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Termes IGN > aménagement > urbanisme
urbanisme
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Aménagement urbain, Développement urbain, Habitat (urbanisme), Planification urbaine, Ville modèle. Synonyme(s)aménagement urbainVoir aussi |
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Geographic information system data considerations in the context of the enhanced bathtub model for coastal inundation / Lauren Lyn Williams in Transactions in GIS, vol 26 n° 7 (November 2022)
[article]
Titre : Geographic information system data considerations in the context of the enhanced bathtub model for coastal inundation Type de document : Article/Communication Auteurs : Lauren Lyn Williams, Auteur ; Melanie Lück-Vogel, Auteur Année de publication : 2022 Article en page(s) : pp 3074 - 3089 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] Afrique du sud (état)
[Termes IGN] ArcGIS
[Termes IGN] données lidar
[Termes IGN] milieu urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] semis de points
[Termes IGN] submersion marine
[Termes IGN] système d'information géographiqueRésumé : (auteur) concerning digital surface models (DSMs) to determine: (a) the highest appropriate resolution achievable from available LiDAR data and consider variations between derived sub-meter DSMs; (b) optimal DSM horizontal resolution for coastal inundation modeling based on “out-the-box” solutions; and (c) mechanisms to address the challenge presented by DSMs regarding overhanging structures for a study site in False Bay, South Africa. Results showed that while sub-meter DSMs are achievable, low point cloud densities may result in the misrepresentation of structures, which affects the inundation extents. High horizontal resolution DSMs are required for inundation modeling in an urban setting to account for narrow thoroughfares. Challenges posed by first return LiDAR depicting bridges as solid structures could be circumvented by modifying the input water source for the eBTM processing. Numéro de notice : A2022-888 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1111/tgis.12995 Date de publication en ligne : 18/10/2022 En ligne : https://doi.org/10.1111/tgis.12995 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102232
in Transactions in GIS > vol 26 n° 7 (November 2022) . - pp 3074 - 3089[article]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)
[article]
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]Driving factors of urban sprawl in the Romanian plain. Regional and temporal modelling using logistic regression / Ines Grigorescu in Geocarto international, vol 37 n° 24 ([20/10/2022])
[article]
Titre : Driving factors of urban sprawl in the Romanian plain. Regional and temporal modelling using logistic regression Type de document : Article/Communication Auteurs : Ines Grigorescu, Auteur ; Gheorghe Kucsicsa, Auteur ; Bianca Mitrică, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 7220 - 7246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de données
[Termes IGN] analyse spatio-temporelle
[Termes IGN] croissance urbaine
[Termes IGN] étalement urbain
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] modélisation spatiale
[Termes IGN] régression logistique
[Termes IGN] Roumanie
[Termes IGN] zone urbaineRésumé : (auteur) The paper investigates built-up areas expansion after the 1990 in one of the highly urbanized regions of Romania - Romanian Plain, in order to explore the urban sprawl phenomena and its temporal and regional disparities in relation to some of the main distance driving factors. The research uses Landsat 4/5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM), and Landsat 8 Operational Land Imager (OLI) imagery to derive built-up areas and quantify their expansion over time in relation to fourteen distance explanatory factors: i.e. previous built-up areas, main road infrastructure, Bucharest city’s boundary, location of the urban centres classified according to demographic size and main economic function, forest land and water bodies. To estimate the influence of the predictors, the binary logistic regression was applied. Furthermore, to estimate the effectiveness of the predictor set in the variation of built-up areas expansion, the pseudo R2 was calculated and discussed. Moreover, to understand the future potential trend of urban sprawl and its spatial pattern, the probability maps were generated by integrating the regression coefficients of the statistically significant predictors into the spatial modeling. For the results performance assessment, the statistic Receiver Operating Characteristic and the pixel-based comparison between the real and predicted data were used. To assess possible differences at spatial and temporal scale, the analysis was carried out at regional level, for two periods: 1990–2002 and 2002–2018. In general, our findings show inverse relationship between the distance driving factors and built-up areas expansion, but the estimated predictive power suggests important disparities within the study area over the analysed periods. Overall, the statistical analysis indicate that the distance to previous build-up areas, distance to road infrastructure, distance to Bucharest and other large urban centres, and distance to urban centres with dominant industrial and service functions were more influential to urban sprawl after 1990. Furthermore, the predicted spatial data shows the highest potential of urban sprawl in the future around Bucharest, in the proximity of existing built-up areas and road infrastructure. Because of its predictive character, the present study is to be a useful tool for land managers and policy makers. Numéro de notice : A2022-777 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1967465 Date de publication en ligne : 23/08/2021 En ligne : https://doi.org/10.1080/10106049.2021.1967465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101832
