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A GIS and hybrid simulation aided environmental impact assessment of city-scale demolition waste management / Zhikun Ding in Sustainable Cities and Society, vol 86 (November 2022)
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Titre : A GIS and hybrid simulation aided environmental impact assessment of city-scale demolition waste management Type de document : Article/Communication Auteurs : Zhikun Ding, Auteur ; Xinping Wen, Auteur ; Xiaoyan Cao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aide à la décision
[Termes IGN] déchet
[Termes IGN] impact sur l'environnement
[Termes IGN] modèle empirique
[Termes IGN] modèle orienté agent
[Termes IGN] planification urbaine
[Termes IGN] Shenzhen
[Termes IGN] simulation dynamique
[Termes IGN] système d'information géographique
[Termes IGN] ville intelligenteRésumé : (auteur) A considerable amount of demolition waste (DW) generated by urbanization and urban renewal has brought significant threats to the environment. However, there is a serious lack of environmental impact assessment towards city-scale demolition waste management (DWM), particularly from the systematical and dynamical perspective. Traditionally the assessment has been conducted from a static perspective. The purpose of this paper is to comprehensively evaluate the environmental impact of city-scale DWM from a complex system perspective. A novel evaluation model was developed by innovatively integrating the geographic information system (GIS) and system hybrid simulation consisting of system dynamics (SD), agent-based modeling (ABM) and discrete event simulation (DES). The proposed model was verified. Based on an empirical analysis of Shenzhen, China, it is found that the environmental impact of city-scale DWM is mainly concentrated in the central and northeastern regions of Shenzhen, demonstrating spatial heterogeneity and regional aggregation. Furthermore, the results reveal that the model is robust and effective to assess environmental impact from four aspects, i.e., land occupation, water pollution, air pollution and energy consumption. The findings contribute to a better understanding of the status quo of city-scale DWM and accompanying environmental impacts, and coordinating various district governments to formulate effective DWM policies. Numéro de notice : A2022-817 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104108 Date de publication en ligne : 06/08/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104108 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101983
in Sustainable Cities and Society > vol 86 (November 2022) . - n° 104108[article]A high-resolution panchromatic-multispectral satellite image fusion method assisted with building segmentation / Fang Gao in Computers & geosciences, vol 168 (November 2022)
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Titre : A high-resolution panchromatic-multispectral satellite image fusion method assisted with building segmentation Type de document : Article/Communication Auteurs : Fang Gao, Auteur ; Yihui Li, Auteur ; Peng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105219 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bâtiment
[Termes IGN] filtre de Gauss
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image Jilin
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] image satellite
[Termes IGN] lissage de donnéesRésumé : (auteur) The main difficulty of panchromatic-multispectral image fusion is to balance the quality of spatial information and the spectral fidelity. Most of the practical fusion methods determine the optimal parameters based on the spatial and spectral characteristics of all original panchromatic and multispectral bands. However, for built-up and non-built-up areas (like cropland, forest) in one image, there may be large differences in their spatial and spectral characteristics, so their fused results are not optimal respectively with same parameters. To address above issues, this paper presents a high-resolution satellite image fusion method assisted with building segmentation. First, the proposed approach computes the average gradient and Gaussian filtering parameters of built-up and non-built-up areas separately according to the building segmentation results, on the basis of smoothing filter-based intensity modulation (SFIM). Then the intermediate data of two types of areas are computed in parallel and they are composited to obtain the final fused image, weighted by the pixel-wise “building factors” derived from the building segmentation results. Moreover, to better simulate the spatial characteristics of the multispectral image, we perform the “gradient simulation” operation to extract the gradient values in the multispectral image. Experimental results on Jilin-1 satellite images show that the proposed method provides competitive performance in spatial resolution, multispectral fidelity and quantity of information, as compared to the state-of-the-art methods in mainstream commercial software. Numéro de notice : A2022-721 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105219 Date de publication en ligne : 11/09/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105219 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101657
