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Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data / Haijian Liu in Remote sensing of environment, vol 258 (June 2021)  

Public [article]  inRemote sensing of environment > vol 258 (June 2021) . - n° 112382 Titre : Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data Type de document : Article/Communication Auteurs : Haijian Liu, Auteur ; Pinliang Dong, Auteur ; Changshan Wu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112382 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] arbre (flore) [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] houppier [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] peuplement mélangé [Termes IGN] semis de points [Termes IGN] surveillance forestière [Termes IGN] Wisconsin (Etats-Unis) Résumé : (auteur) Individual tree identification is a key step for forest surveying and monitoring. To identify individual trees with airborne LiDAR data, a local maximum (LM) filter technique is typically performed. With LM, the highest point in a filtering window is generally considered to represent the tree position. This assumption, however, has great limitations, especially for mixed forests. To address this problem, we developed a new approach, the cluster center of higher points (CCHP), for tree position detection with LiDAR data. CCHP assumes that a tree position is located at the clustering center of higher points within a spatial neighborhood, and the center can be detected by a location-based recursive algorithm. The developed CCHP method was applied to a simulated forest and then verified in two real urban forests. In comparison with the variable window-sized LM filter method and layer stacking method, CCHP successfully identified 97% of trees in the simulated forest, while only 78% and 81% of the trees were recognized by LM and layer stacking methods respectively. The average absolute and relative offsets of CCHP are 0.33 m and 6.59%, respectively, and over 80% of the detected trees have an offset of less than 10% of the tree crown radius. CCHP also correctly detected 93.80% and 88.74% of individual trees in the first and second real forests, respectively, but the detection rates from the variable window-sized LM approach and layer stacking were less than 80%. In addition, the tree positions located by CCHP are considerably more accurate than the other two methods. Therefore, CCHP is proven to be promising for detecting individual tree positions from airborne LiDAR data for both simulated and real forests. Numéro de notice : A2021-443 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112382 date de publication en ligne : 06/03/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112382 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97850 [article]

Provisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change / Debojyoti Chakraborty in Annals of Forest Science [en ligne], vol 78 n° 2 (June 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 2 (June 2021) . - Article 26 Titre : Provisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change Type de document : Article/Communication Auteurs : Debojyoti Chakraborty, Auteur ; Norbert Móricz, Auteur ; Ervin Rasztovits, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : Article 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement [Termes IGN] carte forestière [Termes IGN] changement climatique [Termes IGN] conservation des ressources forestières [Termes IGN] espèce végétale [Termes IGN] Europe (géographie politique) [Termes IGN] Fagus sylvatica [Termes IGN] Larix decidua [Termes IGN] outil d'aide à la décision [Termes IGN] peuplement forestier [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] Quercus pedunculata [Termes IGN] Quercus sessiliflora [Termes IGN] vulnérabilité [Vedettes matières IGN] Ecologie forestière Résumé : (Auteur) We developed a dataset of the potential distribution of seven ecologically and economically important tree species of Europe in terms of their climatic suitability with an ensemble approach while accounting for uncertainty due to model algorithms. The dataset was documented following the ODMAP protocol to ensure reproducibility. Our maps are input data in a decision support tool “SusSelect” which predicts the vulnerability of forest trees in climate change and recommends adapted planting material. Numéro de notice : A2021-329 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01029-4 date de publication en ligne : 22/03/2021 En ligne : https://doi.org/10.1007/s13595-021-01029-4 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97490 [article]

A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 4 (April 2021) . - pp 639 - 660 Titre : A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media Type de document : Article/Communication Auteurs : Yi Bao, Auteur ; Zhou Huang, Auteur ; Linna Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 639 - 660 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données spatiotemporelles [Termes IGN] géopositionnement [Termes IGN] graphe [Termes IGN] modèle de simulation [Termes IGN] point d'intérêt [Termes IGN] réseau social [Termes IGN] service fondé sur la position [Termes IGN] utilisateur [Termes IGN] Wuhan (Chine) Résumé : (auteur) Location prediction based on spatio-temporal footprints in social media is instrumental to various applications, such as travel behavior studies, crowd detection, traffic control, and location-based service recommendation. In this study, we propose a model that uses geotags of social media to predict the potential area containing users’ next locations. In the model, we utilize HiSpatialCluster algorithm to identify clustering areas (CAs) from check-in points. CA is the basic spatial unit for predicting the potential area containing users’ next locations. Then, we use the LINE (Large-scale Information Network Embedding) to obtain the representation vector of each CA. Finally, we apply BiLSTM-CNN (Bidirectional Long Short-Term Memory-Convolutional Neural Network) for location prediction. The results show that the proposed ensemble model outperforms the single LSTM or CNN model. In the case study that identifies 100 CAs out of Weibo check-ins collected in Wuhan, China, the Top-5 predicted areas containing next locations amount to an 80% accuracy. The high accuracy is of great value for recommendation and prediction on areal unit. Numéro de notice : A2021-268 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808896 date de publication en ligne : 26/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808896 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97324 [article]

Geovisualization of COVID-19: State of the art and opportunities / Yu Lan in Cartographica, vol 56 n° 1 (Spring 2021)  

