Готові списки джерел за темами / Crowdsourced Mapping

Добірка наукової літератури з теми "Crowdsourced Mapping"

Автор: Grafiati

Опубліковано: 24 лютого 2024

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Статті в журналах з теми "Crowdsourced Mapping"

Soney, Johns. "Crowdsourced Pothole Mapping and Route Navigation." International Journal of Wireless Communications and Network Technologies 8, no. 3 (May 15, 2019): 21–24. http://dx.doi.org/10.30534/ijwcnt/2019/05832019.

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Dodge, Martin, and Rob Kitchin. "Crowdsourced Cartography: Mapping Experience and Knowledge." Environment and Planning A: Economy and Space 45, no. 1 (January 2013): 19–36. http://dx.doi.org/10.1068/a44484.

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Jestico, Ben, Trisalyn Nelson, and Meghan Winters. "Mapping ridership using crowdsourced cycling data." Journal of Transport Geography 52 (April 2016): 90–97. http://dx.doi.org/10.1016/j.jtrangeo.2016.03.006.

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Rice, Rebecca M., Ahmad O. Aburizaiza, Matthew T. Rice, and Han Qin. "Position Validation in Crowdsourced Accessibility Mapping." Cartographica: The International Journal for Geographic Information and Geovisualization 51, no. 2 (January 2016): 55–66. http://dx.doi.org/10.3138/cart.51.2.3143.

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Gkeli, Maria, and Chryssy Potsiou. "3D crowdsourced parametric cadastral mapping: Pathways integrating BIM/IFC, crowdsourced data and LADM." Land Use Policy 131 (August 2023): 106713. http://dx.doi.org/10.1016/j.landusepol.2023.106713.

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Groß, Simon, Benjamin Herfort, Sabrina Marx, and Alexander Zipf. "Exploring MapSwipe as a Crowdsourcing Tool for (Rapid) Damage Assessment: The Case of the 2021 Haiti Earthquake." AGILE: GIScience Series 4 (June 6, 2023): 1–11. http://dx.doi.org/10.5194/agile-giss-4-5-2023.

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Abstract. Fast and reliable geographic information is vital in disaster management. In the late 2000s, crowdsourcing emerged as a powerful method to provide this information. Base mapping through crowdsourcing is already well-established in relief workflows. However, crowdsourced post-disaster damage assessment is researched but not yet institutionalized. Based on MapSwipe, an established mobile application for crowdsourced base mapping, a damage assessment approach was developed and tested for a case study after the 2021 Haiti earthquake. First, MapSwipe’s damage mapping results are assessed for quality by using a reference dataset in regard to different aggregation methods. Then, the MapSwipe data was compared to an already established rapid damage assessment method by the Copernicus Emergency Management Service (CEMS). Crowdsourced building damage mapping achieved a maximum F1-score of 0.63 in comparison to the reference data set. MapSwipe and CEMS data showed only slight agreement with Cohen’s Kappa values reaching a maximum of 0.16. The results highlight the potential of crowdsourcing damage assessment as well as the importance for a scientific evaluation of the quality of CEMS data. Next steps for further integrating the presented workflow into MapSwipe are discussed.

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McCullagh, M., and M. Jackson. "CROWDSOURCED MAPPING – LETTING AMATEURS INTO THE TEMPLE?" ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W1 (May 22, 2013): 399–432. http://dx.doi.org/10.5194/isprsarchives-xl-1-w1-399-2013.

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Pipelidis, Georgios, Omid Moslehi Rad, Dorota Iwaszczuk, Christian Prehofer, and Urs Hugentobler. "Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data." Sensors 18, no. 2 (February 6, 2018): 480. http://dx.doi.org/10.3390/s18020480.

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Branion-Calles, Michael, Trisalyn Nelson, and 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, no. 1 (January 2017): 1–11. http://dx.doi.org/10.3141/2662-01.

