Статті в журналах: "Crowdsourced Mapping"

1

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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5

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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6

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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Xu, Shan, Bin Zou, Yan Lin, Xiuge Zhao, Shenxin Li, and Chenxia Hu. "Strategies of method selection for fine-scale PM<sub>2.5</sub> mapping in an intra-urban area using crowdsourced monitoring." Atmospheric Measurement Techniques 12, no. 5 (May 28, 2019): 2933–48. http://dx.doi.org/10.5194/amt-12-2933-2019.

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Abstract. Fine particulate matter (PM2.5) is of great concern to the public due to its significant risk to human health. Numerous methods have been developed to estimate spatial PM2.5 concentrations in unobserved locations due to the sparse number of fixed monitoring stations. Due to an increase in low-cost sensing for air pollution monitoring, crowdsourced monitoring of exposure control has been gradually introduced into cities. However, the optimal mapping method for conventional sparse fixed measurements may not be suitable for this new high-density monitoring approach. This study presents a crowdsourced sampling campaign and strategies of method selection for 100 m scale PM2.5 mapping in an intra-urban area of China. During this process, PM2.5 concentrations were measured by laser air quality monitors through a group of volunteers during two 5 h periods. Three extensively employed modelling methods (ordinary kriging, OK; land use regression, LUR; and regression kriging, RK) were adopted to evaluate the performance. An interesting finding is that PM2.5 concentrations in micro-environments varied in the intra-urban area. These local PM2.5 variations can be easily identified by crowdsourced sampling rather than national air quality monitoring stations. The selection of models for fine-scale PM2.5 concentration mapping should be adjusted according to the changing sampling and pollution circumstances. During this project, OK interpolation performs best in conditions with non-peak traffic situations during a lightly polluted period (holdout validation R2: 0.47–0.82), while the RK modelling can perform better during the heavily polluted period (0.32–0.68) and in conditions with peak traffic and relatively few sampling sites (fewer than ∼100) during the lightly polluted period (0.40–0.69). Additionally, the LUR model demonstrates limited ability in estimating PM2.5 concentrations on very fine spatial and temporal scales in this study (0.04–0.55), which challenges the traditional point about the good performance of the LUR model for air pollution mapping. This method selection strategy provides empirical evidence for the best method selection for PM2.5 mapping using crowdsourced monitoring, and this provides a promising way to reduce the exposure risks for individuals in their daily life.

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Nelson, Trisalyn, Avipsa Roy, Colin Ferster, Jaimy Fischer, Vanessa Brum-Bastos, Karen Laberee, Hanchen Yu, and Meghan Winters. "Generalized model for mapping bicycle ridership with crowdsourced data." Transportation Research Part C: Emerging Technologies 125 (April 2021): 102981. http://dx.doi.org/10.1016/j.trc.2021.102981.

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13

Wang, Gang, Bolun Wang, Tianyi Wang, Ana Nika, Haitao Zheng, and Ben Y. Zhao. "Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services." IEEE/ACM Transactions on Networking 26, no. 3 (June 2018): 1123–36. http://dx.doi.org/10.1109/tnet.2018.2818073.

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14

Lee, Ju Young, Sherrie Wang, Anjuli Jain Figueroa, Rob Strey, David B. Lobell, Rosamond L. Naylor, and Steven M. Gorelick. "Mapping Sugarcane in Central India with Smartphone Crowdsourcing." Remote Sensing 14, no. 3 (February 2, 2022): 703. http://dx.doi.org/10.3390/rs14030703.

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In India, the second-largest sugarcane producing country in the world, accurate mapping of sugarcane land is a key to designing targeted agricultural policies. Such a map is not available, however, as it is challenging to reliably identify sugarcane areas using remote sensing due to sugarcane’s phenological characteristics, coupled with a range of cultivation periods for different varieties. To produce a modern sugarcane map for the Bhima Basin in central India, we utilized crowdsourced data and applied supervised machine learning (neural network) and unsupervised classification methods individually and in combination. We highlight four points. First, smartphone crowdsourced data can be used as an alternative ground truth for sugarcane mapping but requires careful correction of potential errors. Second, although the supervised machine learning method performs best for sugarcane mapping, the combined use of both classification methods improves sugarcane mapping precision at the cost of worsening sugarcane recall and missing some actual sugarcane area. Third, machine learning image classification using high-resolution satellite imagery showed significant potential for sugarcane mapping. Fourth, our best estimate of the sugarcane area in the Bhima Basin is twice that shown in government statistics. This study provides useful insights into sugarcane mapping that can improve the approaches taken in other regions.

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Cho, Minwoo, Kitae Kim, Soohyun Cho, Seung-Mo Cho, and Woojin Chung. "Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul." Sensors 23, no. 1 (December 31, 2022): 438. http://dx.doi.org/10.3390/s23010438.

