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A remote sensing assessment index for urban ecological livability and its application / Junbo Yu in Geo-spatial Information Science, vol 26 n° inconnu ([01/08/2023])  

Public [article]  inGeo-spatial Information Science > vol 26 n° inconnu [01/08/2023] Titre : A remote sensing assessment index for urban ecological livability and its application Type de document : Article/Communication Auteurs : Junbo Yu, Auteur ; Xinghua Li, Auteur ; Xiaobin Guan, Auteur ; Huanfeng Shen, Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] afforestation [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] indicateur environnemental [Termes IGN] Wuhan (Chine) [Termes IGN] zone urbaine dense Mots-clés libres : The proposed Ecological Livability Index (ELI) covers five primary ecological indicators – greenness, temperature, dryness, water-wetness, and atmospheric turbidity – which are geometrically aggregated by non-equal weights based on an entropy method. Résumé : (auteur) Remote sensing provides us with an approach for the rapid identification and monitoring of spatiotemporal changes in the urban ecological environment at different scales. This study aimed to construct a remote sensing assessment index for urban ecological livability with continuous fine spatiotemporal resolution data from Landsat and MODIS to overcome the dilemma of single image-based, single-factor analysis, due to the limitations of atmospheric conditions or the revisit period of satellite platforms. The proposed Ecological Livability Index (ELI) covers five primary ecological indicators – greenness, temperature, dryness, water-wetness, and atmospheric turbidity – which are geometrically aggregated by non-equal weights based on an entropy method. Considering multisource time-series data of each indicator, the ELI can quickly and comprehensively reflect the characteristics of the Ecological Livability Quality (ELQ) and is also comparable at different time scales. Based on the proposed ELI, the urban ecological livability in the central urban area of Wuhan, China, from 2002 to 2017, in the different seasons was analyzed every 5 years. The ELQ of Wuhan was found to be generally at the medium level (ELI ≈0.6) and showed an initial trend of degradation but then improved. Moreover, the ecological livability in spring and autumn and near rivers and lakes was found to be better, whereas urban expansion has led to the outward ecological degradation of Wuhan, but urban afforestation has enhanced the environment. In general, this paper demonstrates that the ELI has an exemplary embodiment in urban ecological research, which will support urban ecological protection planning and construction. Numéro de notice : A2022-612 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2022.2072775 Date de publication en ligne : 14/06/2022 En ligne : https://doi.org/10.1080/10095020.2022.2072775 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101366 [article]

Measuring metro accessibility: An exploratory study of Wuhan based on multi-source urban data / Tao Wu in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)  

Public [article]  inISPRS International journal of geo-information > vol 12 n° 1 (January 2023) . - n° 18 Titre : Measuring metro accessibility: An exploratory study of Wuhan based on multi-source urban data Type de document : Article/Communication Auteurs : Tao Wu, Auteur ; Mingjing Li, Auteur ; Ye Zhou, Auteur Année de publication : 2023 Article en page(s) : n° 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] accessibilité [Termes IGN] analyse de groupement [Termes IGN] classification par nuées dynamiques [Termes IGN] données multisources [Termes IGN] OpenStreetMap [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] processus de hiérarchisation analytique [Termes IGN] transport public [Termes IGN] utilisation du sol [Termes IGN] Wuhan (Chine) Résumé : (auteur) Metro accessibility has attracted interest in sustainable transport analyses. Hence, the accuracy of metro-accessibility measures have become increasingly vital. Various spatiotemporal factors, including by-metro accessibility, land-use accessibility and to-metro accessibility, affect metro accessibility; however, measuring metro accessibility while considering all these components simultaneously is challenging. By integrating these factors into a unified analysis framework, this study aims to strengthen the method for metro-accessibility assessment. Specifically, we proposed the “By metro–Land use–To metro” model to conduct a metro-accessibility index and develop an accessibility-based station typology. The results show that Wuhan metro system accessibility presented a “high-medium-low” spatial disparity from the urban center to the periphery. Meanwhile, the variety of metro-accessibility characteristics and typologies in Wuhan will equip urban planners and policymakers with a useful tool for better organising by-metro accessibility, land-use accessibility and to-metro accessibility. Numéro de notice : A2023-104 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12010018 Date de publication en ligne : 10/01/2023 En ligne : https://doi.org/10.3390/ijgi12010018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102432 [article]

Point-of-interest detection from Weibo data for map updating / Xue Yang in Transactions in GIS, vol 26 n° 6 (September 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 6 (September 2022) . - pp 2716 - 2738 Titre : Point-of-interest detection from Weibo data for map updating Type de document : Article/Communication Auteurs : Xue Yang, Auteur ; Jie Gao, Auteur ; Xiaoyun Zheng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2716 - 2738 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] commerce de détail [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] données issues des réseaux sociaux [Termes IGN] géocodage [Termes IGN] inférence [Termes IGN] information sémantique [Termes IGN] mise à jour cartographique [Termes IGN] point d'intérêt [Termes IGN] Wuhan (Chine) Résumé : (auteur) Points-of-interest (POIs) geographic information system data are increasingly important for supporting map generation and navigation services, although updating their semantic and location information still largely depends on manual labor. In this study, we propose a novel method to automatically detect the changes in POIs from Chinese text and check-in position data provided by the Chinese social media platform, Weibo. The proposed method includes three steps: (1) POI name recognition; (2) location confirmation; (3) and change detection. First, we propose recognizing a POI's name from Weibo text using the improved conditional random field algorithm. Then, we detect the location of each named POI by integrating the text address with the check-in position. The changes in the detected POIs are recognized by extracting the status words from Weibo text and a three-level status word database. To verify the effectiveness of the proposed method, we examine Wuhan as a case and detect the changes in the commercial POI using real-world Weibo data collected from January to September 2020. Based on the validation of three common map platforms, the data provided and the manual field investigation of 55 random samples, the identification accuracies for newly added POIs, the unchanged POIs, and expired POIs are approximately 100, 95.8, and 91.7%, respectively. Numéro de notice : A2022-734 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12982 Date de publication en ligne : 04/09/2022 En ligne : https://doi.org/10.1111/tgis.12982 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101701 [article]

