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Habitats, agricultural practices, and population dynamics of a threatened species: The European turtle dove in France / Christophe Sauser in Biological Conservation, vol 274 (octobre 2022)
[article]
Titre : Habitats, agricultural practices, and population dynamics of a threatened species: The European turtle dove in France Type de document : Article/Communication Auteurs : Christophe Sauser, Auteur ; Loïc Commagnac , Auteur ; Cyril Eraud, Auteur ; et al., Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 109730 Note générale : bibliographie
Addendum : "The authors add: This study was partly funded and forms part of OFB's contribution to the European Commission contract ENV.D.3/SER /2019/0021 “Development of a population model and adaptive harvest mechanism for Turtle Dove (Streptopelia turtur)”. The authors would like to apologise for any inconvenience caused."Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] agronomie
[Termes IGN] analyse diachronique
[Termes IGN] Aves
[Termes IGN] habitat animal
[Termes IGN] haie
[Termes IGN] impact sur l'environnement
[Termes IGN] jachère
[Termes IGN] lisière
[Termes IGN] modèle numérique
[Termes IGN] politique de conservation (biodiversité)
[Termes IGN] R (langage)Mots-clés libres : tourterelle des bois Streptopelia turtur Résumé : (auteur) Agricultural changes in recent decades have led to a widespread loss of biodiversity, with habitat loss considered as the main factor in the decline. The European turtle dove is one of the farmland birds that has declined markedly in Europe, leading the IUCN to downgrade its status in 2015 from “Near Threatened” to “Vulnerable”. Knowledge of how habitat factors and agricultural practices influence the turtle dove population is crucial for the conservation of this species through the implementation of targeted measures. Here we investigate how foraging and nesting habitats influence the abundance of turtle doves at national and regional scales, using a 23-year dataset from point counts carried out throughout France, a stronghold country for this species during the breeding season. We found that turtle dove abondance was positively affected by fallow lands, both at national and regional scales. Turtle dove abundance was also negatively affected by fodder crop area at national scale, but the effect was detected in only four of the 13 French regions. We also showed that an increase in hedgerows length had a positive effect on turtle dove abundance. On the other hand, forest edges length showed a bell-shaped trend, suggesting that an increase in forest edges length may have a favourable effect on turtle dove abundance only up to a given threshold. We suggest that targeted conservation measures combining an increase in fallow lands and hedgerows length could allow the stabilisation or even an increase in turtle dove abundance in France, but also in European countries with similar landscapes. Numéro de notice : A2022-687 Affiliation des auteurs : IGN+Ext (2020- ) Autre URL associée : Addendum Thématique : BIODIVERSITE/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.biocon.2022.109730 Date de publication en ligne : 09/09/2022 En ligne : https://doi.org/10.1016/j.biocon.2022.109730 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101612
in Biological Conservation > vol 274 (octobre 2022) . - n° 109730[article]Identify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)
[article]
Titre : Identify urban building functions with multisource data: a case study in Guangzhou, China Type de document : Article/Communication Auteurs : Yingbin Deng, Auteur ; Renrong Chen, Auteur ; Yang Ji, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2060 - 2085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] approche hiérarchique
[Termes IGN] batiment commercial
[Termes IGN] bâtiment industriel
[Termes IGN] bâtiment public
[Termes IGN] Canton (Kouangtoung)
[Termes IGN] données multisources
[Termes IGN] empreinte
[Termes IGN] exploration de données
[Termes IGN] Extreme Gradient Machine
[Termes IGN] figure géométrique
[Termes IGN] image Gaofen
[Termes IGN] logement
[Termes IGN] point d'intérêt
[Termes IGN] zone urbaineRésumé : (auteur) Building function type is an important parameter for urban planning and disaster management. However, existing identification methods do not always correctly recognize all building functions because of missing point of interest (POI) data in private areas. In this study, we proposed a hierarchical data-mining model to identify building function types using accessible auxiliary data, which was then applied to a case study. Residential building property was assessed to address missing residential POIs. The building functions were assigned to one of five different types, or a mixed-function type. Standard deviation and mean values extracted from remotely sensed images, distances to major roads, and building shape parameters were used to infer the function types of buildings without assigned function types. The proposed model was able to identify 65% of buildings not previously assigned as residential through the POI, with an overall accuracy of 87%. In addition, all buildings were successfully assigned a function type of residential, commercial, office, warehouse, public service, or mixed-function, with an overall accuracy of 85% for unclassified buildings. Our results demonstrated that missing POI data in private areas could be addressed by integration with multisource data using a simple method. Numéro de notice : A2022-739 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2046756 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2046756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101716
in International journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 2060 - 2085[article]Identifying the key resources and missing elements to build a knowledge graph dedicated to spatial dataset search / Mehdi Zrhal in Procedia Computer Science, vol 207 (2022)
[article]
Titre : Identifying the key resources and missing elements to build a knowledge graph dedicated to spatial dataset search Type de document : Article/Communication Auteurs : Mehdi Zrhal , Auteur ; Bénédicte Bucher , Auteur ; Fayçal Hamdi , Auteur ; Marie-Dominique Van Damme , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Conférence : KES 2022, 26th International Conference Knowledge-Based and Intelligent Information & Engineering Systems 07/09/2022 09/09/2022 Vérone Italie OA proceedings Article en page(s) : pp 2911 - 2920 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] jeu de données localisées
[Termes IGN] recherche d'information géographique