in Geocarto international > vol 37 n° 24 [20/10/2022] . - pp 7220 - 7246[article]Comparison of change and static state as the dependent variable for modeling urban growth / Yongjiu Feng in Geocarto international, vol 37 n° 23 ([15/10/2022])
[article]
Titre : Comparison of change and static state as the dependent variable for modeling urban growth Type de document : Article/Communication Auteurs : Yongjiu Feng, Auteur ; Rong Wang, Auteur ; Xiaohua Tong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6975 - 6998 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] auto-régression
[Termes IGN] automate cellulaire
[Termes IGN] Chine
[Termes IGN] croissance urbaine
[Termes IGN] distribution spatiale
[Termes IGN] utilisation du sol
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) To examine the effects of historical land-use change and static land-use state on the modeling, we established three cellular automata (CA) models using the spatial autoregressive model (SAR). The models are CASAR-Cha based on the change data, CASAR-Sta based on the start-state data, and CASAR-End based on the end-state data. The models that considered five different neighborhood sizes (from 3 × 3 to 11 × 11) were applied to simulate the urban growth of Jiaxing, China from 2008 to 2018, and predict the urban scenario to the year 2048. All three models can accurately reproduce the urban growth from 2008 to 2018, and the CASAR-End model performed best in calibration and validation. The differences in historical land data did affect the spatial distribution of the simulated urban patterns. The neighborhood size has a significant impact on the model's allocation ability, yet the appropriate size depends on the unique landscape context being studied. Numéro de notice : A2022-752 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1959657 Date de publication en ligne : 02/08/2021 En ligne : https://doi.org/10.1080/10106049.2021.1959657 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101744
in Geocarto international > vol 37 n° 23 [15/10/2022] . - pp 6975 - 6998[article]A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas / Hossein Pourazar in Geocarto international, vol 37 n° 23 ([15/10/2022])
[article]
Titre : A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas Type de document : Article/Communication Auteurs : Hossein Pourazar, Auteur ; Farhad Samadzadegan, Auteur ; Farzaneh Dadrass Javan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6695 - 6712 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] alignement des données
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] modèle numérique de surface
[Termes IGN] orthophotoplan numérique
[Termes IGN] zone urbaineRésumé : (auteur) In this paper, a deep convolutional neural network (CNN) is developed to classify the Unmanned Aerial Vehicle (UAV) derived multispectral imagery and normalized digital surface model (DSM) data in urban areas. For this purpose, a multi-input deep CNN (MIDCNN) architecture is designed using 11 parallel CNNs; 10 deep CNNs to extract the features from all possible triple combinations of spectral bands as well as one deep CNN dedicated to the normalized DSM data. The proposed method is compared with the traditional single-input (SI) and double-input (DI) deep CNN designations and random forest (RF) classifier, and evaluated using two independent test datasets. The results indicate that increasing the CNN layers parallelly augmented the classifier’s generalization and reduced overfitting risk. The overall accuracy and kappa value of the proposed method are 95% and 0.93, respectively, for the first test dataset, and 96% and 0.94, respectively, for the second test data set. Numéro de notice : A2022-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1959655 Date de publication en ligne : 04/08/2021 En ligne : https://doi.org/10.1080/10106049.2021.1959655 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101741
in Geocarto international > vol 37 n° 23 [15/10/2022] . - pp 6695 - 6712[article]Modelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model / Santanu Dinda in Geocarto international, vol 37 n° 22 ([10/10/2022])PermalinkComparison of layer-stacking and Dempster-Shafer theory-based methods using Sentinel-1 and Sentinel-2 data fusion in urban land cover mapping / Dang Hung Bui in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkEstimating urban functional distributions with semantics preserved POI embedding / Weiming Huang in International journal of geographical information science IJGIS, vol 36 n° 10 (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)PermalinkMachine learning for spatial analyses in urban areas: a scoping review / Ylenia Casali in Sustainable Cities and Society, vol 85 (October 2022)PermalinkPredicting the variability in pedestrian travel rates and times using crowdsourced GPS data / Michael J. Campbell in Computers, Environment and Urban Systems, vol 97 (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)PermalinkDevelopment of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood / Amid Darabi in Geocarto international, vol 37 n° 19 ([15/09/2022])PermalinkDeep 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)Permalink