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Artykuły w czasopismach na temat "Crowdsourced Mapping"
Soney, Johns. "Crowdsourced Pothole Mapping and Route Navigation". International Journal of Wireless Communications and Network Technologies 8, nr 3 (15.05.2019): 21–24. http://dx.doi.org/10.30534/ijwcnt/2019/05832019.
Pełny tekst źródłaDodge, Martin, i Rob Kitchin. "Crowdsourced Cartography: Mapping Experience and Knowledge". Environment and Planning A: Economy and Space 45, nr 1 (styczeń 2013): 19–36. http://dx.doi.org/10.1068/a44484.
Pełny tekst źródłaJestico, Ben, Trisalyn Nelson i Meghan Winters. "Mapping ridership using crowdsourced cycling data". Journal of Transport Geography 52 (kwiecień 2016): 90–97. http://dx.doi.org/10.1016/j.jtrangeo.2016.03.006.
Pełny tekst źródłaRice, Rebecca M., Ahmad O. Aburizaiza, Matthew T. Rice i Han Qin. "Position Validation in Crowdsourced Accessibility Mapping". Cartographica: The International Journal for Geographic Information and Geovisualization 51, nr 2 (styczeń 2016): 55–66. http://dx.doi.org/10.3138/cart.51.2.3143.
Pełny tekst źródłaGkeli, Maria, i Chryssy Potsiou. "3D crowdsourced parametric cadastral mapping: Pathways integrating BIM/IFC, crowdsourced data and LADM". Land Use Policy 131 (sierpień 2023): 106713. http://dx.doi.org/10.1016/j.landusepol.2023.106713.
Pełny tekst źródłaGroß, Simon, Benjamin Herfort, Sabrina Marx i Alexander Zipf. "Exploring MapSwipe as a Crowdsourcing Tool for (Rapid) Damage Assessment: The Case of the 2021 Haiti Earthquake". AGILE: GIScience Series 4 (6.06.2023): 1–11. http://dx.doi.org/10.5194/agile-giss-4-5-2023.
Pełny tekst źródłaMcCullagh, M., i M. Jackson. "CROWDSOURCED MAPPING – LETTING AMATEURS INTO THE TEMPLE?" ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W1 (22.05.2013): 399–432. http://dx.doi.org/10.5194/isprsarchives-xl-1-w1-399-2013.
Pełny tekst źródłaPipelidis, Georgios, Omid Moslehi Rad, Dorota Iwaszczuk, Christian Prehofer i Urs Hugentobler. "Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data". Sensors 18, nr 2 (6.02.2018): 480. http://dx.doi.org/10.3390/s18020480.
Pełny tekst źródłaBranion-Calles, Michael, Trisalyn Nelson i Meghan Winters. "Comparing Crowdsourced Near-Miss and Collision Cycling Data and Official Bike Safety Reporting". Transportation Research Record: Journal of the Transportation Research Board 2662, nr 1 (styczeń 2017): 1–11. http://dx.doi.org/10.3141/2662-01.
Pełny tekst źródłaLingua, Federico, Nicholas C. Coops, Valentine Lafond, Christopher Gaston i Verena C. Griess. "Characterizing, mapping and valuing the demand for forest recreation using crowdsourced social media data". PLOS ONE 17, nr 8 (11.08.2022): e0272406. http://dx.doi.org/10.1371/journal.pone.0272406.
Pełny tekst źródłaRozprawy doktorskie na temat "Crowdsourced Mapping"
Stoven-Dubois, Alexis. "Robust Crowdsourced Mapping for Landmarks-based Vehicle Localization". Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2022. http://www.theses.fr/2022UCFAC116.
