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GIS-based logic scoring of preference method for urban densification suitability analysis / Shuoge Shen in Computers, Environment and Urban Systems, vol 89 (September 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 89 (September 2021) . - n° 101654 Titre : GIS-based logic scoring of preference method for urban densification suitability analysis Type de document : Article/Communication Auteurs : Shuoge Shen, Auteur ; Suzana Dragićević, Auteur ; Jozo Dujmović, Auteur Année de publication : 2021 Article en page(s) : n° 101654 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aide à la décision [Termes IGN] analyse multicritère [Termes IGN] croissance urbaine [Termes IGN] démographie [Termes IGN] densité de population [Termes IGN] planification urbaine [Termes IGN] système d'information géographique [Termes IGN] Vancouver (Colombie britannique) [Termes IGN] zone urbaine Résumé : (auteur) Urban Densification Suitability Analysis (UDSA) approach has been proposed using the GIS-based Logic Scoring of Preference (LSP) decision method. Our goal is to provide the methodology for the evaluation of suitability of locations for high density urban development to facilitate the spatial decision-making process and help justifiable urban planning. The main objectives of this research study are (1) to implement the GIS-based LSP method for high density urban growth suitability analysis, and (2) to perform an overall integrated suitability analysis for two key stakeholders, urban developer and urban planner. The LSP method is based on soft computing and used in the decision-making process to integrate large number of attribute criteria in a way that is consistent with human logic reasoning. The GIS-based LSP method is implemented using geospatial data for Metro Vancouver Region, Canada. Main criteria used to characterize the urban densification include recreation, transportation, existing development, economy, terrain, restrictions, and demography. The obtained results provide the distributions of suitability values and indicate that the opinions of two stakeholders differ. They select different locations as the most suitable for urban densifications and rather agree on the most unsuitable locations. The overall integrated suitability analysis has been performed with scenarios of possible decision-making outcomes between the two stakeholders. We introduce the concept of suitability agreement maps along with urban densification decision criteria. The analysis indicates that the GIS-based LSP method is a useful tool for measuring the similarities and differences in stakeholders' views and facilitating decision-making process for urban densification. Numéro de notice : A2021-534 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101654 date de publication en ligne : 07/06/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101654 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97996 [article]

Improving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery / Bin Hu in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 8 (August 2021) . - n° 533 Titre : Improving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery Type de document : Article/Communication Auteurs : Bin Hu, Auteur ; Yongyang Xu, Auteur ; Xiao Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 533 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Chine [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] fusion de données [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] occupation du sol [Termes IGN] planification urbaine [Termes IGN] polarisation [Termes IGN] zone urbaine Résumé : (auteur) Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery. Numéro de notice : A2021-590 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080533 date de publication en ligne : 09/08/2021 En ligne : https://doi.org/10.3390/ijgi10080533 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98210 [article]

Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 203 - 218 Titre : Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America Type de document : Article/Communication Auteurs : Bin Chen, Auteur ; Ying Tu, Auteur ; Yimeng Song, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 203 - 218 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algorithme d'apprentissage [Termes IGN] carte d'utilisation du sol [Termes IGN] données massives [Termes IGN] données multisources [Termes IGN] Etats-Unis [Termes IGN] extraction de traits caractéristiques [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] métropole [Termes IGN] OpenStreetMap [Termes IGN] planification urbaine [Termes IGN] zone urbaine Résumé : (auteur) Urban land-use maps outlining the distribution, pattern, and composition of various land use types are critically important for urban planning, environmental management, disaster control, health protection, and biodiversity conservation. Recent advances in remote sensing and social sensing data and methods have shown great potentials in mapping urban land use categories, but they are still constrained by mixed land uses, limited predictors, non-localized models, and often relatively low accuracies. To inform these issues, we proposed a robust and cost-effective framework for mapping urban land use categories using openly available multi-source geospatial “big data”. With street blocks generated from OpenStreetMap (OSM) data as the minimum classification unit, we integrated an expansive set of multi-scale spatially explicit information on land surface, vertical height, socio-economic attributes, social media, demography, and topography. We further proposed to apply the automatic ensemble learning that leverages a bunch of machine learning algorithms in deriving optimal urban land use classification maps. Results of block-level urban land use classification in five metropolitan areas of the United States found the overall accuracies of major-class (Level-I) and minor-class (Level-II) classification could be high as 91% and 86%, respectively. A multi-model comparison revealed that for urban land use classification with high-dimensional features, the multi-layer stacking ensemble models achieved better performance than base models such as random forest, extremely randomized trees, LightGBM, CatBoost, and neural networks. We found without very-high-resolution National Agriculture Imagery Program imagery, the classification results derived from Sentinel-1, Sentinel-2, and other open big data based features could achieve plausible overall accuracies of Level-I and Level-II classification at 88% and 81%, respectively. We also found that model transferability depended highly on the heterogeneity in characteristics of different regions. The methods and findings in this study systematically elucidate the role of data sources, classification methods, and feature transferability in block-level land use classifications, which have important implications for mapping multi-scale essential urban land use categories. Numéro de notice : A2021-564 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.06.010 date de publication en ligne : 25/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.06.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98129 [article]