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Official sources of cyclist safety data suffer from underreporting and bias. Crowdsourced safety data have the potential to supplement official sources and to provide new data on near-miss incidents. BikeMaps.org is a global online mapping tool that allows cyclists to record the location and details of near misses and collisions they experience. However, little is known about how the characteristics of near-miss and collision events compare. Further, the question remains whether the characteristics of crowdsourced collision data are similar to those of collision data captured by official insurance reports. The objectives of this study were twofold: ( a) to assess similarities and differences in near misses and collisions reported to BikeMaps.org and ( b) to assess similarities and differences in collisions reported to BikeMaps.org and to an official insurance data set. Logistic regression was used first to model the odds of crowdsourced near-miss reports as opposed to collision reports and then to model the odds of crowdsourced as opposed to official insurance collision reports, as a function of incident circumstances. The results indicated higher odds of crowdsourced reports of near misses than of crowdsourced collision reports for commute trips, interactions with motor vehicles, and in locations without bicycle-specific facilities. In addition, relative to insurance reports, crowdsourced collision reports were associated with peak traffic hours, nonintersection locations, and locations where bicycle facilities were present. These analyses indicated that crowdsourced collision data have potential to fill in gaps in reports to official collision sources and that crowdsourced near-miss reporting may be influenced by perceptions of risk.

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Lingua, Federico, Nicholas C. Coops, Valentine Lafond, Christopher Gaston, and Verena C. Griess. "Characterizing, mapping and valuing the demand for forest recreation using crowdsourced social media data." PLOS ONE 17, no. 8 (August 11, 2022): e0272406. http://dx.doi.org/10.1371/journal.pone.0272406.

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Mapping and valuing of forest recreation is time-consuming and complex, hampering its inclusion in forest management plans and hence the achievement of a fully sustainable forest management. In this study, we explore the potential of crowdsourced social media data in tackling the mapping and valuing of forest recreation demand. To do so, we assess the relationships between crowdsourced social media data, acquired from over 350,000 Flickr geotagged pictures, and demand for forest recreation in British Columbia (BC) forests. We first identify temporal and spatial trends of forest recreation demand, as well as the countries of origin of BC forests visitors. Second, we estimate the average number of annual recreational visits with a linear regression model calibrated with empirically collected secondary data. Lastly, we estimate recreational values by deriving the average consumer surpluses for the visitors of BC forested provincial parks. We find that annually, on average, over 44 million recreational experiences are completed in BC forests, with peaks during the summer months and during the weekends. Moreover, a crowdsourced travel cost approach allowed us to value the recreational ecosystem service in five forested provincial parks ranging from ~2.9 to ~35.0 million CAN$/year. Our findings demonstrate that social media data can be used to characterize, quantify and map the demand for forest recreation (especially in peri-urban forests), representing a useful tool for the inclusion of recreational values in forest management. Finally, we address the limitations of crowdsourced social media data in the study of forest recreation and the future perspectives of this rapidly growing research field.

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Дисертації з теми "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.

