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RegNet: a neural network model for predicting regional desirability with VGI data / Wenzhong Shi in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 175 - 192 Titre : RegNet: a neural network model for predicting regional desirability with VGI data Type de document : Article/Communication Auteurs : Wenzhong Shi, Auteur ; Zhewei Liu, Auteur ; Zhenlin An, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 175 - 192 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] classification par réseau neuronal [Termes IGN] données localisées des bénévoles [Termes IGN] Hong-Kong [Termes IGN] modèle de simulation [Termes IGN] niveau local [Termes IGN] participation du public [Termes IGN] régression [Termes IGN] réseau social géodépendant Résumé : (auteur) Volunteered geographic information can be used to predict regional desirability. A common challenge regarding previous works is that intuitive empirical models, which are inaccurate and bring in perceptual bias, are traditionally used to predict regional desirability. This results from the fact that the hidden interactions between user online check-ins and regional desirability have not been revealed and clearly modelled yet. To solve the problem, a novel neural network model ‘RegNet’ is proposed. The user check-in history is input into a neural network encoder structure firstly for redundancy reduction and feature learning. The encoded representation is then fed into a hidden-layer structure and the regional desirability is predicted. The proposed RegNet is data-driven and can adaptively model the unknown mappings from input to output, without presumed bias and prior knowledge. We conduct experiments with real-world datasets and demonstrate RegNet outperforms state-of-the-art methods in terms of ranking quality and prediction accuracy of rating. Additionally, we also examine how the structure of encoder affects RegNet performance and suggest on choosing proper sizes of encoded representation. This work demonstrates the effectiveness of data-driven methods in modelling the hidden unknown relationships and achieving a better performance over traditional empirical methods. Numéro de notice : A2021-023 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1768261 date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1768261 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96526 [article]

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Automated labeling of schematic maps by optimization with knowledge acquired from existing maps / Tian Lan in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1722 - 1739 Titre : Automated labeling of schematic maps by optimization with knowledge acquired from existing maps Type de document : Article/Communication Auteurs : Tian Lan, Auteur ; Zhilin Li, Auteur ; Qian Peng, Auteur ; Xinyu Gong, Auteur Année de publication : 2020 Article en page(s) : pp 1722 - 1739 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] automatisation [Termes IGN] calcul d'itinéraire [Termes IGN] Chine [Termes IGN] données cartographiques [Termes IGN] étiquetage sémantique [Termes IGN] Hong-Kong [Termes IGN] optimisation (mathématiques) [Termes IGN] réseau métropolitain Résumé : (Auteur) Schematic maps are simplified representations of line networks, aiming to help people quickly and accurately perform route planning and orientation tasks. The automated generation of such maps is generally treated as an optimization problem. Most researchers prefer to optimize network layouts and name labels separately, because optimizing them simultaneously is still intractable. It is found that optimizing network layouts is extensively studied, while optimizing name labels is rarely considered. In the optimization of name labels, constraints can be established with rules from cartographic experts, literature (e.g., specification and technical documents), and/or existing maps. However, some rules from experts and literature cannot be explicitly and mathematically expressed. This study aims to develop an automated labeling method with rules from existing maps. We first acquire the rules (i.e., the potential positions and the preferences of these positions) from some existing schematic maps and then integrate them into an optimization algorithm. Experimental evaluation is conducted by a questionnaire in terms of “ease level of finding name labels,” “congestion level,” and “satisfaction level” using Tianjin and Hong Kong metro schematic maps and the labels of our method. The results show that the proposed method can automatically generate effective labels. Numéro de notice : A2020-769 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12671 date de publication en ligne : 06/08/2020 En ligne : https://doi.org/10.1111/tgis.12671 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96661 [article]

An empirical study on the intra-urban goods movement patterns using logistics big data / Pengxiang Zhao in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 6 (June 2020) . - pp 1089 - 1116 Titre : An empirical study on the intra-urban goods movement patterns using logistics big data Type de document : Article/Communication Auteurs : Pengxiang Zhao, Auteur ; Wenzhong Shi, Auteur ; Tao Jia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1089 - 1116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] analyse systémique [Termes IGN] circulation urbaine [Termes IGN] fret [Termes IGN] gestion urbaine [Termes IGN] Hong-Kong [Termes IGN] interaction spatiale [Termes IGN] logistique [Termes IGN] objet mobile [Termes IGN] origine - destination [Termes IGN] plan de déplacement urbain [Termes IGN] réseau de transport [Termes IGN] série temporelle Résumé : (auteur) Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning. Numéro de notice : A2020-268 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1520236 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/13658816.2018.1520236 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95039 [article]