Public [article]  inCartographica > vol 56 n° 1 (Spring 2021) . - pp 2 - 13 Titre : Geovisualization of COVID-19: State of the art and opportunities Type de document : Article/Communication Auteurs : Yu Lan, Auteur ; Michael R. Desjardins, Auteur ; Alexander Hohl, Auteur ; Eric Delmelle, Auteur Année de publication : 2021 Article en page(s) : pp 2 - 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement [Termes IGN] carte interactive [Termes IGN] carte thématique [Termes IGN] cube espace-temps [Termes IGN] données spatiotemporelles [Termes IGN] Etats-Unis [Termes IGN] maladie virale [Termes IGN] modèle dynamique [Termes IGN] variation saisonnière [Termes IGN] WebSIG [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Mapping the prevalence and spread of infectious diseases has never been more critical than during the COVID-19 pandemic. A plethora of Web-based GIS dashboards have been created that incorporate basic GIS functionality; these dashboards have served as platforms for rapid data sharing and real-time information, ultimately facilitating decision making. However, many of them have merely focused on presenting and monitoring cumulative or daily incidence of COVID-19 data, disregarding the temporal dimension. In this paper, we review the usefulness of GIS-based dashboards for mapping the prevalence of COVID-19, but also missed opportunities to emphasize the temporal component of the disease (cyclicity, seasonality). We suggest that advanced geovisualization techniques can be used to integrate the temporal component in interactive animated maps illustrating (a) the daily relative risk and the number of days a geographic region has been in a disease cluster, (b) the ratio between the observed and expected number of cases over time, and (c) mortality count dynamics in a space–time cube. We illustrate these approaches by using COVID-19 cases and death counts across the U.S. at the county level from 25 January 2020 to 1 October 2020. We discuss how each of these visualization approaches can promote the understanding of important public health concepts applied to the pandemic such as risk, spread, and mortality. Finally, we suggest future avenues to promote research at the intersection of space–time visualization and infectious diseases. Numéro de notice : A2021-409 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart-2020-0027 date de publication en ligne : 15/03/2021 En ligne : https://doi.org/10.3138/cart-2020-0027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97730 [article]

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Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3266 - 3276 Titre : Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework Type de document : Article/Communication Auteurs : Peyman Azimpour, Auteur ; Tahereh Bahraini, Auteur ; Hadi Sadoghi Yazdi, Auteur Année de publication : 2021 Article en page(s) : pp 3266 - 3276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] caractéristique spatio-spectrale [Termes IGN] classification bayesienne [Termes IGN] classification floue [Termes IGN] distribution de Gauss [Termes IGN] factorisation de matrice non-négative [Termes IGN] filtrage du bruit [Termes IGN] filtre de Gauss [Termes IGN] image hyperspectrale [Termes IGN] Matlab [Termes IGN] processeur graphique [Termes IGN] qualité des données [Termes IGN] variable Résumé : (auteur) The hyperspectral-image (HSI) noise-reduction step is a very significant preprocessing phase of data-quality enhancement. It has been attracting immense research attention in the remote sensing and image processing domains. Many methods have been developed for HSI restoration, the goal of which is to remove noise from the whole HSI cube simultaneously without considering the spectral–spatial similarity. When a noise-removal algorithm is used globally to the entire data set, it would not eliminate all levels of noise, effectively. Furthermore, most of the existing methods remove independent and identically distributed (i.i.d.) Gaussian noise. The real scenarios are much more complicated than this assumption. The complexity created by natural noise that has a non-i.i.d. structure leads to inefficient methods containing underestimation and invalid performance. In this article, we calculated the spatial–spectral similarity criteria by defining a set of clustering-based latent variables (CLVs) in a Bayesian framework to improve the robustness. These criteria can be extracted using the clustering operators. Then, by applying the CLV to the variational Bayesian model, we investigated a new low-rank matrix factorization denoising approach based on the proposed clustering-based latent variable (CLV-LRMF) to remove noise with the non-i.i.d. mixture of Gaussian structures. Finally, we switched to the GPU for MATLAB implementation to reduce the runtime. The experimental results show that the performance has been improved by applying the proposed CLV and demonstrate the effectiveness of the proposed CLV-LRMF over other state-of-the-art methods. Numéro de notice : A2021-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2939512 date de publication en ligne : 24/03/2021 En ligne : https://doi.org/10.1109/TGRS.2019.2939512 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97396 [article]

Utilizing urban geospatial data to understand heritage attractiveness in Amsterdam / Sevim Sezi Karayazi in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)  

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Recognition of varying size scene images using semantic analysis of deep activation maps / Shikha Gupta in Machine Vision and Applications, vol 32 n° 2 (March 2021)  

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A heuristic approach to the generalization of complex building groups in urban villages / Wenhao Yu in Geocarto international, vol 36 n° 2 ([01/02/2021])  

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Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Topoclimatic zoning of continental Chile / Donna Cortez in Journal of maps, Vol 17 n° 2 (February 2021)  

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Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs / Yang Bai in Computers & geosciences, vol 146 (January 2021)  

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Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation / George Grekousis in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)  

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Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data / Yi Qiang in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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Group diagrams for representing trajectories / Maike Buchin in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities / Chao Wang in Transactions in GIS, Vol 24 n° 6 (December 2020)  

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