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Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling / Stefanos Georganos in Geocarto international, vol 36 n° 2 ([01/02/2021])
[article]
Titre : Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling Type de document : Article/Communication Auteurs : Stefanos Georganos, Auteur ; Tais Grippa, Auteur ; Assane Niang Gadiaga, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 121 -1 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] autocorrélation spatiale
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Dakar (Sénégal)
[Termes IGN] densité de population
[Termes IGN] distribution spatiale
[Termes IGN] hétérogénéité spatiale
[Termes IGN] modèle dynamique
[Termes IGN] population
[Termes IGN] utilisation du solRésumé : (auteur) Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditionally geographical topics such as population estimation. Even though RF is a well performing and generalizable algorithm, the vast majority of its implementations is still ‘aspatial’ and may not address spatial heterogenous processes. At the same time, remote sensing (RS) data which are commonly used to model population can be highly spatially heterogeneous. From this scope, we present a novel geographical implementation of RF, named Geographical Random Forest (GRF) as both a predictive and exploratory tool to model population as a function of RS covariates. GRF is a disaggregation of RF into geographical space in the form of local sub-models. From the first empirical results, we conclude that GRF can be more predictive when an appropriate spatial scale is selected to model the data, with reduced residual autocorrelation and lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values. Finally, and of equal importance, GRF can be used as an effective exploratory tool to visualize the relationship between dependent and independent variables, highlighting interesting local variations and allowing for a better understanding of the processes that may be causing the observed spatial heterogeneity. Numéro de notice : A2021-080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595177 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96822
in Geocarto international > vol 36 n° 2 [01/02/2021] . - pp 121 -1 36[article]A density-based algorithm for the detection of individual trees from LiDAR data / Melissa Latella in Remote sensing, Vol 13 n° 2 (January-2 2021)
[article]
Titre : A density-based algorithm for the detection of individual trees from LiDAR data Type de document : Article/Communication Auteurs : Melissa Latella, Auteur ; Fabio Sola, Auteur ; Carlo Camporeal, Auteur Année de publication : 2021 Article en page(s) : n° 322 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] arbre (flore)
[Termes IGN] comptage
[Termes IGN] densité de la végétation
[Termes IGN] détection d'arbres
[Termes IGN] distribution spatiale
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt de feuillus
[Termes IGN] hauteur des arbres
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] semis de points
[Termes IGN] sous-étageRésumé : (auteur) Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management. Numéro de notice : A2021-196 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13020322 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.3390/rs13020322 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97146
in Remote sensing > Vol 13 n° 2 (January-2 2021) . - n° 322[article]Chinese tourists in Nordic countries: An analysis of spatio-temporal behavior using geo-located travel blog data / Yunhao Zheng in Computers, Environment and Urban Systems, vol 85 (January 2021)
[article]
Titre : Chinese tourists in Nordic countries: An analysis of spatio-temporal behavior using geo-located travel blog data Type de document : Article/Communication Auteurs : Yunhao Zheng, Auteur ; Naixia Mou, Auteur ; Lingxian Zhang, Auteur Année de publication : 2021 Article en page(s) : n° 101561 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] accès aux données localisées
[Termes IGN] analyse spatio-temporelle
[Termes IGN] aurore polaire
[Termes IGN] comportement
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] distribution spatiale
[Termes IGN] géomercatique
[Termes IGN] GeoWeb
[Termes IGN] ressources web
[Termes IGN] saison
[Termes IGN] Scandinavie
[Termes IGN] tourisme
[Termes IGN] voyage
[Termes IGN] zone boréaleRésumé : (auteur) Geo-located travel blogs, a new data source, enable to achieve more detailed analysis of tourists' spatio-temporal behavior. Taking Chinese tourists in Nordic countries as the research object, this paper focuses on their behavior, seasonal patterns and complex network effects by using geo-located travel blog data collected from Qunar.com. The results show that: (1) Chinese tourists visiting Nordic countries are often experienced in traveling. The local climate during the cold season does not prevent them from pursuing the aurora scenery. (2) The travel behavior of Chinese tourists is spatially heterogeneous. The network analysis reveals that Iceland showcases stronger, compared to the other Nordic countries, community independence and small world effect. (3) During the warm season, Chinese tourists choose a variety of destinations, while in cold season, they tend to choose destinations with higher chances for spotting the northern lights. These results provide helpful information for the tourism management departments of Nordic countries to improve their marketing and development efforts directed for Chinese tourists. Numéro de notice : A2021-006 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101561 Date de publication en ligne : 13/10/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101561 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96280
in Computers, Environment and Urban Systems > vol 85 (January 2021) . - n° 101561[article]LANet: Local attention embedding to improve the semantic segmentation of remote sensing images / Lei Ding in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
[article]
