Ma in Geomatics, Natural Hazards and Risk, vol 11 n° 1 (2020)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 11 n° 1 (2020) . - pp 40 - 70 Titre : Geo-environment risk assessment in Zhengzhou City, China Type de document : Article/Communication Auteurs : Chuanming Ma, Auteur ; Wu Yan, Auteur ; Xinjie Hu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 40 - 70 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] Chine [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] évaluation des données [Termes descripteurs IGN] gestion des risques [Termes descripteurs IGN] pollution des eaux [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] risque environnemental [Termes descripteurs IGN] séisme [Termes descripteurs IGN] structure hiérarchique de données [Termes descripteurs IGN] surveillance géologique [Termes descripteurs IGN] urbanisme [Termes descripteurs IGN] zone urbaine dense Résumé : (auteur) The urban geological environment risk assessment is based on the research and analysis of the main geological environmental problems of the city, comprehensively assessing the risk of urban geological environment problems and the possible losses, and studying the degree of matching between the natural and social attributes of the geological environment. According to the urban planning of Zhengzhou City, the different types of functional areas of the city were used as evaluation objects, and the analytic hierarchy-composite index model was used to evaluate the geological environment risk and social economic vulnerability. The risk assessment model was used to evaluate the geological environment risk of Zhengzhou City. The evaluation results show that the area of high-risk area in Zhengzhou accounts for 4.05%; the area of medium-high risk area accounts for 12.89%; the area of medium-low and low-risk area accounts for 83.06%. According to the assessment results, suggestions are put forward to provide service for the urban planning, development and risk management. Highlights: * An urban geo-environment risk assessment technique system combining with the AHP - composite index assessment model is proposed. * Different types of functional zones in Zhengzhou City are taken as assessment units. * Geo-environment risk in Zhengzhou City is qualitatively and quantitatively evaluated. Numéro de notice : A2020-565 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475705.2019.1701571 date de publication en ligne : 27/12/2019 En ligne : https://doi.org/10.1080/19475705.2019.1701571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95890 [article]

A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery / Bo Yang in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1740 - 1764 Titre : A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery Type de document : Article/Communication Auteurs : Bo Yang, Auteur ; Lin Liu, Auteur ; Minxuan Lan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1740 - 1764 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] criminalité [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] géostatistique [Termes descripteurs IGN] historique des données [Termes descripteurs IGN] image VIIRS [Termes descripteurs IGN] krigeage [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] nuit [Termes descripteurs IGN] Ohio (Etats-Unis) [Termes descripteurs IGN] prédiction [Termes descripteurs IGN] prévention des risques [Termes descripteurs IGN] prise de vue nocturne [Termes descripteurs IGN] test statistique [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test. Numéro de notice : A2020-475 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1737701 date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1737701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95622 [article]

SemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-5 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-5 (August 2020) . - pp 109 - 116 Titre : SemCity Toulouse: a benchmark for building instance segmentation in satellite images Type de document : Article/Communication Auteurs : Ribana Roscher, Auteur ; Michele Volpi, Auteur ; Clément Mallet , Auteur ; Lukas Drees, Auteur ; Jan Dirk Wegner, Auteur Congrès : Congrès: ISPRS 2020, virtual Congress Imaging today foreseeing tomorrow (31 aout - 2 septembre 2020; Nice, France), Commanditaire Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 109 - 116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bati [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] instance [Termes descripteurs IGN] Toulouse [Termes descripteurs IGN] zone urbaine dense Résumé : (auteur) In order to reach the goal of reliably solving Earth monitoring tasks, automated and efficient machine learning methods are necessary for large-scale scene analysis and interpretation. A typical bottleneck of supervised learning approaches is the availability of accurate (manually) labeled training data, which is particularly important to train state-of-the-art (deep) learning methods. We present SemCity Toulouse, a publicly available, very high resolution, multi-spectral benchmark data set for training and evaluation of sophisticated machine learning models. The benchmark acts as test bed for single building instance segmentation which has been rarely considered before in densely built urban areas. Additional information is provided in the form of a multi-class semantic segmentation annotation covering the same area plus an adjacent area 3 times larger. The data set addresses interested researchers from various communities such as photogrammetry and remote sensing, but also computer vision and machine learning. Numéro de notice : A2020-503 Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-5-2020-109-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-5-2020-109-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95639 [article]

Extraction of urban built-up areas from nighttime lights using artificial neural network / Tingting Xu in Geocarto international, vol 35 n° 10 ([01/08/2020])  

Public [article]  inGeocarto international > vol 35 n° 10 [01/08/2020] . - pp 1049 - 1066 Titre : Extraction of urban built-up areas from nighttime lights using artificial neural network Type de document : Article/Communication Auteurs : Tingting Xu, Auteur ; Giovanni Coco, Auteur ; Jay Gao, Auteur Année de publication : 2020 Article en page(s) : pp 1049 - 1066 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] aménagement du territoire [Termes descripteurs IGN] bati [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] développement durable [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] éclairage public [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] rayonnement lumineux [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] seuillage [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] zone urbaine Résumé : (auteur) The spatial distribution of urban areas at the national and regional scales is critical for urban planners and governments to design sustainable and environment-friendly future development plans. The nighttime lights (NTL) data provide an effective way to monitor the urban at different scales however is usually achieved by using empirical threshold-based algorithms. This study proposed a novel Artificial Neural Network (ANN) approach, using moderate resolution imageries as NTL, MODIS NDVI and land surface temperature data, to map urban areas. Both random and maximum dissimilarity distance algorithm sampling methods were considered and compared. The validation of the urban areas extracted from MDA-based ANN against the 2011 US national land cover data showed a reasonable quality (overall accuracy = 97.84; Kappa = 0.74) and achieved more accurate result than the threshold method. This study demonstrates that ANN can provide an effective, rapid, and accurate alternative in extracting urban built-up areas from NTL. Numéro de notice : A2020-424 Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1559887 date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1080/10106049.2018.1559887 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95488 [article]

Dense stereo matching strategy for oblique images that considers the plane directions in urban areas / Jianchen Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

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A simple distributed water balance model for an urbanized river basin using remote sensing and GIS techniques / Olutoyin Adeola Fashae in Geocarto international, vol 35 n° 9 ([01/07/2020])  

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The image of subsurface geology / Ane Bang-Kittilsen in International journal of cartography, Vol 6 n° 2 (July 2020)  

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Estimating and interpreting fine-scale gridded population using random forest regression and multisource data / Yun Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data / Rochelle Schneider dos Santos in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

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Delineating and modeling activity space using geotagged social media data / Lingqian Hu in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

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Semantic signatures for large-scale visual localization / Li Weng in Multimedia tools and applications, vol inconnu (2020)  

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Geocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

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Suitable location selection for the electric vehicle fast charging station with AHP and fuzzy AHP methods using GIS / Dogus Guler in Annals of GIS, vol 26 n° 2 (April 2020)  

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Use of automated change detection and VGI sources for identifying and validating urban land use change / Ana-Maria Olteanu-Raimond in Remote sensing, vol 12 n° 7 (April 2020)  

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