Novel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images / Zhi Yong Lv in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 12 (December 2019) . - pp 9554 - 9574 Titre : Novel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images Type de document : Article/Communication Auteurs : Zhi Yong Lv, Auteur ; Tong Fei Liu, Auteur ; Zhang Penglin, Auteur ; Jon Atli Benediktsson, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 9554 - 9574 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse diachronique [Termes IGN] approche pixel [Termes IGN] changement d'occupation du sol [Termes IGN] Chine [Termes IGN] détection de changement [Termes IGN] histogramme [Termes IGN] Hong-Kong [Termes IGN] image à très haute résolution [Termes IGN] phénologie [Termes IGN] seuillage de points Résumé : (auteur) Detecting land cover change through very-high-resolution (VHR) remote sensing images is helpful in supporting urban sustainable development, natural disaster evaluation, and environmental assessment. However, the intraclass spectral variance in VHR remote sensing images is usually larger than that of median-low remote sensing images. Furthermore, the bitemporal images are usually acquired under different atmospheric conditions, sun height, soil moisture, and other factors. Consequently, in practical applications, many pseudo changes are presented in the detected map. In this paper, an adaptive histogram trend (AHT) similarity approach is promoted to quantitatively measure the magnitude between the corresponding pixels in bitemporal images in terms of change semantic. In the proposed approach, to reduce the phenological effect on the bitemporal images of land cover change detection (LCCD), we first define the quantitative description of AHT. Second, the change magnitudes between pairwise pixels are quantitatively measured by an improved bin-to-bin (B2B) distance between the corresponding AHTs. Then, the change magnitudes between two entire bitemporal images are measured AHT-by-AHT. Finally, binary threshold methods, such as the Otsu method or the double-window flexible pace search (DFPS) method, are used to divide the change magnitude image into binary change detection maps and obtain the final change detection map. The performance of the AHT-based LCCD approach is verified by four pairs of VHR remote-sensing images that correspond to two types of real land cover change cases. The detected results based on the four pairs of bitemporal VHR images outperformed the compared state-of-the-art LCCD methods. Numéro de notice : A2019-599 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2927659 date de publication en ligne : 01/08/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2927659 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94593 [article]

Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images / Cheolhee Yoo in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 155 - 170 Titre : Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images Type de document : Article/Communication Auteurs : Cheolhee Yoo, Auteur ; Daehyeon Han, Auteur ; Jungho Im, Auteur ; Benjamin Bechtel, Auteur Année de publication : 2019 Article en page(s) : pp 155 - 170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] Chicago (Illinois) [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] climat urbain [Termes IGN] Hong-Kong [Termes IGN] ilot thermique urbain [Termes IGN] image Landsat-8 [Termes IGN] Madrid (Espagne) [Termes IGN] Rome [Termes IGN] World Urban Database and Access Portal Tools [Termes IGN] zone urbaine dense Résumé : (Auteur) The Local Climate Zone (LCZ) scheme is a classification system providing a standardization framework to present the characteristics of urban forms and functions, especially for urban heat island (UHI) research. Landsat-based 100 m resolution LCZ maps have been classified by the World Urban Database and Portal Tool (WUDAPT) method using a random forest (RF) machine learning classifier. Some studies have proposed modified RF and convolutional neural network (CNN) approaches. This study aims to compare CNN with an RF classifier for LCZ mapping in great detail. We designed five schemes (three RF-based schemes (S1–S3) and two CNN-based ones (S4–S5)), which consist of various combinations of input features from bitemporal Landsat 8 data over four global mega cities: Rome, Hong Kong, Madrid, and Chicago. Among the five schemes, the CNN-based one with the incorporation of a larger neighborhood information showed the best classification performance. When compared to the WUDAPT workflow, the overall accuracies for entire land cover classes (OA) and for urban LCZ types (i.e., LCZ1-10; OAurb) increased by about 6–8% and 10–13%, respectively, for the four cities. The transferability of LCZ models for the four cities were evaluated, showing that CNN consistently resulted in higher accuracy (increased by about 7–18% and 18–29% for OA and OAurb, respectively) than RF. This study revealed that the CNN classifier classified particularly well for the specific LCZ classes in which buildings were mixed with trees or buildings or plants were sparsely distributed. The research findings can provide a basis for guidance of future LCZ classification using deep learning. Numéro de notice : A2019-495 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.009 date de publication en ligne : 19/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93728 [article]

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Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)  

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Integration of aerial oblique imagery and terrestrial imagery for optimized 3D modeling in urban areas / Bo Wu in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)  

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A new scheme for urban impervious surface classification from SAR images / Hongsheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)  

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Investigation of ionospheric effects on SAR Interferometry (InSAR): A case study of Hong Kong / Wu Zhu in Advances in space research, vol 58 n° 4 (August 2016)  

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Empirical determination of geometric parameters for selective omission in a road network / Qi Zhou in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)  

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Geometric integration of high-resolution satellite imagery and airborne LiDAR data for improved geopositioning accuracy in metropolitan areas / Bo Wu in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)  

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Temporal characteristics of thermal satellite images for urban heat stress and heat island mapping / J.E. Nichol in ISPRS Journal of photogrammetry and remote sensing, vol 74 (Novembrer 2012)

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Construction of digital 3D highway model using stereo IKONOS satellite imagery / Ahmed Shaker in Geocarto international, vol 26 n° 1 (February 2011)  

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