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From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 / Yousra Hamrouni in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
[article]
Titre : From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 Type de document : Article/Communication Auteurs : Yousra Hamrouni, Auteur ; Eric Paillassa, Auteur ; Véronique Chéret, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 76 - 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] base de données forestières
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] couvert forestier
[Termes IGN] échantillonnage
[Termes IGN] France (administrative)
[Termes IGN] image Sentinel-MSI
[Termes IGN] mise à jour de base de données
[Termes IGN] Populus (genre)
[Termes IGN] série temporelleRésumé : (auteur) Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies because of data shift issues. In this paper, we propose the use of active learning to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing the classification performance. The adaptation consists in actively adding to the initial local model new relevant training samples from other areas in a cascade that iteratively improves the generalisation capabilities of the classifier leading to a global model tailored to these different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series revealed their high capacity to identify poplar plantations at a local scale with an average F-score ranging from 89.5% to 99.3%. For large area adaptation, the results showed that when the same number of training samples was used, active learning outperformed random sampling by up to 5% of the overall accuracy and up to 12% of the class F-score. Additionally, and depending on the class considered, the random sampling model required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for a national scale mapping in an operational context. Numéro de notice : A2021-013 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.018 Date de publication en ligne : 20/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.018 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96417
in ISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 76 - 100[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021011 SL Revue Centre de documentation Revues en salle Disponible 081-2021013 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Introducing diversion graph for real-time spatial data analysis with location based social networks / Sameera Kannangara (2021)
Titre : Introducing diversion graph for real-time spatial data analysis with location based social networks Type de document : Article/Communication Auteurs : Sameera Kannangara, Auteur ; Hairuo Xie, Auteur ; Egemen Tanin, Auteur ; Aaron Harwood, Auteur ; Shanika Karunasekera, Auteur Editeur : Leibniz [Allemagne] : Schloss Dagstuhl – Leibniz-Zentrum für Informatik Année de publication : 2021 Conférence : GIScience 2021, 11th International Conference on Geographic Information Science 27/09/2021 30/09/2021 Poznań Pologne Open Access Proceedings Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] chemin le plus court, algorithme du
[Termes IGN] graphe
[Termes IGN] image Flickr
[Termes IGN] objet mobile
[Termes IGN] réseau social géodépendant
[Termes IGN] temps réel
[Termes IGN] triangulation de Delaunay
[Termes IGN] TwitterRésumé : (auteur) Neighbourhood graphs are useful for inferring the travel network between locations posted in the Location Based Social Networks (LBSNs). Existing neighbourhood graphs, such as the Stepping Stone Graph lack the ability to process a high volume of LBSN data in real time. We propose a neighbourhood graph named Diversion Graph, which uses an efficient edge filtering method from the Delaunay triangulation mechanism for fast processing of LBSN data. This mechanism enables Diversion Graph to achieve a similar accuracy level as Stepping Stone Graph for inferring travel networks, but with a reduction of the execution time of over 90%. Using LBSN data collected from Twitter and Flickr, we show that Diversion Graph is suitable for travel network processing in real time. Numéro de notice : C2021-079 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : 10.4230/LIPIcs.GIScience.2021.I.7 Date de publication en ligne : 25/09/2020 En ligne : https://doi.org/10.4230/LIPIcs.GIScience.2021.I.7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100930
Titre : Knowledge graph management and streaming in the context of edge computing Type de document : Thèse/HDR Auteurs : Weiqin Xu, Auteur ; Olivier Curé, Directeur de thèse Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2021 Importance : 122 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université Gustave Eiffel, Spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] flux continu
[Termes IGN] informatique en nuage
[Termes IGN] internet des objets
[Termes IGN] langage de requête
[Termes IGN] module d'extension
[Termes IGN] ontologie
[Termes IGN] OWL
[Termes IGN] RDF
[Termes IGN] réseau sémantique
[Termes IGN] SPARQL
[Termes IGN] stockage de données
[Termes IGN] web sémantiqueIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Edge Computing proposes to distribute computation and data storage closer to original data sources. This technology is becoming an important trend in IT. This is mainly due to the emergence of the Internet of Things and its set of compact devices, eg sensors, actuators or gateways, whose computing and storing capacities are ever-increasing. Different from Cloud Computing, which targets large data centers, Edge Computing's computation distribution strategy can potentially reduce network pressure and make full use of computation power of edge devices.In order to support smart data processing at the edge of the network, a knowledge representation strategy is needed. In 2021, technologies belonging to the so-called Semantic Web are mature and robust enough to bring intelligence to Edge computing. These technologies correspond to the RDF (Resource Description Framework) data model, the RDFS (RDF Schema) and OWL (Web ontology Language) ontology languages and their associated reasoning services, the SPARQL query language. A cornerstone of such an approach is an Edge device compliant RDF database management system. However, most RDF stores are designed for powerful servers or Cloud Computing. These systems partly owe their efficiency to costly indexing strategies, ie based on multiples indexes.In the context of Edge computing, characterised by relatively limited memory footprint and computing power, it is not reasonable to use any of these RDF stores. Hence, a novel kind of RDF store is needed. In this work, we consider that some of its features must be an in-memory approach, low-memory footprint for both the system and its managed data, adapted query optimization techniques to make query processing as fast as possible. Moreover, reasoning at query run-time and stream processing are required by several of the use cases that we have identified in real-world situations.For the aim of compressing RDF data while maintaining querying speed, we make an extensive use of Succinct Data Structure (SDS) data structures to benefit from its data compression and high data