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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]

Drought-vulnerable vegetation increases exposure of disadvantaged populations to heatwaves under global warming: A case study from Los Angeles / Chunyu Dong in Sustainable Cities and Society, vol 93 (June 2023)  

Public [article]  inSustainable Cities and Society > vol 93 (June 2023) . - n° 104488 Titre : Drought-vulnerable vegetation increases exposure of disadvantaged populations to heatwaves under global warming: A case study from Los Angeles Type de document : Article/Communication Auteurs : Chunyu Dong, Auteur ; Yu Yan, Auteur ; Jie Guo, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 104488 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement climatique [Termes IGN] climat urbain [Termes IGN] données socio-économiques [Termes IGN] espace vert [Termes IGN] ilot thermique urbain [Termes IGN] image Terra-MODIS [Termes IGN] Los Angeles [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] sécheresse [Termes IGN] température au sol Résumé : (auteur) Urban vegetation is valuable in alleviating local heatwaves. However, drought may decrease vegetation health and limit this cooling effect. Here we use satellite-based Normalized Difference Vegetation Index (NDVI) and Palmer Drought Severity Index (PDSI) to investigate the sensitivity of urban vegetation to drought in Coastal Greater Los Angeles (CGLA) from 2001 to 2020. We applied four statistical models to analyze the relations between 15 socioeconomic variables and the vegetation's sensitivity to drought. We then examined the changes in the cooling effect of the urban vegetation during drought and non-drought periods using remotely sensed land surface temperature (LST) data. The results suggest that economically disadvantaged areas with higher proportions of Hispanics and Blacks are typified by vegetation more sensitive to drought, which is likely linked to inequality in water use. Moreover, these populations experience a lower degree of vegetation cooling effects and higher exposure to heatwaves. The findings of this study imply that the potential of a community's vegetation in mitigating heatwaves is significantly influenced by the socioeconomic conditions of the community. Increasing the resilience of urban vegetation to drought in disadvantaged communities may help promote environmentally sustainable and socially resilient cities under a warming climate. Numéro de notice : A2023-191 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scs.2023.104488 Date de publication en ligne : 26/02/2023 En ligne : https://doi.org/10.1016/j.scs.2023.104488 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102972 [article]

Optimized position estimation in mobile multipath environments using machine learning / Nesreen I. Ziedan in Navigation : journal of the Institute of navigation, vol 70 n° 2 (Summer 2023)  

Public [article]  inNavigation : journal of the Institute of navigation > vol 70 n° 2 (Summer 2023) . - n° 569 Titre : Optimized position estimation in mobile multipath environments using machine learning Type de document : Article/Communication Auteurs : Nesreen I. Ziedan, Auteur Année de publication : 2023 Article en page(s) : n° 569 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] apprentissage automatique [Termes IGN] estimation de pose [Termes IGN] milieu urbain [Termes IGN] signal GNSS [Termes IGN] trajet multiple Résumé : (auteur) The positioning accuracy of global navigation satellite system receivers is frequently degraded in urban areas due to reflected signals. A moving receiver faces additional challenges because it needs to adjust to changes in the statuses of the signals received, including line-of-sight (LOS), multipath, non-LOS, or invisible. This paper proposes two new algorithms that can be used to enhance the accuracy of a moving receiver. The first algorithm is called Optimized Position Estimation (OPE). The OPE algorithm estimates the most likely paths and identifies the one with the optimal weight. The second algorithm is called Intelligent Signal Status Estimation (ISE). The ISE algorithm utilizes a self-organizing map machine-learning algorithm to estimate the probability of a change in signal status. The algorithms are tested using global positioning system C/A signals, which have over 50 changes in their statuses. The results obtained using these algorithms reveal that the accuracy is enhanced by as much as 96.3% (i.e., a 27-fold improvement) when compared to results using a conventional navigation algorithm. Numéro de notice : A2023-200 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.33012/navi.569 Date de publication en ligne : 12/09/2022 En ligne : https://doi.org/10.33012/navi.569 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103094 [article]

Flood vulnerability assessment of urban buildings based on integrating high-resolution remote sensing and street view images / Ziyao Xing in Sustainable Cities and Society, vol 92 (May 2023)  

