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Identification of urban agglomeration spatial range based on social and remote-sensing data - For evaluating development level of urban agglomerations / Shuai Zhang in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 456 Titre : Identification of urban agglomeration spatial range based on social and remote-sensing data - For evaluating development level of urban agglomerations Type de document : Article/Communication Auteurs : Shuai Zhang, Auteur ; Hua Wei, Auteur Année de publication : 2022 Article en page(s) : n° 456 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] agglomération [Termes IGN] analyse spatiale [Termes IGN] Chine [Termes IGN] croissance urbaine [Termes IGN] données massives [Termes IGN] données socio-économiques [Termes IGN] éclairage public [Termes IGN] fusion de données [Termes IGN] image NPP-VIIRS [Termes IGN] point d'intérêt [Termes IGN] prise de vue nocturne [Termes IGN] segmentation d'image [Termes IGN] transformation en ondelettes Résumé : (auteur) The accurate identification of urban agglomeration spatial area is helpful in understanding the internal spatial relationship under urban expansion and in evaluating the development level of urban agglomeration. Previous studies on the identification of spatial areas often ignore the functional distribution and development of urban agglomerations by only using nighttime light data (NTL). In this study, a new method is firstly proposed to identify the accurate spatial area of urban agglomerations by fusing night light data (NTL) and point of interest data (POI); then an object-oriented method is used by this study to identify the spatial area, finally the identification results obtained by different data are verified. The results show that the accuracy identified by NTL data is 82.90% with the Kappa coefficient of 0.6563, the accuracy identified by POI data is 81.90% with the Kappa coefficient of 0.6441, and the accuracy after data fusion is 90.70%, with the Kappa coefficient of 0.8123. The fusion of these two kinds of data has higher accuracy in identifying the spatial area of urban agglomeration, which can play a more important role in evaluating the development level of urban agglomeration; this study proposes a feasible method and path for urban agglomeration spatial area identification, which is not only helpful to optimize the spatial structure of urban agglomeration, but also to formulate the spatial development policy of urban agglomeration. Numéro de notice : A2022-645 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080456 Date de publication en ligne : 21/08/2022 En ligne : https://doi.org/10.3390/ijgi11080456 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101461 [article]

Dynamic 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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 3 (March 2022) . - pp 171 - 179 Titre : Dynamic linkage between urbanization, electrical power consumption, and suitability analysis using remote sensing and GIS techniques Type de document : Article/Communication Auteurs : Muhammad Nasar Ahmad, Auteur ; Qimin Cheng, Auteur ; Fang Luo, Auteur Année de publication : 2022 Article en page(s) : pp 171 - 179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] consommation [Termes IGN] densité de population [Termes IGN] éclairage public [Termes IGN] électricité [Termes IGN] étalement urbain [Termes IGN] image DMSP-OLS [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] Pakistan [Termes IGN] prise de vue nocturne [Termes IGN] urbanisation Résumé : (auteur) This article proposes an estimation method for assessing urban sprawl using multispectral remote sensing data: SNPP-VIIRS, DMSP/OLS, Landsat 5-TM, and Landsat 8-OLI. This study focuses on the impacts of human activities, in terms of increased electrical-power consumption (EPC) due to urbanization. For this purpose, night-time light data are used to measure the EPC growth from 2000 to 2020. We also perform a suitability analysis using geographic information-systems techniques to propose a new urban town in Lahore to mitigate urbanization and EPC increase. We found an overall increase of 33% in urban area and an EPC increase of 21.6% in the last two decades. We also find that the best proposed site for the new urban town is in the northwest of Lahore. Numéro de notice : A2022-201 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00026R3 Date de publication en ligne : 01/03/2022 En ligne : https://doi.org/10.14358/PERS.21-00026R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100004 [article]

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Monitoring population dynamics in the Pearl River Delta from 2000 to 2010 / Sisi Yu in Geocarto international, vol 35 n° 14 ([15/10/2020])  