Pełny tekst źródłaThe deployment of intelligent and connected vehicles, equipped with increasingly sophisticated equipment, and capable of sharing accurate positions and trajectories, is expected to lead to a substantial improvement of road safety and traffic efficiency. For this safety gain to become effective, vehicles will have to be accurately geo-positioned in a common reference, with an error up to a few decimeters [1]. To achieve this, they will be able to count on a variety of embedded sensors, such as GNSS (Global Navigation Satellite Systems) receivers, as well as additional proprioceptive and perception sensors. Nevertheless, in order to guarantee accurate positioning in all conditions, including in dense zones where GNSS signals can get degraded by multi-path effects, it is expected that vehicles will need to use precise maps of the environment to support their localization algorithms.To build maps of the main highways, major automotive actors have made use of dedicated fleets of vehicles equipped with high-end sensors. Because of the associated high operational costs, they have been operating a limited number of vehicles, and remain unable to provide live updates of the maps and to register entire road networks. Crowdsourced mapping represents a cost-effective solution to this problem, and has been creating interest among automotive players. It consists in making use of measurements retrieved by multiple production vehicles equipped with standard sensors in order to build a map of landmarks. Nevertheless, while this approach appears promising, its real potential to build an accurate map of landmarks and maintain it up-to-date remains to be assessed in realistic, long-term scenarios.In this thesis, in a first time, we propose a crowdsourced mapping solution based on triangulation optimization, and evaluate it using field-tests. The result analysis shows the potential of crowdsourced mapping to take advantage from measurements issued by multiple vehicles. On the other hand, it also indicates some critical limitations associated with triangulation optimization.Therefore, in a second time, we propose another crowdsourced mapping solution based on graph optimization, and we introduce different approaches to include and update the map within the optimization, which correspond to different trade-offs between the map quality and computational scalability. Simulation experiments are conducted in order to compare the different approaches. The results enable to identify the most efficient one, and to assert that it provides a scalable solution for crowdsourced mapping.The robustness of this solution to various types of noises, such as auto-correlated and biased noises, is then evaluated using extended simulation tests. The results analysis show its ability to build an accurate map of landmarks in various noises conditions, making use of measurements retrieved by multiple vehicles. Subsequently, field-tests are performed to confirm the results obtained in simulation, and draw conclusions both from a theoretical and practical viewpoint. Finally, the capacity of our crowdsourced mapping solution to increase the localization capabilities of vehicles is evaluated in simulation. The results show the effectiveness of the proposed approach to improve positioning performances in various conditions, while also pointing out the importance of providing a map with a sufficient density of landmarks
Huai, Jianzhu. "Collaborative SLAM with Crowdsourced Data". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1483669256597152.
Pełny tekst źródłaDevoid, Alexander David, i Alexander David Devoid. "Collaboratively Mapping Militarized Borders and Law Enforcement: A Crowdsourced Mobile App". Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/625682.
Pełny tekst źródłaKsiążki na temat "Crowdsourced Mapping"
Capineri, Cristina, Muki Haklay, Haosheng Huang, Vyron Antoniou, Juhani Kettunen, Frank Ostermann i Ross Purves, red. European Handbook of Crowdsourced Geographic Information. London, United Kingdom: Ubiquity Press, 2016.
Znajdź pełny tekst źródłaFoody, Giles, Peter Mooney, Cidália Costa Fonte, Ana Maria Olteanu Raimond, Steffen Fritz i Linda See, red. Mapping and the Citizen Sensor. London, United Kingdom: Ubiquity Press, 2017.
Znajdź pełny tekst źródłaMooney, Peter, Giles Foody i Linda See. Mapping and the Citizen Sensor. Saint Philip Street Press, 2020.
Znajdź pełny tekst źródłaCzęści książek na temat "Crowdsourced Mapping"
Pődör, Andrea, i László Zentai. "Educational Aspects of Crowdsourced Noise Mapping". W Advances in Cartography and GIScience, 35–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57336-6_3.
Pełny tekst źródłaSharker, Monir H., Jessica G. Benner i Hassan A. Karimi. "On Reliability of Routes Computed Based on Crowdsourced Points of Interest". W Citizen Empowered Mapping, 153–72. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51629-5_7.
Pełny tekst źródłaSoden, Robert, i Leysia Palen. "From Crowdsourced Mapping to Community Mapping: The Post-earthquake Work of OpenStreetMap Haiti". W COOP 2014 - Proceedings of the 11th International Conference on the Design of Cooperative Systems, 27-30 May 2014, Nice (France), 311–26. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06498-7_19.
Pełny tekst źródłaLeao, Simone Z., i Chris Pettit. "Mapping Bicycling Patterns with an Agent-Based Model, Census and Crowdsourced Data". W Agent Based Modelling of Urban Systems, 112–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51957-9_7.
Pełny tekst źródłaMakhortykh, Mykola. "Geospatial Data Analysis in Russia’s Geoweb". W The Palgrave Handbook of Digital Russia Studies, 585–604. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42855-6_32.