in Computers & geosciences > vol 168 (November 2022) . - n° 105219[article]Human mobility and COVID-19 transmission: a systematic review and future directions / Mengxi Zhang in Annals of GIS, vol 28 n° 4 (November 2022)
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Titre : Human mobility and COVID-19 transmission: a systematic review and future directions Type de document : Article/Communication Auteurs : Mengxi Zhang, Auteur ; Siqin Wang, Auteur ; Tao Hu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 501 - 514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] données spatiotemporelles
[Termes IGN] épidémie
[Termes IGN] hétérogénéité spatiale
[Termes IGN] littérature
[Termes IGN] maladie virale
[Termes IGN] mobilité humaine
[Termes IGN] mobilité territoriale
[Termes IGN] modèle dynamique
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] régression linéaireRésumé : (auteur) Without a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, mathematical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from the Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration; 2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic; 3) to improve mathematical models used in analysing, simulating, and predicting the transmission of the disease; and 4) to enrich the source of mobility data to ensure data accuracy and suability. Numéro de notice : A2022-863 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2022.2041725 Date de publication en ligne : 03/03/2022 En ligne : https://doi.org/10.1080/19475683.2022.2041725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102153
in Annals of GIS > vol 28 n° 4 (November 2022) . - pp 501 - 514[article]A machine learning approach for detecting rescue requests from social media / Zheye Wang in ISPRS International journal of geo-information, vol 11 n° 11 (November 2022)
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Titre : A machine learning approach for detecting rescue requests from social media Type de document : Article/Communication Auteurs : Zheye Wang, Auteur ; Nina S.N. Lam, Auteur ; Mingxuan Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 570 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] apprentissage automatique
[Termes IGN] code postal
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] Etats-Unis
[Termes IGN] filtrage d'information
[Termes IGN] secours d'urgence
[Termes IGN] tempête
[Termes IGN] terminologie
[Termes IGN] TwitterRésumé : (auteur) Hurricane Harvey in 2017 marked an important transition where many disaster victims used social media rather than the overloaded 911 system to seek rescue. This article presents a machine-learning-based detector of rescue requests from Harvey-related Twitter messages, which differentiates itself from existing ones by accounting for the potential impacts of ZIP codes on both the preparation of training samples and the performance of different machine learning models. We investigate how the outcomes of our ZIP code filtering differ from those of a recent, comparable study in terms of generating training data for machine learning models. Following this, experiments are conducted to test how the existence of ZIP codes would affect the performance of machine learning models by simulating different percentages of ZIP-code-tagged positive samples. The findings show that (1) all machine learning classifiers except K-nearest neighbors and Naïve Bayes achieve state-of-the-art performance in detecting rescue requests from social media; (2) using ZIP code filtering could increase the effectiveness of gathering rescue requests for training machine learning models; (3) machine learning models are better able to identify rescue requests that are associated with ZIP codes. We thereby encourage every rescue-seeking victim to include ZIP codes when posting messages on social media. This study is a useful addition to the literature and can be helpful for first responders to rescue disaster victims more efficiently. Numéro de notice : A2022-846 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11110570 Date de publication en ligne : 16/11/2022 En ligne : https://doi.org/10.3390/ijgi11110570 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102081
in ISPRS International journal of geo-information > vol 11 n° 11 (November 2022) . - n° 570[article]Point2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds / Li Li in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)
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Titre : Point2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Li Li, Auteur ; Nan Song, Auteur ; Fei Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 17 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modélisation 3D
[Termes IGN] Perceptron multicouche
[Termes IGN] primitive géométrique
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de points
