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Application of GIS tools in the measurement analysis of urban spatial layouts using the square grid method / Łukasz Musiaka 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° 558 Titre : Application of GIS tools in the measurement analysis of urban spatial layouts using the square grid method Type de document : Article/Communication Auteurs : Łukasz Musiaka, Auteur ; Marta Nalej, Auteur Année de publication : 2021 Article en page(s) : n° 558 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] historique des données [Termes IGN] maille carrée [Termes IGN] morphologie urbaine [Termes IGN] morphométrie [Termes IGN] plan de ville [Termes IGN] planification urbaine [Termes IGN] Pologne [Termes IGN] système d'information géographique [Termes IGN] urbanisme Résumé : (auteur) The principal aim of this paper is to present the capabilities of newly developed GIS tools for measurement analysis of urban spatial layouts, using the square grid method. The study of urban morphology and metrology is a multistage process, which involves the metrological analysis of town plans. The main research step is the determination of measurement modules of town layouts, using the square grid. By using GIS software, the authors developed a new tool, named HGIS Tools, which allow to create any number of modular grids with the selected cell size that corresponds to urban units of distance and surface area. When investigating a large number of towns and cities, this offers a significant improvement of the research procedure. The paper presents a test of the tool’s potential on the example of regular medieval towns from the area of the former Teutonic Order state (currently the territory of Poland), diversified in terms of size, genesis and morphometrics. The obtained results confirmed that HGIS Tools allowed to determine the hypothetical measurement module of the layout of the cities studied. The results were consistent with the analyses of other authors carried out with the traditional grid-square methods. The test of the HGIS Tools showed their significant potential in conducting morphometric analyses of spatial arrangements of urban spatial layout on a larger scale. Numéro de notice : A2021-587 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080558 En ligne : https://doi.org/10.3390/ijgi10080558 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98205 [article]

Predicting user activity intensity using geographic interactions based on social media check-in data / Jing Li 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° 555 Titre : Predicting user activity intensity using geographic interactions based on social media check-in data Type de document : Article/Communication Auteurs : Jing Li, Auteur ; Wenyue Guo, Auteur ; Haiyan Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 555 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] données issues des réseaux sociaux [Termes IGN] interaction spatiale [Termes IGN] mobilité humaine [Termes IGN] modèle non linéaire [Termes IGN] relation spatiale [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau neuronal récurrent [Termes IGN] utilisateur Résumé : (auteur) Predicting user activity intensity is crucial for various applications. However, existing studies have two main problems. First, as user activity intensity is nonstationary and nonlinear, traditional methods can hardly fit the nonlinear spatio-temporal relationships that characterize user mobility. Second, user movements between different areas are valuable, but have not been utilized for the construction of spatial relationships. Therefore, we propose a deep learning model, the geographical interactions-weighted graph convolutional network-gated recurrent unit (GGCN-GRU), which is good at fitting nonlinear spatio-temporal relationships and incorporates users’ geographic interactions to construct spatial relationships in the form of graphs as the input. The model consists of a graph convolutional network (GCN) and a gated recurrent unit (GRU). The GCN, which is efficient at processing graphs, extracts spatial features. These features are then input into the GRU, which extracts their temporal features. Finally, the GRU output is passed through a fully connected layer to obtain the predictions. We validated this model using a social media check-in dataset and found that the geographical interactions graph construction method performs better than the baselines. This indicates that our model is appropriate for fitting the complex nonlinear spatio-temporal relationships that characterize user mobility and helps improve prediction accuracy when considering geographic flows. Numéro de notice : A2021-588 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080555 Date de publication en ligne : 17/08/2021 En ligne : https://doi.org/10.3390/ijgi10080555 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98206 [article]

Vehicle detection in very-high-resolution remote sensing images based on an anchor-free detection model with a more precise foveal area / Xungen Li 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° 549 Titre : Vehicle detection in very-high-resolution remote sensing images based on an anchor-free detection model with a more precise foveal area Type de document : Article/Communication Auteurs : Xungen Li, Auteur ; Feifei Men, Auteur ; Shuaishuai Lv, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 549 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de cible [Termes IGN] image à très haute résolution [Termes IGN] image aérienne [Termes IGN] véhicule Résumé : (auteur) Vehicle detection in aerial images is a challenging task. The complexity of the background information and the redundancy of the detection area are the main obstacles that limit the successful operation of vehicle detection based on anchors in very-high-resolution (VHR) remote sensing images. In this paper, an anchor-free target detection method is proposed to solve the problems above. First, a multi-attention feature pyramid network (MA-FPN) was designed to address the influence of noise and background information on vehicle target detection by fusing attention information in the feature pyramid network (FPN) structure. Second, a more precise foveal area (MPFA) is proposed to provide better ground truth for the anchor-free method by determining a more accurate positive sample selection area. The proposed anchor-free model with MA-FPN and MPFA can predict vehicles accurately and quickly in VHR remote sensing images through direct regression and predict the pixels in the feature map. A detailed evaluation based on remote sensing image (RSI) and vehicle detection in aerial imagery (VEDAI) data sets for vehicle detection shows that our detection method performs well, the network is simple, and the detection is fast. Numéro de notice : A2021-589 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080549 Date de publication en ligne : 14/08/2021 En ligne : https://doi.org/10.3390/ijgi10080549 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98209 [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]

Random forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture / Pashrant K. Srivastava 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° 507 Titre : Random forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture Type de document : Article/Communication Auteurs : Pashrant K. Srivastava, Auteur ; George P. Petropoulos, Auteur ; Rajendra Prasad, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 507 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algorithme génétique [Termes IGN] Angleterre [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] ensachage [Termes IGN] humidité du sol [Termes IGN] image SMOS [Termes IGN] régression des moindres carrés partiels Résumé : (auteur) Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use. Numéro de notice : A2021-595 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080507 Date de publication en ligne : 27/07/2021 En ligne : https://doi.org/10.3390/ijgi10080507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98220 [article]