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Identification of urban agglomeration spatial range based on social and remote-sensing data - For evaluating development level of urban agglomerations / Shuai Zhang in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 456 Titre : Identification of urban agglomeration spatial range based on social and remote-sensing data - For evaluating development level of urban agglomerations Type de document : Article/Communication Auteurs : Shuai Zhang, Auteur ; Hua Wei, Auteur Année de publication : 2022 Article en page(s) : n° 456 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] agglomération [Termes IGN] analyse spatiale [Termes IGN] Chine [Termes IGN] croissance urbaine [Termes IGN] données massives [Termes IGN] données socio-économiques [Termes IGN] éclairage public [Termes IGN] fusion de données [Termes IGN] image NPP-VIIRS [Termes IGN] point d'intérêt [Termes IGN] prise de vue nocturne [Termes IGN] segmentation d'image [Termes IGN] transformation en ondelettes Résumé : (auteur) The accurate identification of urban agglomeration spatial area is helpful in understanding the internal spatial relationship under urban expansion and in evaluating the development level of urban agglomeration. Previous studies on the identification of spatial areas often ignore the functional distribution and development of urban agglomerations by only using nighttime light data (NTL). In this study, a new method is firstly proposed to identify the accurate spatial area of urban agglomerations by fusing night light data (NTL) and point of interest data (POI); then an object-oriented method is used by this study to identify the spatial area, finally the identification results obtained by different data are verified. The results show that the accuracy identified by NTL data is 82.90% with the Kappa coefficient of 0.6563, the accuracy identified by POI data is 81.90% with the Kappa coefficient of 0.6441, and the accuracy after data fusion is 90.70%, with the Kappa coefficient of 0.8123. The fusion of these two kinds of data has higher accuracy in identifying the spatial area of urban agglomeration, which can play a more important role in evaluating the development level of urban agglomeration; this study proposes a feasible method and path for urban agglomeration spatial area identification, which is not only helpful to optimize the spatial structure of urban agglomeration, but also to formulate the spatial development policy of urban agglomeration. Numéro de notice : A2022-645 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080456 Date de publication en ligne : 21/08/2022 En ligne : https://doi.org/10.3390/ijgi11080456 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101461 [article]

Measuring COVID-19 vulnerability for Northeast Brazilian municipalities: Social, economic, and demographic factors based on multiple criteria and spatial analysis / Ciro José Jardim De Figueiredo in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 449 Titre : Measuring COVID-19 vulnerability for Northeast Brazilian municipalities: Social, economic, and demographic factors based on multiple criteria and spatial analysis Type de document : Article/Communication Auteurs : Ciro José Jardim De Figueiredo, Auteur ; Caroline Maria de Miranda Mota, Auteur ; Kaliane Gabriele Dias de Araújo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 449 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse multicritère [Termes IGN] autocorrélation spatiale [Termes IGN] Brésil [Termes IGN] densité de population [Termes IGN] données socio-économiques [Termes IGN] épidémie [Termes IGN] maladie virale [Termes IGN] vulnérabilité Résumé : (auteur) COVID-19 has brought several harmful consequences to the world from many perspectives, including social, economic, and well-being in addition to health issues. However, these harmful consequences vary in intensity in different regions. Identifying which cities are most vulnerable to COVID-19 and understanding which variables could be associated with the advance of registered cases is a challenge. Therefore, this study explores and builds a spatial decision model to identify the characteristics of the cities that are most vulnerable to COVID-19, taking into account social, economic, demographic, and territorial aspects. Hence, 18 features were separated into the four groups mentioned. We employed a model joining the dominance-based rough set approach to aggregate the features (multiple criteria) and spatial analysis (Moran index, and Getis and Ord) to obtain final results. The results show that the most vulnerable places have characteristics with high population density and poor economic conditions. In addition, we conducted subsequent analysis to validate the results. The case was developed in the northeast region of Brazil. Numéro de notice : A2022-646 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080449 Date de publication en ligne : 16/08/2022 En ligne : https://doi.org/10.3390/ijgi11080449 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101462 [article]

Using attributes explicitly reflecting user preference in a self-attention network for next POI recommendation / Ruijing Li in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 440 Titre : Using attributes explicitly reflecting user preference in a self-attention network for next POI recommendation Type de document : Article/Communication Auteurs : Ruijing Li, Auteur ; Jianzhong Guo, Auteur ; Chun Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 440 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] distance [Termes IGN] filtrage d'information [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] point d'intérêt [Termes IGN] réseau social géodépendant [Termes IGN] Tokyo (Japon) Résumé : (auteur) With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user’s work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user’s decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average. Numéro de notice : A2022-647 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080440 Date de publication en ligne : 04/08/2022 En ligne : https://doi.org/10.3390/ijgi11080440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101463 [article]