[Termes IGN] toitRésumé : (auteur) Three-dimensional (3D) building roof reconstruction from airborne LiDAR point clouds is an important task in photogrammetry and computer vision. To automatically reconstruct the 3D building models at Level of Detail 2 (LoD-2) from airborne LiDAR point clouds, the data-driven approaches usually need to be performed in two steps: geometric primitive extraction and roof structure inference. Obviously, the traditional approaches are not end-to-end, the accumulated errors in different stages cannot be avoided and the final 3D roof models may not be optimal. In addition, the results of 3D roof models largely depend on the accuracy of geometric primitives (planes, lines, etc.). To solve these problems, we present a deep learning-based approach to directly reconstruct building roofs from airborne LiDAR point clouds, named Point2Roof. In our method, we start by extracting the deep features for each input point using PointNet++. Then, we identify a set of candidate corner points from the input point clouds using the extracted deep features. In addition, we also regress the offset for each candidate corner point to refine their locations. After that, these candidates are clustered into a set of initial vertices, and we further refine their locations to obtain the final accurate vertices. Finally, we propose a Paired Point Attention (PPA) module to predict the true model edges from an exhaustive set of candidate edges between the vertices. Unlike traditional roof modeling approaches, the proposed Point2Roof is end-to-end. However, due to the lack of a building reconstruction dataset, we construct a large-scale synthetic dataset to verify the effectiveness and robustness of the proposed Point2Roof. The experimental results conducted on the synthetic benchmark demonstrate that the proposed Point2Roof significantly outperforms the traditional roof modeling approaches. The experiments also show that the network trained on the synthetic dataset can be applied to the real point clouds after fine-tuning the trained model on a small real dataset. The large-scale synthetic dataset, the small real dataset and the source code of our approach are publicly available in https://github.com/Li-Li-Whu/Point2Roof. Numéro de notice : A2022-745 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.027 Date de publication en ligne : 10/09/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101728
in ISPRS Journal of photogrammetry and remote sensing > vol 193 (November 2022) . - pp 17 - 28[article]Topographic descriptors on the early Dutch charts of the antipodes / Jan Tent in International journal of cartography, vol 8 n° 3 (November 2022)
PermalinkUsing converted WW1 Army Grid Referencing Systems to identify locations where Australian soldiers fell Europe / Rodney Deakin in International journal of cartography, vol 8 n° 3 (November 2022)
PermalinkA model-based scenario analysis of the impact of forest management and environmental change on the understorey of temperate forests in Europe / Bingbin Wen in Forest ecology and management, vol 522 (October-15 2022)
PermalinkRaster-based method for building selection in the multi-scale representation of two-dimensional maps / Yilang Shen in Geocarto international, vol 37 n° 22 ([10/10/2022])
PermalinkDeep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope / V.S. Martins in Remote sensing of environment, vol 280 (October 2022)
PermalinkDeveloping a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran / Sahand Seraj in Geo-spatial Information Science, vol 25 n° 3 (October 2022)
PermalinkIdentify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)
PermalinkInvestigation of recognition and classification of forest fires based on fusion color and textural features of images / Cong Li in Forests, vol 13 n° 10 (October 2022)
PermalinkMachine learning for spatial analyses in urban areas: a scoping review / Ylenia Casali in Sustainable Cities and Society, vol 85 (October 2022)
PermalinkPotential and limitation of PlanetScope images for 2-D and 3-D Earth surface monitoring with example of applications to glaciers and earthquakes / Saif Aati in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)
PermalinkSemi-supervised adversarial recognition of refined window structures for inverse procedural façade modelling / Han Hu in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)
PermalinkSpatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)
PermalinkThe fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas / Jun Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 10 (October 2022)
PermalinkRegional climate moderately influences species-mixing effect on tree growth-climate relationships and drought resistance for beech and pine across Europe / Géraud de Streel in Forest ecology and management, vol 520 (September-15 2022)
PermalinkAn improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds / Chaoquan Zhang in Photogrammetric record, vol 37 n° 179 (September 2022)
PermalinkPermalinkDeep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques / Wang Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)
PermalinkExploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 36 n° 9 (September 2022)
PermalinkFeux de forêt : un drone traque les risques de reprise / Nathalie Da Cruz in Géomètre, n° 2205 (septembre 2022)
PermalinkFlood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach / Quoc Bao Pham in Natural Hazards, vol 113 n° 2 (September 2022)
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