Public [article]  inGeocarto international > vol 35 n° 14 [15/10/2020] . - pp 1511 - 1526 Titre : Monitoring population dynamics in the Pearl River Delta from 2000 to 2010 Type de document : Article/Communication Auteurs : Sisi Yu, Auteur ; ZengXiang Zhang, Auteur ; Fang Liu, Auteur Année de publication : 2020 Article en page(s) : pp 1511 - 1526 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] agglomération [Termes IGN] croissance urbaine [Termes IGN] delta de la rivière des perles [Termes IGN] données démographiques [Termes IGN] image DMSP-OLS [Termes IGN] Kouangtoung (Chine) [Termes IGN] prise de vue nocturne [Termes IGN] recensement démographique [Termes IGN] répartition géographique [Termes IGN] série temporelle [Termes IGN] surveillance de l'urbanisation Résumé : (auteur) Although numerous literatures have documented the monitoring of population distributions and dynamics for socio-economic development, environmental protection, and urban planning on different scales, little attention has been paid to long-term and multi-frequency population evolution on urban agglomeration scale, especially in non-census years. Furthermore, although multi models have been applied to population spatialization based on night-time light imagery (NLT) and census data, their accuracy needs to be further improved. Selected the Pearl River Delta (PRD), China as the study area, this work aimed to solve the aforementioned problems by constructing the residential extent extraction index (REEI) and employing the population growth theory and ‘DN density–population density’ model. Results indicated that the proposed approaches were feasible to optimize NTL products and simulate populations in both census (2000, 2010) and non-census (2005) years. Population evolution in the PRD presented distinct differences from space and over time, and mainly driven by socioeconomic development. Numéro de notice : A2020-617 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1576778 Date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/10106049.2019.1576778 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95993 [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 IGN] coefficient de corrélation [Termes IGN] criminalité [Termes IGN] données spatiotemporelles [Termes IGN] géostatistique [Termes IGN] historique des données [Termes IGN] image NPP-VIIRS [Termes IGN] krigeage [Termes IGN] modèle dynamique [Termes IGN] nuit [Termes IGN] Ohio (Etats-Unis) [Termes IGN] prédiction [Termes IGN] prévention des risques [Termes IGN] prise de vue nocturne [Termes IGN] test statistique [Termes 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 Affiliation des auteurs : non IGN 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]

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Learning scene geometry for visual localization in challenging conditions / Nathan Piasco (2019)    

Public Titre : Learning scene geometry for visual localization in challenging conditions Type de document : Article/Communication Auteurs : Nathan Piasco , Auteur ; Désiré Sidibé, Auteur ; Valérie Gouet-Brunet , Auteur ; Cédric Demonceaux, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : PLaTINUM / Gouet-Brunet, Valérie Conférence : ICRA 2019, International Conference on Robotics and Automation 20/05/2019 24/05/2019 Montréal Québec - Canada Proceedings IEEE Importance : pp 9094 - 9100 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse visuelle [Termes IGN] appariement d'images [Termes IGN] carte de profondeur [Termes IGN] descripteur [Termes IGN] géométrie de l'image [Termes IGN] image RVB [Termes IGN] localisation basée vision [Termes IGN] précision de localisation [Termes IGN] prise de vue nocturne [Termes IGN] robotique [Termes IGN] scène urbaine [Termes IGN] variation diurne [Termes IGN] variation saisonnière [Termes IGN] vision par ordinateur Résumé : (auteur) We propose a new approach for outdoor large scale image based localization that can deal with challenging scenarios like cross-season, cross-weather, day/night and longterm localization. The key component of our method is a new learned global image descriptor, that can effectively benefit from scene geometry information during training. At test time, our system is capable of inferring the depth map related to the query image and use it to increase localization accuracy. We are able to increase recall@1 performances by 2.15% on cross-weather and long-term localization scenario and by 4.24% points on a challenging winter/summer localization sequence versus state-of-the-art methods. Our method can also use weakly annotated data to localize night images across a reference dataset of daytime images. Numéro de notice : C2019-002 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICRA.2019.8794221 Date de publication en ligne : 12/08/2019 En ligne : http://doi.org/10.1109/ICRA.2019.8794221 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93774

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