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Le déploiement de véhicules intelligents et connectés, dotés de capteurs de plus en plus sophistiqués, et capables de partager des positions et des trajectoires précises, permettra d’améliorer considérablement la sécurité routière et l’efficacité du trafic. Pour que ce gain de sécurité devienne effectif, les véhicules devront être géo-positionnés dans un référentiel commun avec précision, avec une erreur d’au plus quelques décimètres [1]. Pour y parvenir, ils pourront compter sur une variété de capteurs embarqués, tels que des récepteurs GNSS (Global Navigation Satellite Systems), ainsi que des capteurs proprioceptifs et des capteurs de perception. Toutefois, afin de garantir un positionnement précis dans toutes les conditions, y compris dans les zones denses où les signaux GNSS peuvent être dégradés par des effets de trajets multiples, les véhicules devront utiliser des cartes précises de l’environnement pour soutenir leurs algorithmes de localisation.Afin d’établir de telles cartes pour les principales autoroutes, les principaux acteurs automobiles ont eu recours à des flottes de véhicules spécialisés équipés de capteurs haut de gamme. Cependant, en raison des coûts opérationnels élevés qui y sont associés, ils n’ont exploité qu’un nombre limité de véhicules et ne sont pas en mesure de fournir des mises à jour en direct des cartes, ni de cartographier des réseaux routiers entiers. La cartographie crowdsourcée représente une solution rentable à ce problème et suscite aujourd’hui l’intérêt des acteurs du secteur automobile. Cette technique consiste à exploiter les mesures récupérées par de multiples véhicules de production équipés de capteurs standard, afin de construire une carte contenant des points de repère. Néanmoins, même si cette approche semble prometteuse, sa capacité réelle à construire une carte précise et à la maintenir à jour a besoin d’être évaluée dans des scénarios réalistes et long-terme.Dans cette thèse, nous proposons d’abord une solution de cartographie crowdsourcée basée sur une optimisation par triangulation, et l’évaluons à l’aide de tests de terrain. L’analyse des résultats montre le potentiel de cette approche à tirer profit des mesures émises par plusieurs véhicules. Elle permet aussi d’identifier certaines limitations critiques associées à l’optimisation par triangulation.Pour remédier à cela, nous proposons ensuite une autre solution de cartographie crowdsourcée basée sur l’optimisation de graphe, et nous introduisons différentes approches pour inclure et mettre à jour la carte dans l’optimisation, qui correspondent à différents compromis entre la qualité de la carte et la scalabilité. Des expériences de simulation sont menées afin de comparer ces approches. Les résultats permettent d’identifier la plus efficace, ainsi que de vérifier qu’elle représente une solution scalable de cartographie crowdsourcée.La robustesse de cette approche à divers types de bruits, tels que les bruits auto-corrélés et biaisés, est ensuite évaluée à l’aide de tests de simulation étendus. L’analyse des résultats montre sa capacité à construire une carte précise dans diverses conditions de bruits, en utilisant des mesures récupérées par plusieurs véhicules. Ensuite, des tests de terrain sont effectués afin de confirmer les résultats obtenus en simulation, et de tirer des conclusions tant d’un point de vue théorique que pratique. Enfin, la capacité de notre solution de cartographie crowdsourcée à améliorer les capacités de localisation des véhicules est évaluée en simulation. Les résultats montrent l’efficacité de l’approche proposée dans diverses conditions, tout en soulignant l’importance de fournir une carte avec une densité suffisante de points de repère
The 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

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Huai, Jianzhu. "Collaborative SLAM with Crowdsourced Data." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1483669256597152.

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Devoid, Alexander David, and 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.

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This thesis utilizes concepts of computational and watchdog journalism as a means to map border militarization. The study includes the creation of a mobile app that maps the global, "ground truth" (The GroundTruth Project, 2008), or direct and first-hand observations, of the rise of border building and law-enforcement militarization.

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Книги з теми "Crowdsourced Mapping"

Capineri, Cristina, Muki Haklay, Haosheng Huang, Vyron Antoniou, Juhani Kettunen, Frank Ostermann, and Ross Purves, eds. European Handbook of Crowdsourced Geographic Information. London, United Kingdom: Ubiquity Press, 2016.

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Foody, Giles, Peter Mooney, Cidália Costa Fonte, Ana Maria Olteanu Raimond, Steffen Fritz, and Linda See, eds. Mapping and the Citizen Sensor. London, United Kingdom: Ubiquity Press, 2017.

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Mooney, Peter, Giles Foody, and Linda See. Mapping and the Citizen Sensor. Saint Philip Street Press, 2020.

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Частини книг з теми "Crowdsourced Mapping"

Pődör, Andrea, and László Zentai. "Educational Aspects of Crowdsourced Noise Mapping." In Advances in Cartography and GIScience, 35–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57336-6_3.

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Sharker, Monir H., Jessica G. Benner, and Hassan A. Karimi. "On Reliability of Routes Computed Based on Crowdsourced Points of Interest." In Citizen Empowered Mapping, 153–72. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51629-5_7.

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Soden, Robert, and Leysia Palen. "From Crowdsourced Mapping to Community Mapping: The Post-earthquake Work of OpenStreetMap Haiti." In 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.

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Leao, Simone Z., and Chris Pettit. "Mapping Bicycling Patterns with an Agent-Based Model, Census and Crowdsourced Data." In 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.

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Makhortykh, Mykola. "Geospatial Data Analysis in Russia’s Geoweb." In 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.