Integrating post-processing kinematic (PPK) structure-from-motion (SfM) with unmanned aerial vehicle (UAV) photogrammetry and digital field mapping for structural geological analysis / Daniele Cirillo in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 437 Titre : Integrating post-processing kinematic (PPK) structure-from-motion (SfM) with unmanned aerial vehicle (UAV) photogrammetry and digital field mapping for structural geological analysis Type de document : Article/Communication Auteurs : Daniele Cirillo, Auteur ; Francesca Cerritelli, Auteur ; Silvano Agostini, Auteur Année de publication : 2022 Article en page(s) : n° 437 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Apennins [Termes IGN] carte géologique [Termes IGN] déformation de la croute terrestre [Termes IGN] géologie [Termes IGN] image captée par drone [Termes IGN] Italie [Termes IGN] modèle numérique de surface [Termes IGN] modélisation 3D [Termes IGN] point d'appui [Termes IGN] positionnement cinématique en temps réel [Termes IGN] post-traitement [Termes IGN] structure-from-motion Résumé : (auteur) We studied some exposures of the Roccacaramanico Conglomerate (RCC), a calcareous-clastic mega-bed intercalated within the Late Messinian–Early Pliocene pelitic succession of the La Queglia and Maiella tectonic units (central Apennines). The outcrops, localized in the overturned limb of a kilometric-scale syncline, show a complex array of fractures, including multiple systems of closely spaced cleavages, joints, and mesoscopic faults, which record the progressive deformation associated with the Late Pliocene thrusting. Due to the extent of the investigated sites and a large amount of data to collect, we applied a multi-methodology survey technique integrating unmanned aerial vehicle (UAV) technologies and digital mapping in the field. We reconstructed the 3D digital outcrop model of the RCC in the type area and defined the 3D pattern of fractures and their time–space relationships. The field survey played a pivotal role in determining the various sets of structures, their kinematics, the associated displacements, and relative chronology. The results unveiled the investigated area’s tectonic evolution and provide a deformation model that could be generalized in similar tectonic contexts. Furthermore, the methodology allows for evaluating the reliability of the applied remote survey techniques (i.e., using UAV) compared to those based on the direct measurements of structures using classic devices. Our purpose was to demonstrate that our multi-methodology approach can describe the tectonic evolution of the study area, providing consistent 3D data and using a few ground control points. Finally, we propose two alternative working methods and discuss their different fields of application. Numéro de notice : A2022-648 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080437 Date de publication en ligne : 02/08/2022 En ligne : https://doi.org/10.3390/ijgi11080437 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101464 [article]

The influence of data density and integration on forest canopy cover mapping using Sentinel-1 and Sentinel-2 time series in Mediterranean oak forests / Vahid Nasiri in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 423 Titre : The influence of data density and integration on forest canopy cover mapping using Sentinel-1 and Sentinel-2 time series in Mediterranean oak forests Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Seyed Mohammad Moein Sadeghi, Auteur ; Fardin Moradi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] canopée [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] couvert forestier [Termes IGN] forêt méditerranéenne [Termes IGN] Google Earth Engine [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Iran [Termes IGN] placette d'échantillonnage [Termes IGN] Quercus (genre) Résumé : (auteur) Forest canopy cover (FCC) is one of the most important forest inventory parameters and plays a critical role in evaluating forest functions. This study examines the potential of integrating Sentinel-1 (S-1) and Sentinel-2 (S-2) data to map FCC in the heterogeneous Mediterranean oak forests of western Iran in different data densities (one-year datasets vs. three-year datasets). This study used very high-resolution satellite images from Google Earth, gridded points, and field inventory plots to generate a reference dataset. Based on it, four FCC classes were defined, namely non-forest, sparse forest (FCC = 1–30%), medium-density forest (FCC = 31–60%), and dense forest (FCC > 60%). In this study, three machine learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), were used in the Google Earth Engine and their performance was compared for classification. Results showed that the SVM produced the highest accuracy on FCC mapping. The three-year time series increased the ability of all ML models to classify FCC classes, in particular the sparse forest class, which was not distinguished well by the one-year dataset. Class-level accuracy assessment results showed a remarkable increase in F-1 scores for sparse forest classification by integrating S-1 and S-2 (10.4% to 18.2% increased for the CART and SVM ML models, respectively). In conclusion, the synergetic use of S-1 and S-2 spectral temporal metrics improved the classification accuracy compared to that obtained using only S-2. The study relied on open data and freely available tools and can be integrated into national monitoring systems of FCC in Mediterranean oak forests of Iran and neighboring countries with similar forest attributes. Numéro de notice : A2022-649 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080423 Date de publication en ligne : 26/07/2022 En ligne : https://doi.org/10.3390/ijgi11080423 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101465 [article]