retrieving speed simultaneously. This help us to get a self-indexed compact RDF store which does not require decompression operation. Our query processing approach is adapted to our storage layout and to standard SDS operations, namely access, rank and select. We prove the efficiency of our approach with thorough evaluation.In order to help the acceleration of RDFS reasoning, we have designed our system based on a semantic-aware encoding strategy named LiteMat. This encoding scheme, which has been developed and maintained by our research team, has been extended in the PhD thesis to support multiple inheritance, transitive and inverse properties. It thus extends the expressive power of addressed ontologies.In real IoT use cases, data are usually continuously coming from sensors or actuators. To address this issue, an extension of SuccinctEdge has been designed to handle those streaming data. This extension includes an extra data structure in our RDF store to process numeric data with time-based aggregations and an adapted streaming-SPARQL extension processor to permit the querying of streaming data. With the help of this extra data structure and the adapted query processor, one can easily query the dynamic RDF graph by a streaming-SPARQL query. However, query execution on a dynamic graph may have many repeating graph searching, which may heavily slow down the system. In order to solve this problem, we separate a query into dynamic part and static part. The result of the static part is computed once and stored all along the duration of the continuous query processing. Concerning the dynamic part, the corresponding result is combined with the static part result to generate the final result of each query execution. We prove that our streaming extension system is of low latency and of high throughput with good robustness and correctness properties. Note de contenu : 1- Introduction
2- Background knowledge
3- LiteMat, an encoding scheme for RDFS++
4- SuccinctEdge
5- Streaming SuccinctEdge
6- ConclusionNuméro de notice : 24026 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Gustave Eiffel : 2021 Organisme de stage : Laboratoire d’Informatique Gaspard Monge DOI : sans En ligne : https://tel.hal.science/tel-03697222/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101824
Titre : Lessons learned from a VGI initiative for Land Use monitoring Type de document : Article/Communication Auteurs : Ana-Maria Olteanu-Raimond , Auteur ; Marie-Dominique Van Damme , Auteur ; Laurent Jolivet, Auteur Editeur : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Abstracts of the ICA Projets : 1-Pas de projet / Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie Open Access Proceedings Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] données localisées de référence
[Termes IGN] données localisées des bénévoles
[Termes IGN] enrichissement sémantique
[Termes IGN] mise à jour de base de données
[Termes IGN] plateforme collaborative
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] utilisation du solRésumé : (auteur) Land Use (LU) mapping and monitoring at fine spatial and temporal resolutions requires many efforts. Remote-sensing based change detection approaches exist (Lu et al., 2014), though use is not trivial and not necessarily related to cover. Considerable interest has then emerged in using Volunteered Geographic Information (VGI) (Goodchild, 2007) as an alternative source of data (Fonte et al., 2013; Fritz et al., 2015). The goal of this paper is to discuss the lessons learned from a VGI data collection initiative which have aimed to collect change and local LU observations (i.e. quarry activity, usage and number of floors of a building, construction in progress) for updating and enriching authoritative LU data. Numéro de notice : C2021-055 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-3-225-2021 Date de publication en ligne : 13/12/2021 En ligne : https://doi.org/10.5194/ica-abs-3-225-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99393
Titre : Mining the semantic Web for OWL axioms Titre original : Fouille du Web sémantique à la recherche d'axiomes OWL Type de document : Thèse/HDR Auteurs : Thu Huong Nguyen, Auteur ; Andrea Tettamanzi, Directeur de thèse Editeur : Nice : Université Côte d'Azur Année de publication : 2021 Importance : 175 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat présentée en vue de l’obtention du grade de docteur en Informatique de l’Université Côte d’AzurLangues : Français (fre) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme génétique
[Termes IGN] données ouvertes
[Termes IGN] exploration de données
[Termes IGN] logique floue
[Termes IGN] ontologie
[Termes IGN] OWL
[Termes IGN] RDF
[Termes IGN] théorie des possibilités
[Termes IGN] web des données
[Termes IGN] web sémantiqueIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In the Semantic Web era, Linked Open Data (LOD) is its most successful implementation, which currently contains billions of RDF (Resource Data Framework) triples derived from multiple, distributed, heterogeneous sources. The role of a general semantic schema, represented as an ontology, is essential to ensure the correctness and consistency in LOD and make it possible to infer implicit knowledge by reasoning. The growth of LOD creates an opportunity for the discovery of
ontological knowledge from its raw RDF data itself to enrich relevant knowledge bases. In this work, we aim at discovering schema-level knowledge in the form of axioms encoded in OWL (Ontology Web Language) from RDF data. The approaches to automated generation of the axioms from recorded RDF facts on the Web may be regarded as a case of inductive reasoning and ontology learning. The instances, represented by RDF triples, play the role of specific observations, from which axioms can be extracted by generalization. Based on the insight that discovering new knowledge is essentially an evolutionary, whereby hypotheses are generated by some heuristic mechanism and then tested against the available evidence, so that only the best hypotheses survive, we propose a model applying Grammatical Evolution, one type of evolutionary algorithm, to mine OWL axioms from an RDF data repository. In addition, we specialize the model for the specific problem of learning OWL class disjointness axioms, along with the experiments performed on DBpedia, one of the prominent examples of LOD. Furthermore, we use different axiom scoring functions based on possibility theory, which are well-suited to the open world assumption scenario of LOD, to evaluate the quality of discovered axioms. Specifically, we proposed a set of measures to build objective functions based on single-objective and multi-objective models, respectively. Finally, in order to validate it, the performance of our approach is evaluated against subjective and objective benchmarks, and is also compared to the main state-of-the-art systems.Note de contenu : 1- Introduction
2- Foundation
3- Literature review
4- Learning OWL axioms from RDF data
5- Axiom evaluation
6- Grammatical evolution models toward class disjointness axiom discovery
7- A multi-objective GE approach to class disjointness axioms discovery
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