Centre dedocumentation
scientifique

Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction / Jincai Huang in Transactions in GIS, vol 26 n° 2 (April 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 2 (April 2022) . - pp 735 - 754 Titre : Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction Type de document : Article/Communication Auteurs : Jincai Huang, Auteur ; Yunfei Zhang, Auteur ; Min Deng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 735 - 754 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Vedettes matières IGN] Géomatique [Termes IGN] base de données routières [Termes IGN] carrefour [Termes IGN] carte routière [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] données routières [Termes IGN] extraction de traits caractéristiques [Termes IGN] information sémantique [Termes IGN] intégration de données [Termes IGN] navigation automobile [Termes IGN] vitesse [Termes IGN] Wuhan (Chine) Résumé : (auteur) The road map is a fundamental part of a spatial data infrastructure (SDI), and is widely applied in navigation, smart transportation, and mobile location services. Recently, with the ubiquity of positioning devices, crowdsourced trajectories have become a significant data resource for road map construction and updating. However, existing trajectory-based methods mainly place emphasis on extracting road geometry features and may ignore continuous updating of road semantic information. Hence, we propose a divide-and-conquer method to construct a spatial-semantic road map by incorporating multiple data sources (e.g., crowdsourced trajectories and geo-tagged data). The proposed method divides road map construction into two sub-tasks, road structure reconstruction and road attributes inference. The road structure reconstruction process starts to partition raw trajectory data into different cliques of roadways and road intersections, and then extracts various targeted road structures by analyzing the turning modes in different trajectory cliques. The road attributes inference process aims to infer three pieces of crucial semantic information about road speeds, turning rules, and road names from crowdsourced trajectories and geo-tagged data. The case studies in Wuhan were examined to illustrate that the proposed method can construct a routable road map with enhanced geometric structures and rich semantic information, providing a beneficial data solution for car navigation and SDI update. Numéro de notice : A2022-364 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12879 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.1111/tgis.12879 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100583 [article]

The integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands / Aaron Judah in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)  

Public [article]  inCanadian journal of remote sensing > vol 48 n° 2 (April 2022) . - pp 158 - 181 Titre : The integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands Type de document : Article/Communication Auteurs : Aaron Judah, Auteur ; Baoxin Hu, Auteur Année de publication : 2022 Article en page(s) : pp 158 - 181 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] classification bayesienne [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-TM [Termes IGN] image Sentinel-SAR [Termes IGN] intégration de données [Termes IGN] modèle numérique de terrain [Termes IGN] précision de la classification [Termes IGN] tourbière [Termes IGN] utilisation du sol [Termes IGN] zone humide Résumé : (auteur) Methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from remotely sensed data using advanced classification algorithms through two hierarchical approaches. The data utilized included multispectral optical and thermal data (Landsat-5, and Landsat-8), radar imagery (Sentinel-1), and a digital elevation model. Goals were to determine the best way to combine imagery to classify wetlands through hierarchically based classification approaches to produce more accurate and efficient maps compared to standard classification. Algorithms used were Random Forest (RF), and Naïve Bayes. A hierarchically based RF classification methodology produced the most accurate classification result (91.94%). The hierarchically based approaches also improved classification accuracies for low-quality data, as defined through feature analysis, when compared to a nonhierarchical classifier. The hierarchical approaches also produced a significant increase in classification accuracy for the Naïve Bayes classifier versus the standard approach (∼12% increase) while not significantly increasing computation time – comparable in accuracy to the RF tests for around 20% the computational effort. Preselection of spectral bands, polarizations and other input parameters (Normalized Difference Vegetation Index, Normalized Difference Water Index, albedo, slope, etc.) using log-normal or RF variable importance analysis was very effective at identifying low-quality features and features which were of higher quality. Numéro de notice : A2022-372 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/07038992.2021.1967732 Date de publication en ligne : 13/11/2021 En ligne : https://doi.org/10.1080/07038992.2021.1967732 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100614 [article]

Accessing spatial knowledge networks with maps / Markus Jobst in International journal of cartography, vol 8 n° 1 (March 2022)  