[Termes IGN] réseau sémantiqueRésumé : (auteur) The number of spatial datasets available online has increased exponentially in recent years. Therefore, the search for spatial datasets is becoming a fourishing research field. The use of knowledge graphs has become rampant in search engines and in information retrieval. In this article, we identify the main resources needed and those missing to allow a knowledge graph to support spatial dataset search. We then apply our approach to the water domain in France by building a dedicated knowledge graph and describe an evaluation method to measure its effectiveness. Numéro de notice : A2022-695 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.procs.2022.09.349 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1016/j.procs.2022.09.349 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101910
in Procedia Computer Science > vol 207 (2022) . - pp 2911 - 2920[article]Incremental road network update method with trajectory data and UAV remote sensing imagery / Jianxin Qin in ISPRS International journal of geo-information, vol 11 n° 10 (October 2022)
[article]
Titre : Incremental road network update method with trajectory data and UAV remote sensing imagery Type de document : Article/Communication Auteurs : Jianxin Qin, Auteur ; Wenjie Yang, Auteur ; Tao Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 502 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] données spatiotemporelles
[Termes IGN] extraction du réseau routier
[Termes IGN] image captée par drone
[Termes IGN] mise à jour de base de données
[Termes IGN] modèle de Markov caché
[Termes IGN] OpenStreetMap
[Termes IGN] réseau routier
[Termes IGN] segmentation
[Termes IGN] trace au solRésumé : (auteur) GPS trajectory and remote sensing data are crucial for updating urban road networks because they contain critical spatial and temporal information. Existing road network updating methods, whether trajectory-based (TB) or image-based (IB), do not integrate the characteristics of both types of data. This paper proposed and implemented an incremental update method for rapid road network checking and updating. A composite update framework for road networks is established, which integrates trajectory data and UAV remote sensing imagery. The research proposed utilizing connectivity between adjacent matched points to solve the problem of updating problematic road segments in networks based on the features of the Hidden Markov Model (HMM) map-matching method in identifying new road segments. Deep learning is used to update the local road network in conjunction with the flexible and high-precision characteristics of UAV remote sensing. Additionally, the proposed method is evaluated against two baseline methods through extensive experiments based on real-world trajectories and UAV remote sensing imagery. The results show that our method has higher extraction accuracy than the TB method and faster updates than the IB method. Numéro de notice : A2022-791 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/ijgi11100502 Date de publication en ligne : 27/09/2022 En ligne : https://doi.org/10.3390/ijgi11100502 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101904
in ISPRS International journal of geo-information > vol 11 n° 10 (October 2022) . - n° 502[article]Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity / Omid Memarian Sorkhabi in Earth and space science, vol 9 n° 10 (October 2022)
[article]
Titre : Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity Type de document : Article/Communication Auteurs : Omid Memarian Sorkhabi, Auteur ; Muhammed Milani, Auteur ; Seyed Mehdi Seyed Alizadeh, Auteur Année de publication : 2022 Article en page(s) : 8 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] apprentissage profond
[Termes IGN] champ de vitesse
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] géodynamique
[Termes IGN] point géodésique
[Termes IGN] positionnement par GPS
[Termes IGN] station GPS
[Termes IGN] tectoniqueRésumé : (auteur) Geodetic velocity (GV) has many applications in tectonic motion determination and geodynamic studies. Due to the high cost of global navigation satellite system stations, deep learning methods have been investigated to estimate GV. In this research, four methods of convolutional neural networks (CNNs), deep Boltzmann machines, deep belief net and recurrent neural networks have been applied. The GV of 42 global positioning system stations is entered the deep learning methods. The outputs of the four methods have successfully passed the normality test. The results show that the CNN method has a lower goodness of fit and root mean square error (RMSE). CNN can learn different dependencies and extract features. Numéro de notice : A2022-757 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1029/2021EA002202 Date de publication en ligne : 22/09/2022 En ligne : https://doi.org/10.1029/2021EA002202 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101763
in Earth and space science > vol 9 n° 10 (October 2022) . - 8 p.[article]Machine learning and natural language processing of social media data for event detection in smart cities / Andrei Hodorog in Sustainable Cities and Society, vol 85 (October 2022)PermalinkMachine learning for spatial analyses in urban areas: a scoping review / Ylenia Casali in Sustainable Cities and Society, vol 85 (October 2022)PermalinkNovel algorithm based on geometric characteristics for tree branch skeleton extraction from LiDAR point cloud / Jie Yang in Forests, vol 13 n° 10 (October 2022)PermalinkPPP rapid ambiguity resolution using Android GNSS raw measurements with a low-cost helical antenna / Xingxing Li in Journal of geodesy, vol 96 n° 10 (October 2022)PermalinkPrecise onboard time synchronization for LEO satellites / Florian Kunzi in Navigation : journal of the Institute of navigation, vol 69 n° 3 (Fall 2022)PermalinkPyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning / J.F. Roberts in Computers & geosciences, vol 167 (October 2022)PermalinkA relation-augmented embedded graph attention network for remote sensing object detection / Shu Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)PermalinkSemi-supervised adversarial recognition of refined window structures for inverse procedural façade modelling / Han Hu in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)PermalinkSimulating multiple urban land use changes by integrating transportation accessibility and a vector-based cellular automata: a case study on city of Toronto / Xiaocong Xu in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkSingle-image super-resolution for remote sensing images using a deep generative adversarial network with local and global attention mechanisms / Yadong Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)Permalink