Street-view imagery guided street furniture inventory from mobile laser scanning point clouds / Yuzhou Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 63 - 77 Titre : Street-view imagery guided street furniture inventory from mobile laser scanning point clouds Type de document : Article/Communication Auteurs : Yuzhou Zhou, Auteur ; Xu Han, Auteur ; Mingjun Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 63 - 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image Streetview [Termes IGN] instance [Termes IGN] inventaire [Termes IGN] jeu de données localisées [Termes IGN] masque [Termes IGN] mobilier urbain [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] séparateur à vaste marge [Termes IGN] Shanghai (Chine) [Termes IGN] Wuhan (Chine) Résumé : (auteur) Outdated or sketchy inventory of street furniture may misguide the planners on the renovation and upgrade of transportation infrastructures, thus posing potential threats to traffic safety. Previous studies have taken their steps using point clouds or street-view imagery (SVI) for street furniture inventory, but there remains a gap to balance semantic richness, localization accuracy and working efficiency. Therefore, this paper proposes an effective pipeline that combines SVI and point clouds for the inventory of street furniture. The proposed pipeline encompasses three steps: (1) Off-the-shelf street furniture detection models are applied on SVI for generating two-dimensional (2D) proposals and then three-dimensional (3D) point cloud frustums are accordingly cropped; (2) The instance mask and the instance 3D bounding box are predicted for each frustum using a multi-task neural network; (3) Frustums from adjacent perspectives are associated and fused via multi-object tracking, after which the object-centric instance segmentation outputs the final street furniture with 3D locations and semantic labels. This pipeline was validated on datasets collected in Shanghai and Wuhan, producing component-level street furniture inventory of nine classes. The instance-level mean recall and precision reach 86.4%, 80.9% and 83.2%, 87.8% respectively in Shanghai and Wuhan, and the point-level mean recall, precision, weighted coverage all exceed 73.7%. Numéro de notice : A2022-403 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2022.04.023 Date de publication en ligne : 12/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.04.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100711 [article]

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Detecting interchanges in road networks using a graph convolutional network approach / Min Yang in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 6 (June 2022) . - pp 1119 - 1139 Titre : Detecting interchanges in road networks using a graph convolutional network approach Type de document : Article/Communication Auteurs : Min Yang, Auteur ; Chenjun Jiang, Auteur ; Xiongfeng Yan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1119 - 1139 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse vectorielle [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification semi-dirigée [Termes IGN] détection d'objet [Termes IGN] échangeur routier [Termes IGN] extraction de traits caractéristiques [Termes IGN] modélisation [Termes IGN] noeud [Termes IGN] Pékin (Chine) [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau routier [Termes IGN] Wuhan (Chine) Résumé : (auteur) Detecting interchanges in road networks benefit many applications, such as vehicle navigation and map generalization. Traditional approaches use manually defined rules based on geometric, topological, or both properties, and thus can present challenges for structurally complex interchange. To overcome this drawback, we propose a graph-based deep learning approach for interchange detection. First, we model the road network as a graph in which the nodes represent road segments, and the edges represent their connections. The proposed approach computes the shape measures and contextual properties of individual road segments for features characterizing the associated nodes in the graph. Next, a semi-supervised approach uses these features and limited labeled interchanges to train a graph convolutional network that classifies these road segments into an interchange and non-interchange segments. Finally, an adaptive clustering approach groups the detected interchange segments into interchanges. Our experiment with the road networks of Beijing and Wuhan achieved a classification accuracy >95% at a label rate of 10%. Moreover, the interchange detection precision and recall were 79.6 and 75.7% on the Beijing dataset and 80.6 and 74.8% on the Wuhan dataset, respectively, which were 18.3–36.1 and 17.4–19.4% higher than those of the existing approaches based on characteristic node clustering. Numéro de notice : A2022-404 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2024195 Date de publication en ligne : 11/03/2022 En ligne : https://doi.org/10.1080/13658816.2021.2024195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100716 [article]

Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction / Jincai Huang in Transactions in GIS, vol 26 n° 2 (April 2022)  

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A combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)  

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Exploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods / Bin Zhang in GIScience and remote sensing, vol 59 n° 1 (2022)  

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Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network / Da He in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)  

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Recurrent origin–destination network for exploration of human periodic collective dynamics / Xiaojian Chen in Transactions in GIS, vol 26 n° 1 (February 2022)  

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Attributing pedestrian networks with semantic information based on multi-source spatial data / Xue Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)  

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Multi-GNSS PPP/INS tightly coupled integration with atmospheric augmentation and its application in urban vehicle navigation / Shengfeng Gu in Journal of geodesy, vol 95 n° 6 (June 2021)  

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A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)  

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Learning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery / Ju Zhang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

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Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale / Chen Yang in Geo-spatial Information Science, vol 23 n° 4 (December 2020)  

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