Public [article]  inInternational journal of cartography > vol 8 n° 1 (March 2022) Titre : Accessing spatial knowledge networks with maps Type de document : Article/Communication Auteurs : Markus Jobst, Auteur ; Georg Gartner, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] agrégation spatiale [Termes IGN] généralisation automatique de données [Termes IGN] intégration de données [Termes IGN] jumeau numérique [Termes IGN] ontologie [Termes IGN] réseau sémantique [Vedettes matières IGN] Cartologie Résumé : (auteur) Currently, knowledge networks develop to establish common data spaces. A common data-space offers mutual exchange and reusability for data sources and their derived information and provides access to structured knowledge and even creates wisdom. The geospatial domain becomes included in those knowledge networks and, therefore, creates spatial knowledge networks. ‘Geospatial’ is moving from a special expert domain to a ‘normal’ common data source that is processed for specific data science use cases. Maps with their different levels of abstraction according to its transmission task may offer (1) strategies to enhance processing performance, due to its abstraction, (2) persistent references of map features throughout different scales (abstractions) and (3) improvement of the transmission of spatial information, which includes the transmission interfaces as well as geo-communication. This paper tries to identify new functions for maps in new developing application areas. For example, a ‘universal semantic structure of topographic content’ could help to establish relations/links across domains that only have their own feature keys. We try to set the scene of cartography in a common data-space and highlight some requirements in the world of spatial knowledge networks, which are needed for automatization, machine learning and AI. According to Gordon and de Souza location matters: ‘Mapping is not simply a mode of visualisation, but a “central organizational device for networked communications”, an adaptive interface through which users can access, alter and deploy an expansive database of information, and a platform to socialize spatial information through collective editing, annotations, discussion, etc.’. Numéro de notice : A2022-306 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/23729333.2021.1972910 Date de publication en ligne : 06/09/2021 En ligne : https://doi.org/10.1080/23729333.2021.1972910 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100394 [article]

Examining the integration of Landsat operational land imager with Sentinel-1 and vegetation indices in mapping southern yellow pines (Loblolly, Shortleaf, and Virginia pines) / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 1 (January 2022) . - pp 29 - 38 Titre : Examining the integration of Landsat operational land imager with Sentinel-1 and vegetation indices in mapping southern yellow pines (Loblolly, Shortleaf, and Virginia pines) Type de document : Article/Communication Auteurs : Clement E. Akumu, Auteur ; Eze O. Amadi, Auteur Année de publication : 2022 Article en page(s) : pp 29 - 38 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] bande C [Termes IGN] canopée [Termes IGN] carte de la végétation [Termes IGN] coefficient de rétrodiffusion [Termes IGN] image Landsat-OLI [Termes IGN] image Sentinel-SAR [Termes IGN] indice de végétation [Termes IGN] intégration de données [Termes IGN] inventaire forestier local [Termes IGN] Pinus (genre) [Termes IGN] Pinus ponderosa [Termes IGN] précision de la classification [Termes IGN] Soil Adjusted Vegetation Index Résumé : (Auteur) The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this study was to examine the integration of Landsat Operational Land Imager (OLI ) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data—normalized difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, transformed soil-adjusted vegetation index, and infrared percentage vegetation index; and 4) Landsat OLI with Sentinel-1 and vegetation indices. The results showed that the integration of Landsat OLI reflectance bands with Sentinel-1 backscattering coefficients and vegetation indices yielded the best overall classification accuracy, about 77%, and standalone Landsat OLI the weakest accuracy, approximately 67%. The findings in this study demonstrate that the addition of backscattering coefficients from Sentinel-1 and vegetation indices positively contributed to the mapping of southern yellow pines. Numéro de notice : A2022-062 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00024R2 Date de publication en ligne : 01/01/2022 En ligne : https://doi.org/10.14358/PERS.21-00024R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99706 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2022011	SL	Revue	Centre de documentation	Revues en salle	Disponible

The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data / Ana-Maria Olteanu-Raimond (2022)  