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Flood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 88 (July 2021) . - n° 101628 Titre : Flood depth mapping in street photos with image processing and deep neural networks Type de document : Article/Communication Auteurs : Bahareh Alizadeh Kharazi, Auteur ; Amir H. Behzadan, Auteur Année de publication : 2021 Article en page(s) : n° 101628 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] Canada [Termes IGN] centre urbain [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] crue [Termes IGN] détection de contours [Termes IGN] Etats-Unis [Termes IGN] image Streetview [Termes IGN] inondation [Termes IGN] profondeur [Termes IGN] signalisation routière [Termes IGN] système d'aide à la décision [Termes IGN] traitement d'image [Termes IGN] transformation de Hough [Termes IGN] zone urbaine Résumé : (auteur) Many parts of the world experience severe episodes of flooding every year. In addition to the high cost of mitigation and damage to property, floods make roads impassable and hamper community evacuation, movement of goods and services, and rescue missions. Knowing the depth of floodwater is critical to the success of response and recovery operations that follow. However, flood mapping especially in urban areas using traditional methods such as remote sensing and digital elevation models (DEMs) yields large errors due to reshaped surface topography and microtopographic variations combined with vegetation bias. This paper presents a deep neural network approach to detect submerged stop signs in photos taken from flooded roads and intersections, coupled with Canny edge detection and probabilistic Hough transform to calculate pole length and estimate floodwater depth. Additionally, a tilt correction technique is implemented to address the problem of sideways tilt in visual analysis of submerged stop signs. An in-house dataset, named BluPix 2020.1 consisting of paired web-mined photos of submerged stop signs across 10 FEMA regions (for U.S. locations) and Canada is used to evaluate the models. Overall, pole length is estimated with an RMSE of 17.43 and 8.61 in. in pre- and post-flood photos, respectively, leading to a mean absolute error of 12.63 in. in floodwater depth estimation. Findings of this research are sought to equip jurisdictions, local governments, and citizens in flood-prone regions with a simple, reliable, and scalable solution that can provide (near-) real time estimation of floodwater depth in their surroundings. Numéro de notice : A2021-358 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101628 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97620 [article]

Road-network-based fast geolocalization / Yongfei Li in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 6065 - 6076 Titre : Road-network-based fast geolocalization Type de document : Article/Communication Auteurs : Yongfei Li, Auteur ; Dongfang Yang, Auteur ; Shicheng Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6065 - 6076 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carrefour [Termes IGN] carte routière [Termes IGN] cohérence [Termes IGN] enregistrement de données [Termes IGN] géolocalisation [Termes IGN] image aérienne [Termes IGN] réseau routier [Termes IGN] segmentation d'image [Termes IGN] transformation homographique [Termes IGN] zone urbaine Résumé : (auteur) In this article, a road-network-based geolocalization method is proposed. We match roads in the onboard images to the reference road vector map, and realize successful localization over areas as large as a whole city. The road network matching problem is treated as a point cloud registration problem under the homography transformation and solved under the hypothesize-and-test framework. To tackle the point cloud registration problem, a global projective-invariant feature is proposed, which consists of two road intersections augmented with their tangents. In addition, we propose the necessary conditions for the features to match. This can reduce the candidate matching features, thus accelerating the search to a great extent. These matching candidates are first “filtered” with the model consistency check in parameter space and then tested with similarity metrics to identify the correct transformation. The experiments show that our method can localize an aerial image over an area larger than 1000 km 2 within several seconds on a single CPU. Our code can be found at: https://github.com/FlyAlCode/RCLGeolocalization-2.0 . Numéro de notice : A2021-532 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3011034 date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3011034 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97989 [article]

Roadside tree extraction and diameter estimation with MMS lidar by using point-cloud image / Genki Takahashi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2-2021 (July 2021)  

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Fast unsupervised multi-scale characterization of urban landscapes based on Earth observation data / Claire Teillet in Remote sensing, vol 13 n° 12 (June-2 2021)  

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A framework for classification of volunteered geographic data based on user’s need / Nazila Mohammadi in Geocarto international, vol 36 n° 11 ([15/06/2021])  

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Crowdsourcing of popular toponyms: How to collect and preserve toponyms in spoken use / Daniel Vrbik in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)  

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Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery / Neema Nicodemus Lyimo in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)  

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Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping / Jiadi Yin in Remote sensing, vol 13 n° 8 (April-2 2021)  

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Detecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network / Nantheera Anantrasirichai in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks / Bozhao Li in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)  

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Urban heat island formation in greater Cairo: Spatio-temporal analysis of daytime and nighttime land surface temperatures along the urban–rural gradient / Darshana Athukorala in Remote sensing, vol 13 n° 7 (April 2021)  

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Characterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)  

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