Titre : LANet: Local attention embedding to improve the semantic segmentation of remote sensing images Type de document : Article/Communication Auteurs : Lei Ding, Auteur ; Hao Tang, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2021 Article en page(s) : pp 426 - 435 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de données
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] décodage
[Termes IGN] distribution spatiale
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] segmentation sémantiqueRésumé : (auteur) The trade-off between feature representation power and spatial localization accuracy is crucial for the dense classification/semantic segmentation of remote sensing images (RSIs). High-level features extracted from the late layers of a neural network are rich in semantic information, yet have blurred spatial details; low-level features extracted from the early layers of a network contain more pixel-level information but are isolated and noisy. It is therefore difficult to bridge the gap between high- and low-level features due to their difference in terms of physical information content and spatial distribution. In this article, we contribute to solve this problem by enhancing the feature representation in two ways. On the one hand, a patch attention module (PAM) is proposed to enhance the embedding of context information based on a patchwise calculation of local attention. On the other hand, an attention embedding module (AEM) is proposed to enrich the semantic information of low-level features by embedding local focus from high-level features. Both proposed modules are lightweight and can be applied to process the extracted features of convolutional neural networks (CNNs). Experiments show that, by integrating the proposed modules into a baseline fully convolutional network (FCN), the resulting local attention network (LANet) greatly improves the performance over the baseline and outperforms other attention-based methods on two RSI data sets. Numéro de notice : A2021-035 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2994150 Date de publication en ligne : 27/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2994150 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96737
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 426 - 435[article]
Titre : Mapping urban spaces Type de document : Monographie Auteurs : Lamberto Amistadi, Éditeur scientifique ; Valter Balducci, Éditeur scientifique ; Thomasz Bradecki, Éditeur scientifique ; et al., Auteur Editeur : Londres : Routledge Année de publication : 2021 Importance : 308 p. Format : 15 x 21 cm ISBN/ISSN/EAN : 978-1-00-319066-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] Allemagne
[Termes IGN] analyse spatiale
[Termes IGN] architecture urbaine
[Termes IGN] carte ancienne
[Termes IGN] distribution spatiale
[Termes IGN] espace vert
[Termes IGN] France (administrative)
[Termes IGN] Grèce
[Termes IGN] Italie
[Termes IGN] morphologie urbaine
[Termes IGN] paysage urbain
[Termes IGN] répartition géographiqueRésumé : (éditeur) Mapping Urban Spaces focuses on medium-sized European cities and more specifically on their open spaces from psychological, sociological, and aesthetic points of view. The chapters illustrate how the characteristics that make life in medium-sized European cities pleasant and sustainable – accessibility, ease of travel, urban sustainability, social inclusiveness – can be traced back to the nature of that space. The chapters develop from a phenomenological study of space to contributions on places and landscapes in the city. Centralities and their meaning are studied, as well as the social space and its complexity. The contributions focus on history and theory as well as concrete research and mapping approaches and the resulting design applications. The case studies come from countries around Europe including Poland, Italy, Greece, Germany, and France, among others. The book will be of interest to students, scholars, and practitioners in architecture, urban planning, and landscape architecture. Note de contenu : Introduction
1- Mapping spaces: The phenomenological approach to the city of spaces
2- Mapping places: The Italian tradition of urban studies
3- Mapping natural space: Greenspaces and urban design
4- Mapping centralities: urban regeneration toward a polycentric city
5- Mapping social space: Demographic analysis as an image of urban complexity
AfterwardNuméro de notice : 28452 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.4324/9781003190660 En ligne : https://doi.org/10.4324/9781003190660 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98969 Spatial characterization and distribution modelling of Ensete ventricosum (wild and cultivated) in Ethiopia / Meron Awoke Eshetae in Geocarto international, vol 36 n° 1 ([01/01/2021])PermalinkThe use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution / Dimitri I. Rukhovitch in Remote sensing, vol 13 n° 1 (January-1 2021)PermalinkUsing remote sensing and modeling to monitor and understand harmful algal blooms. Application to Karaoun Reservoir (Lebanon) / Najwa Sharaf (2021)PermalinkBioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates / Robert E. Keane in Forest ecology and management, vol 477 ([01/12/2020])PermalinkEvaluating geo-tagged Twitter data to analyze tourist flows in Styria, Austria / Johannes Scholz in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkMapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery / Astrid Helena Huechacona-Ruiz in Forests, vol 11 n°11 (November 2020)PermalinkUsing climate-sensitive 3D city modeling to analyze outdoor thermal comfort in urban areas / Rabeeh Hosseinihaghighi in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkEvaluating the impact of declining tsetse fly (Glossina pallidipes) habitat in the Zambezi valley of Zimbabwe / Farai Matawa in Geocarto international, vol 35 n° 12 ([01/09/2020])PermalinkGeovisualization and harmonic analysis for the exploratory search of localized cyclic recurrences in spatio-temporal event data / Jacques Gautier in Geomatica, vol 74 n° 3 (September 2020)PermalinkHow do species and data characteristics affect species distribution models and when to use environmental filtering? / Lukáš Gábor in International journal of geographical information science IJGIS, vol 34 n° 8 (August 2020)Permalink