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AbstractThe chapter examines the role of geospatial data in Russia’s online ecosystem. Facilitated by the rise of geographic information systems and user-generated content, the distribution of geospatial data has blurred the line between physical spaces and their virtual representations. The chapter discusses different sources of these data available for Digital Russian Studies (e.g., social data and crowdsourced databases) together with the novel techniques for extracting geolocation from various data formats (e.g., textual documents and images). It also scrutinizes different ways of using these data, varying from mapping the spatial distribution of social and political phenomena to investigating the use of geotag data for cultural practices’ digitization to exploring the use of geoweb for narrating individual and collective identities online.

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Nakacwa, Stellamaris, and Bert Manieson. "Cities of the Future Need to Be Both Smart and Just: How We Think Open Mapping Can Help." In Sustainable Development Goals Series, 305–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05182-1_27.

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AbstractAlong with increasing urban growth rates, especially in the global south, cities are becoming more fragile because of rapid climate change, insecurity, and increasing urban landscape challenges. With the limited budget sums, coupled with outdated and limited spatial and aspatial data, city planners, governors, and governments are left short of the optimal and efficient approaches to deploy and reckon just, smart, and sustainable cities across all populaces. This demands agile tools and applications for effective decision-making to maintain and sustainably improve quality of life with an assurance that no one is left behind. We demonstrate the potential utilization of OpenStreetMap datasets by urban planners and governing councils to enhance evidence-based planning and policy initiatives. Several projects have been pioneered and executed by youth to demonstrate their crucial role in the organization and collection of crowdsourced geospatial data as a manifestation of the broader theoretical underpinnings of urban governance encapsulated in SDG 16 – Peace, Justice, and Strong Institutions and SDG 11 – Sustainable Cities and Communities. We argue youth are communicating through the collection of the data. We demonstrate practical approaches to the inclusion of OSM and the participation of local YouthMappers chapters towards objectively positive, just urban governance.

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García-Álvarez, David, Javier Lara Hinojosa, and Jaime Quintero Villaraso. "Global General Land Use Cover Datasets with a Single Date." In 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.

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AbstractGlobal general Land Use and Land Cover (LUC) datasets map all land uses and covers across the globe, without focusing on any specific use or cover. This chapter only reviews those datasets available for one single date, which have not been updated over time. Seven different datasets are described in detail. Two other ones were identified, but are not included in this review, because of its coarsens, which limits their utility: Mathews Global Vegetation/Land Use and GMRCA LULC. The first experiences in global LUC mapping date back to the 1990s, when leading research groups in the field produced the first global LUC maps at fine scales of 1 km spatial resolution: the UMD LC Classification and the Global Land Cover Characterization. Not long afterwards, in an attempt to build on these experiences and take them a stage further, an international partnership produced GLC2000 for the reference year 2000. These initial LUC mapping projects produced maps for just one reference year and were not continued or updated over time. Subsequent projects have mostly focused on the production of timeseries of global LUC maps, which allow us to study LUC change over time (see Chapter “Global General Land Use Cover Datasets with a Time Series of Maps”). As a result, there are relatively few single-date global LUC maps for recent years of reference. The latest projects and initiatives producing global LUC maps for single dates have focused on improving the accuracy of global LUC mapping and the use of crowdsourcing production strategies. The Geo-Wiki Hybrid and GLC-SHARE datasets built on the previous research in a bid to obtain more accurate global LUC maps by merging the data from existing datasets. OSM LULC is an ongoing test project that is trying to produce a global LUC map cheaply, using crowdsourced information provided by the Open Street Maps community. The other dataset reviewed here is the LADA LUC Map, which was developed for a specific thematic project (Land Degradation Assessment in Dryland). This dataset is not comparable to the others reviewed in this chapter in terms of its purpose and nature, as is clear from its coarse spatial resolution (5 arc minutes). We therefore believe that this dataset should not be considered part of initiatives to produce more accurate, more detailed land use maps at a global level.

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Marino, Andrea, Marco Pesce, and Raffaella Succi. "Access to emergency care services and inequalities in living standards: Some evidence from two Italian northern regions." In 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.

