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BDS and GPS side-lobe observation quality analysis and orbit determination with a GEO satellite onboard receiver / Wenwen Li in GPS solutions, vol 27 n° 1 (January 2023)  

Public [article]  inGPS solutions > vol 27 n° 1 (January 2023) . - n° 18 Titre : BDS and GPS side-lobe observation quality analysis and orbit determination with a GEO satellite onboard receiver Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Kecai Jiang, Auteur ; Min Li, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Techniques orbitales [Termes IGN] code GNSS [Termes IGN] orbite géostationnaire [Termes IGN] orbite précise [Termes IGN] orbitographie [Termes IGN] orbitographie par GNSS [Termes IGN] phase [Termes IGN] positionnement par BeiDou [Termes IGN] positionnement par GPS Résumé : (auteur) Multi-GNSS combination can alleviate problems associated with GNSS-based geostationary earth orbit (GEO) satellite navigation and orbit determination (OD), such as GNSS availability and observation geometry deterioration. However, only GPS has been widely applied and investigated in GEO missions, whereas GEO OD with BDS requires further exploration. The Chinese GEO satellite TJS-5, equipped with a GPS and BDS-compatible receiver, is the first GEO mission that tracks both BDS 2nd and 3rd generation satellites since BDS global deployment. With the TJS-5 real onboard data, we evaluate BDS side-lobe signal performance and the BDS contribution to GEO OD. Due to transmit antenna gain deficiencies in the side lobes, BDS shows a worse tracking performance than GPS with an average satellite number of 4.3 versus 7.8. Both GPS and BDS reveal inconsistency between carrier-phase and code observations, which reaches several meters and significantly degrades post-dynamic OD calculation. We estimate the consistency as a random walk process in the carrier-phase observation model to reduce its impact. With inconsistency estimated, the post-fit carrier-phase residuals decrease from 0.21 to 0.09 m for both GPS and BDS. The OD precision is significantly improved, from 1.95 to 1.42 m with only GPS and from 3.14 to 2.71 m with only BDS; the GPS and BDS combined OD exhibits the largest improvement from 1.74 to 0.82 m, demonstrating that adding BDS improves the OD precision by 43.3%. The above results indicate that the proposed carrier-phase inconsistency estimation approach is effective for both GPS and BDS and can achieve an orbit precision within 1.0 m using multi-GNSS measurements. Numéro de notice : A2023-026 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-022-01358-5 Date de publication en ligne : 06/11/2022 En ligne : https://doi.org/10.1007/s10291-022-01358-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102261 [article]

Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications / Wenwen Li in Computers, Environment and Urban Systems, vol 98 (December 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 98 (December 2022) . - n° 101884 Titre : Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Sizhe Wang, Auteur ; Sheng wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101884 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] base de données relationnelles [Termes IGN] entrepôt de données [Termes IGN] ontologie [Termes IGN] RDF [Termes IGN] référentiel sémantique [Termes IGN] requête spatiale [Termes IGN] réseau sémantique [Termes IGN] SPARQL [Termes IGN] stockage de données [Termes IGN] test de performance [Termes IGN] web sémantique Résumé : (auteur) Knowledge graph has become a cutting-edge technology for linking and integrating heterogeneous, cross-domain datasets to address critical scientific questions. As big data has become prevalent in today's scientific analysis, semantic data repositories that can store and manage large knowledge graph data have become critical in successfully deploying spatially explicit knowledge graph applications. This paper provides a comprehensive evaluation of the popular semantic data repositories and their computational performance in managing and providing semantic support for spatial queries. There are three types of semantic data repositories: (1) triple store solutions (RDF4j, Fuseki, GraphDB, Virtuoso), (2) property graph databases (Neo4j), and (3) an Ontology-Based Data Access (OBDA) approach (Ontop). Experiments were conducted to compare each repository's efficiency (e.g., query response time) in handling geometric, topological, and spatial-semantic related queries. The results show that Virtuoso achieves the overall best performance in both non-spatial and spatial-semantic queries. The OBDA solution, Ontop, has the second-best query performance in spatial and complex queries and the best storage efficiency, requiring the least data-to-RDF conversion efforts. Other triple store solutions suffer from various issues that cause performance bottlenecks in handling spatial queries, such as inefficient memory management and lack of proper query optimization. Numéro de notice : A2022-720 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101884 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101884 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101654 [article]

GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning / Samantha T. Arundel in Transactions in GIS, Vol 24 n° 3 (June 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 556 - 572 Titre : GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning Type de document : Article/Communication Auteurs : Samantha T. Arundel, Auteur ; Wenwen Li, Auteur ; Sizhe Wang, Auteur Année de publication : 2020 Article en page(s) : pp 556 - 572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] cartographie topographique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] collecte de données [Termes IGN] détection automatique [Termes IGN] détection d'objet [Termes IGN] géobalise [Termes IGN] toponyme [Termes IGN] United States Geological Survey Résumé : (Auteur) Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain. Numéro de notice : A2020-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12633 Date de publication en ligne : 08/05/2020 En ligne : https://doi.org/10.1111/tgis.12633 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95307 [article]

Automated terrain feature identification from remote sensing imagery: a deep learning approach / Wenwen Li in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 637 - 660 Titre : Automated terrain feature identification from remote sensing imagery: a deep learning approach Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Chia-Yu Hsu, Auteur Année de publication : 2020 Article en page(s) : pp 637 - 660 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse du paysage [Termes IGN] apprentissage profond [Termes IGN] base de données d'images [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compréhension de l'image [Termes IGN] détection automatique [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] intelligence artificielle Résumé : (auteur) Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model’s characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science. Numéro de notice : A2020-108 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542697 Date de publication en ligne : 07/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542697 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94708 [article]

Association rules-based multivariate analysis and visualization of spatiotemporal climate data / Feng Wang in ISPRS International journal of geo-information, vol 7 n° 7 (July 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 7 (July 2018) Titre : Association rules-based multivariate analysis and visualization of spatiotemporal climate data Type de document : Article/Communication Auteurs : Feng Wang, Auteur ; Wenwen Li, Auteur ; Sizhe Wang, Auteur ; Chris R. Johnson, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle [Termes IGN] analyse multivariée [Termes IGN] Arctique [Termes IGN] cyclone [Termes IGN] données météorologiques [Termes IGN] données spatiotemporelles [Termes IGN] phénomène atmosphérique [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) Understanding atmospheric phenomena involves analysis of large-scale spatiotemporal multivariate data. The complexity and heterogeneity of such data pose a significant challenge in discovering and understanding the association between multiple climate variables. To tackle this challenge, we present an interactive heuristic visualization system that supports climate scientists and the public in their exploration and analysis of atmospheric phenomena of interest. Three techniques are introduced: (1) web-based spatiotemporal climate data visualization; (2) multiview and multivariate scientific data analysis; and (3) data mining-enabled visual analytics. The Arctic System Reanalysis (ASR) data are used to demonstrate and validate the effectiveness and usefulness of our method through a case study of “The Great Arctic Cyclone of 2012”. The results show that different variables have strong associations near the polar cyclone area. This work also provides techniques for identifying multivariate correlation and for better understanding the driving factors of climate phenomena. Numéro de notice : A2018-503 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7070266 Date de publication en ligne : 09/07/2018 En ligne : https://doi.org/10.3390/ijgi7070266 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90575 [article]

Precise orbit determination of the Fengyun-3C satellite using onboard GPS and BDS observations / Min Li in Journal of geodesy, vol 91 n° 11 (November 2017)  

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PolarGlobe : A web-wide virtual globe system for visualizing multidimensional, time-varying, big climate data / Wenwen Li in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

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vol 43 n° 5 - November 2016 - Integrating big social data, computing and modeling for spatial social science (Bulletin de Cartography and Geographic Information Science) / Xinyue Ye  

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SinoGrids: a practice for open urban data in China / Xinyue Ye in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)  

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Estimating spatial efficiency using cyber search, GIS, and spatial optimization: a case study of fire service deployment in Los Angeles County / R.L. Church in International journal of geographical information science IJGIS, vol 30 n° 3-4 (March - April 2016)  

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Real-time high-precision earthquake monitoring using single-frequency GPS receivers / Min Li in GPS solutions, vol 19 n° 1 (January 2015)  

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