Public [article]  inSustainable Cities and Society > vol 92 (May 2023) . - n° 104467 Titre : Flood vulnerability assessment of urban buildings based on integrating high-resolution remote sensing and street view images Type de document : Article/Communication Auteurs : Ziyao Xing, Auteur ; Shuai Yang, Auteur ; Xuli Zan, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 104467 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] bâtiment [Termes IGN] Chine [Termes IGN] gestion des risques [Termes IGN] image Streetview [Termes IGN] inondation [Termes IGN] milieu urbain [Termes IGN] planification urbaine [Termes IGN] Quickbird [Termes IGN] segmentation sémantique [Termes IGN] vulnérabilité Résumé : (auteur) Urban flood risk management requires an extensive investigation of the vulnerability characteristics of buildings. Large-scale field surveys usually cost a lot of time and money, while satellite remote sensing and street view images can provide information on the tops and facades of buildings respectively. Thereupon, this paper develops a building vulnerability assessment framework using remote sensing and street view features. Specifically, a UNet-based semantic segmentation model, FSA-UNet (Fusion-Self-Attention-UNet) is proposed to integrate remote sensing and street view features and the vulnerability information contained in the images is fully exploited. And the building vulnerability index is generated to provide the spatial distribution characteristics of urban building vulnerability. The experiment shows that the mIoU of the proposed model can reach 82% for building vulnerability classification in Hefei, China, which is more accurate than the traditional semantic segmentation models. The results indicate that the integration of street view and remote sensing image features can improve the ability of building vulnerability assessment, and the model proposed in this study can better capture the correlation features of multi-angle images through the self-attention mechanism and combines hierarchy features and edge information to improve the classification effect. This study can support for disaster management and urban planning. Numéro de notice : A2023-152 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scs.2023.104467 Date de publication en ligne : 23/02/2023 En ligne : https://doi.org/10.1016/j.scs.2023.104467 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102826 [article]

An agent-based modeling approach for public charging demand estimation and charging station location optimization at urban scale / Zhiyan Yi in Computers, Environment and Urban Systems, vol 101 (April 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 101 (April 2023) . - n° 101949 Titre : An agent-based modeling approach for public charging demand estimation and charging station location optimization at urban scale Type de document : Article/Communication Auteurs : Zhiyan Yi, Auteur ; Bingkun Chen, Auteur ; Xiaoyue Cathy Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101949 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] chaîne de Markov [Termes IGN] distribution spatiale [Termes IGN] équipement collectif [Termes IGN] modèle orienté agent [Termes IGN] optimisation spatiale [Termes IGN] planification urbaine [Termes IGN] véhicule électrique [Termes IGN] zone urbaine Résumé : (auteur) As the market penetration of electric vehicles (EVs) increases, the surge of charging demand could potentially overload the power grid and disrupt infrastructure planning. Hence, an efficient deployment strategy of electrical vehicle supply equipment (EVSE) is much needed. This study attempts to address the EVSE problem from a microscopic perspective by formulating the problem in two steps: public charging demand simulation and charging station location optimization. Specifically, we apply agent-based modeling approach to produce high-resolution daily driving profiles within an urban-scale context using MATSim. Subsequently, we perform EV assignment based on socioeconomic attributes to determine EV adopters. Energy consumption model and public charging rule are specified for generating synthetic public charging demand and such demand is validated against real-world public charging records to guarantee the robustness of simulation results. In the second step, we apply a location approach – capacitated maximal coverage location problem (CMCLP) model – to reallocate existing charging stations with the objective of maximizing the coverage of total charging demands generated from the previous step under the budget and load capacity constraints. The entire framework is capable of modeling the spatiotemporal distribution of public charging demand in a bottom-up fashion, and provide practical support for future public EVSE installation. Numéro de notice : A2023-186 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2023.101949 Date de publication en ligne : 15/02/2023 En ligne : https://doi.org/10.1016/j.compenvurbsys.2023.101949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102960 [article]

Evaluating future railway-induced urban growth of twelve cities using multiple SLEUTH models with open-source geospatial inputs / Alvin Christopher G. Varquez in Sustainable Cities and Society, vol 91 (April 2023)  

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A unified cycle-slip, multipath estimation, detection and mitigation method for VIO-aided PPP in urban environments / Bo Xu in GPS solutions, vol 27 n° 2 (April 2023)  

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Mapping population distribution from open address data: application to mainland Portugal / Nelson Mileu in Journal of maps, vol 18 n° 3 (March 2023)  

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Residents’ Experiential Knowledge and Its Importance for Decision-Making Processes in Spatial Planning: A PPGIS Based Study / Edyta Bąkowska-Waldmann in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)  

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SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images / Hao Wu in Computers, Environment and Urban Systems, vol 100 (March 2023)  

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Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning / Iris de Gelis in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)  

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Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities / Pavlos Tsagkis in Sustainable Cities and Society, vol 89 (February 2023)  

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Long-term changes in 3D urban form in four Spanish cities / Dario Domingo in Landscape and Urban Planning, vol 230 (February 2023)  

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Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model / Zensheng Wang in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)  

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Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services / Mingyue Xu in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)  

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