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Rapid access to emergency medical care is crucial in reducing the implications of negative health events in terms of both mortality and disability. Thus, in a well-designed health system the geographical distribution of emergency care services should be able to minimize the share of people whose access time lies beyond critical thresholds. In spite of this, statistical information measuring accessibility to emergency care services at a highly disaggregated level is unavailable in Italy. This paper makes a step in filling this gap, by providing geographically detailed estimates of accessibility in two northern regions, Liguria and Lombardia. To do so, we use three data sources: 1) georeferenced population data measured at the currently most possible detailed level (census enumeration areas, CEAs) from the 2011 Population Census; 2) open data on location of emergency care services; 3) crowdsourced data on road travel distances. Elaborating these data with an efficient algorithm based on open source routing machine provides us with a clear mapping of particularly disadvantaged areas. We find that in 2013 the population share whose access time to emergency care services lies beyond a critical –and policy relevant- threshold of 60 minutes is fairly limited (about 0.1% in both regions). Regional differences emerge when setting lower thresholds. We briefly discuss how accessibility may have evolved in recent years, based upon some conjecture on population dynamics at the CEA level and updated information on emergency care centers. Finally, we analyze how differences in accessibility are related to a set of characteristics describing the population’s living conditions. Different results emerge. In particular, older and less educated people in Liguria face significantly lower access to emergency care. Overall, our results suggest that spatial differences in accessibility -within and between regions- should be considered a relevant determinant of health inequality.

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Hossain, A. K. M. Mahtab. "Crowdsourced Indoor Mapping." In 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.

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De Chiara, Francesca, and Maurizio Napolitano. "Mapping the Mappers." In 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.

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Volunteered geographic information (VGI) platforms generate crowdsourced layers where a vast amount of shared and shareable geo-information is available. Monitoring the informative reliability of these sources is an important task, and the main VGI project, OpenStreetMap is a good testing ground to investigate how the collective intelligence made of users' networks creates public knowledge. OpenStreetMap (OSM) can be defined as a language of representation of real geographical entities shared as web maps. Mappers often work in solitude, but they stick to and strictly respect the rules given by their community. The aim is to create a geographical database used by anyone for any purpose. The chapter explores the following questions: How many contributors are there? Where are they and what do they collect? What are the interactions between them? The chapter illustrates what can be read from the OSM data, the available tools, and what could help researchers to understand this community.

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Тези доповідей конференцій з теми "Crowdsourced Mapping"

Das, Anweshan, Joris IJsselmuiden, and Gijs Dubbelman. "Pose-graph based Crowdsourced Mapping Framework." In 2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2020. http://dx.doi.org/10.1109/cavs51000.2020.9334622.

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Akimoto, Mina, Xiaoyan Wang, Masahiro Umehira, and Yusheng Ji. "Crowdsourced Radio Environment Mapping by Exploiting Machine Learning." In 2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC). IEEE, 2019. http://dx.doi.org/10.1109/wpmc48795.2019.9096108.

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Wang, Gang, Bolun Wang, Tianyi Wang, Ana Nika, Haitao Zheng, and Ben Y. Zhao. "Defending against Sybil Devices in Crowdsourced Mapping Services." In 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.

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Stoven-Dubois, Alexis, Aziz Dziri, Bertrand Leroy, and Roland Chapuis. "Graph Optimization Methods for Large-Scale Crowdsourced Mapping." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190292.

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Apajalahti, Kasper, Ermias Andargie Walelgne, Jukka Manner, and Eero Hyvonen. "Correlation-Based Feature Mapping of Crowdsourced LTE Data." In 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.

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Butler, Crystal, Lakshmi Subramanian, and Stephanie Michalowicz. "Crowdsourced Facial Expression Mapping Using a 3D Avatar." In CHI'16: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2851581.2892535.

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Wang, Bolun. "Defending against Sybil Devices in Crowdsourced Mapping Services." In 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.

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Jia, Shuaidong, Zhicheng Liang, Lihua Zhang, and Hao Yuan. "Uncertainty Modeling of Crowdsourced Bathymetry Data Influenced by Marine Environment." In 2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS). IEEE, 2022. http://dx.doi.org/10.1109/icgmrs55602.2022.9849245.

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Ingensand, Jens, Marion Nappez, Stéphane Joost, Ivo Widmer, Olivier Ertz, and Daniel Rappo. "The Urbangene Project - Experience from a Crowdsourced Mapping Campaign." In 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.

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de Campos, Vitor Queiroz, Jose Maria N. David, and Regina Braga. "Coordination in Crowdsourced Software Development: A Systematic Mapping Study." In 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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