Public Titre : The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data : EuroSDR and LandSense Workshop, November 24th - 25th 2020, Online Conference Type de document : Actes de congrès Auteurs : Ana-Maria Olteanu-Raimond , Auteur ; Joep Crompvoets, Auteur ; Inian Moorthy, Auteur ; Clément Mallet , Auteur ; Bénédicte Bucher , Auteur Editeur : Dublin : European Spatial Data Research EuroSDR Année de publication : 2022 Collection : EuroSDR Workshop report Projets : Landsense / Raimond, Ana-Maria Conférence : VGI4LULC 2020, The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data 24/11/2020 25/11/2020 online OA Proceedings Importance : 40 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] approche participative [Termes IGN] cartographie collaborative [Termes IGN] collecte de données [Termes IGN] Corine Land Cover [Termes IGN] détection de changement [Termes IGN] données localisées des bénévoles [Termes IGN] intégration de données [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] science citoyenne [Termes IGN] utilisation du sol Résumé : (éditeur) The report refers to the workshop that was organized on behalf of EuroSDR and the LandSense project (24-25 November 2020). LandSense aims to build a citizen observatory for Land Use and Land Cover (LULC) monitoring by proposing innovate technologies for data collection, change detection, data quality assessment and offering tools and systems to empower different communities (e.g., private companies, Non Governmental Organisation, National Mapping Agencies, research, public authorities) to monitor and report on LULC. The workshop was co-organized by the LASTIG laboratory of the University Gustave Eiffel and IGN-ENSG, the French National Mapping agency (Ana-Maria Olteanu-Raimond, Clément Mallet, Bénédicte Bucher), the Katholieke Universiteit Leuven (Joep Crompvoets), the International Institute for Applied Systems Analysis (Inian Moorthy) and EuroSDR. Note de contenu : INTRODUCTION GENERALE 1. Introduction 1.1 Land Use and Land Cover data: specificities and challenges 1.2 VGI and citizen science for LULC monitoring 2. Session 1: Use of VGI for LULC data production 2.1 National Land Cover and Land Use Information System of Spain (SIOSE)- Coordination, production, maintenance and VGI 2.2 A fusion data approach for integrating VGI to update and enrich authoritative LULC data 2.3 OpenStreetMap for Earth Observation (OSM4EO) land use application and benchmark 2.4 Using OpenStreetMap as a data source for training classifiers to generate LULC maps 3. Session 2: Data collection and validation 3.1 A mapping prototype for land use mapping by land users 3.2 A mobile application for collecting snow data in support to satellite remote sensing 3.3 Global land cover monitoring, validation and participation: experiences from several case studies 4. Session 3: Sustainability 4.1 Crowdsourcing reference data collection for land cover and land use mapping: Findings from Picture Pile and FotoquestGo 4.2 Land Cover Monitoring System with Sentinel-Hub and Python Machine Learning Library eo-learn. Is it possible to build a fast and cost-effective LCMS? 4.3 Regular monitoring of landscape changes with Copernicus data- The German land cover change detection service 4.4 Authentication as a Service - A LandSense contribution to improve the FAIR principle in Citizen Science 5. Conclusion Numéro de notice : 28680 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : sans En ligne : http://www.eurosdr.net/sites/default/files/uploaded_files/eurosdr_vgi4lulc.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99973

Mobile mapping et PCRS / Clément Benoît in Géomatique expert, n° 136 (novembre - décembre 2021)

Permalink

Consistency assessment for open geodata integration: an ontology-based approach / Linfang Ding in Geoinformatica [en ligne], vol 25 n° 4 (October 2021)  

Permalink

Towards culture-aware smart and sustainable cities: Integrating historical sources in spatial information infrastructures / Bénédicte Bucher in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)  

Permalink

Integration of cadastral survey data into building information models / Behnam Atazadeh in Geo-spatial Information Science, vol 24 n° 3 (July 2021)  

Permalink

An area merging method in map generalization considering typical characteristics of structured geographic objects / Chengming Li in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)  

Permalink

Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping / Jiadi Yin in Remote sensing, vol 13 n° 8 (April-2 2021)  

Permalink

A data fusion-based framework to integrate multi-source VGI in an authoritative land use database / Lanfa Liu in International Journal of Digital Earth, vol 14 n° 4 (April 2021)  

Permalink

Topological integration of BIM and geospatial water utility networks across the building envelope / Thomas Gilbert in Computers, Environment and Urban Systems, vol 86 (March 2021)  

Permalink

A points of interest matching method using a multivariate weighting function with gradient descent optimization / Zhou Yang in Transactions in GIS, Vol 25 n° 1 (February 2021)  

Permalink

Real-time multimodal semantic scene understanding for autonomous UGV navigation / Yifei Zhang (2021)  

Permalink