Pełny tekst źródłaNakacwa, Stellamaris, i Bert Manieson. "Cities of the Future Need to Be Both Smart and Just: How We Think Open Mapping Can Help". W Sustainable Development Goals Series, 305–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05182-1_27.
Pełny tekst źródłaGarcía-Álvarez, David, Javier Lara Hinojosa i Jaime Quintero Villaraso. "Global General Land Use Cover Datasets with a Single Date". W Land Use Cover Datasets and Validation Tools, 269–86. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_14.
Pełny tekst źródłaMarino, Andrea, Marco Pesce i Raffaella Succi. "Access to emergency care services and inequalities in living standards: Some evidence from two Italian northern regions". W Proceedings e report, 135–40. Florence: Firenze University Press and Genova University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0106-3.24.
Pełny tekst źródłaHossain, A. K. M. Mahtab. "Crowdsourced Indoor Mapping". W Geographical and Fingerprinting Data to Create Systems for Indoor Positioning and Indoor/Outdoor Navigation, 97–114. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-813189-3.00005-8.
Pełny tekst źródłaDe Chiara, Francesca, i Maurizio Napolitano. "Mapping the Mappers". W Handbook of Research on Advanced Research Methodologies for a Digital Society, 526–47. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8473-6.ch031.
Pełny tekst źródłaStreszczenia konferencji na temat "Crowdsourced Mapping"
Das, Anweshan, Joris IJsselmuiden i Gijs Dubbelman. "Pose-graph based Crowdsourced Mapping Framework". W 2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2020. http://dx.doi.org/10.1109/cavs51000.2020.9334622.
Pełny tekst źródłaAkimoto, Mina, Xiaoyan Wang, Masahiro Umehira i Yusheng Ji. "Crowdsourced Radio Environment Mapping by Exploiting Machine Learning". W 2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC). IEEE, 2019. http://dx.doi.org/10.1109/wpmc48795.2019.9096108.
Pełny tekst źródłaWang, Gang, Bolun Wang, Tianyi Wang, Ana Nika, Haitao Zheng i Ben Y. Zhao. "Defending against Sybil Devices in Crowdsourced Mapping Services". W MobiSys'16: The 14th Annual International Conference on Mobile Systems, Applications, and Services. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2906388.2906420.
Pełny tekst źródłaStoven-Dubois, Alexis, Aziz Dziri, Bertrand Leroy i Roland Chapuis. "Graph Optimization Methods for Large-Scale Crowdsourced Mapping". W 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190292.
Pełny tekst źródłaApajalahti, Kasper, Ermias Andargie Walelgne, Jukka Manner i Eero Hyvonen. "Correlation-Based Feature Mapping of Crowdsourced LTE Data". W 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2018. http://dx.doi.org/10.1109/pimrc.2018.8580999.
Pełny tekst źródłaButler, Crystal, Lakshmi Subramanian i Stephanie Michalowicz. "Crowdsourced Facial Expression Mapping Using a 3D Avatar". W CHI'16: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2851581.2892535.
Pełny tekst źródłaWang, Bolun. "Defending against Sybil Devices in Crowdsourced Mapping Services". W MobiSys'16: The 14th Annual International Conference on Mobile Systems, Applications, and Services. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2930056.2933320.
Pełny tekst źródłaJia, Shuaidong, Zhicheng Liang, Lihua Zhang i Hao Yuan. "Uncertainty Modeling of Crowdsourced Bathymetry Data Influenced by Marine Environment". W 2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS). IEEE, 2022. http://dx.doi.org/10.1109/icgmrs55602.2022.9849245.
Pełny tekst źródłaIngensand, Jens, Marion Nappez, Stéphane Joost, Ivo Widmer, Olivier Ertz i Daniel Rappo. "The Urbangene Project - Experience from a Crowdsourced Mapping Campaign". W 1st International Conference on Geographical Information Systems Theory, Applications and Management. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005468501780184.
Pełny tekst źródłade Campos, Vitor Queiroz, Jose Maria N. David i Regina Braga. "Coordination in Crowdsourced Software Development: A Systematic Mapping Study". W 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2021. http://dx.doi.org/10.1109/cscwd49262.2021.9437804.
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