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Recently, HD maps have become important parts of autonomous driving, from localization to perception and path planning. For the practical application of HD maps, it is significant to regularly update environmental changes in HD maps. Conventional approaches require expensive mobile mapping systems and considerable manual work by experts, making it difficult to achieve frequent map updates. In this paper, we show how frequent and automatic updates of lane marking in HD maps are made possible with enormous crowdsourced data. Crowdsourced data is acquired from onboard low-cost sensing devices installed on many city buses and taxis in Seoul, South Korea. A large amount of crowdsourced data is daily accumulated on the server. The quality of sensor measurement is not very high due to the limited performance of low-cost devices. Therefore, the technical challenge is to overcome the uncertainty of the crowdsourced data. Appropriately filtering out a large amount of low-quality data is a significant problem. The proposed HD map update strategy comprises several processing steps including pose correction, observation assignment, observation clustering, and landmark classification. The proposed HD map update strategy is experimentally verified using crowdsourced data. If the changed environments are successfully extracted, then precisely updated HD maps are generated.

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Grove, Nicole Sunday. "The cartographic ambiguities of HarassMap: Crowdmapping security and sexual violence in Egypt." Security Dialogue 46, no. 4 (August 2015): 345–64. http://dx.doi.org/10.1177/0967010615583039.

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In December 2010, HarassMap was launched as a Cairo-based interactive online mapping interface for reporting and mapping incidents of sexual harassment anonymously and in real time, in Egypt. The project’s use of spatial information technologies for crowdmapping sexual harassment raises important questions about the use of crowdsourced mapping as a technique of global human security governance, as well as the techno-politics of interpreting and representing spaces of gendered security and insecurity in Egypt’s urban streetscape. By recoding Egypt’s urban landscape into spaces subordinated to the visual cartography of the project’s crowdsourced data, HarassMap obscures the complex assemblage that it draws together as the differentially open space of the Egyptian street – spaces that are territorialized and deterritorialized for authoritarian control, state violence, revolt, rape, new solidarities, gender reversals, sectarian tensions, and class-based mobilization. What is at stake in my analysis is the plasticity of victimage: to what extent can attempts to ‘empower’ women be pursued at the microlevel without amplifying the similarly imperial techniques of objectifying them as resources used to justify other forms of state violence? The question requires taking seriously the practices of mapping and targeting as an interface for securing public space.

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Ruggiano, Nicole, Tish Winton, Jane Daquin, Zhe Jiang, Monica Herzog, and Jeff Gray. "THE POTENTIAL OF CROWDSOURCED ASSET MAPPING TECHNOLOGIES FOR SUPPORTING DEMENTIA CAREGIVERS." Innovation in Aging 7, Supplement_1 (December 1, 2023): 534. http://dx.doi.org/10.1093/geroni/igad104.1753.

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Abstract It is well-established that caregivers of people living with dementia (PLWD) often report having difficulty finding information about education and support that is available to them in their community. Caregiver support groups have demonstrated to be an effective approach to caregiver education through mutual support, though many caregivers are unable to participate in caregiver support groups due to time and geographic constraints. Crowdsourced technologies that rely on volunteered geographic information (VGI) are a viable way of community members to provide mutual support on a variety of issues, including disaster response, traffic conditions, health, and travel. These technologies often rely on a mobile app where users upload VGI that are of interest to others who use the app. There are benefits and drawbacks of crowdsourced technologies. For example, they may be an easy way for community members to share needed information, though there may be challenges in the quality of data that users provide. This presentation will report findings from a project that examined dementia caregivers’ interest in using crowdsourced mapping technologies to share information about services and resources in their community that they have found helpful. Data were collected from a diverse sample of dementia caregivers living in Alabama through an online survey that was administered between June and November of 2022. Overall, it was found that caregivers were interested in using a crowdsourcing technology for support and education. Additional findings provide guidance for developing a crowdsourcing protocol that is based on caregivers’ reported information needs.

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Darmody, Aron, Mujde Yuksel, and Meera Venkatraman. "The work of mapping and the mapping of work: prosumer roles in crowdsourced maps." Journal of Marketing Management 33, no. 13-14 (July 17, 2017): 1093–119. http://dx.doi.org/10.1080/0267257x.2017.1348384.

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Nagaraj, Abhishek. "Does Open Data Spur Online Communities? Evidence from Crowdsourced Mapping." Academy of Management Proceedings 2017, no. 1 (August 2017): 13110. http://dx.doi.org/10.5465/ambpp.2017.13110abstract.

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Wijaya, Benny, Kun Jiang, Mengmeng Yang, Tuopu Wen, Xuewei Tang, and Diange Yang. "Crowdsourced Road Semantics Mapping Based on Pixel-Wise Confidence Level." Automotive Innovation 5, no. 1 (January 29, 2022): 43–56. http://dx.doi.org/10.1007/s42154-021-00173-x.

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Potsiou, C., N. Doulamis, N. Bakalos, M. Gkeli, and C. Ioannidis. "INDOOR LOCALIZATION FOR 3D MOBILE CADASTRAL MAPPING USING MACHINE LEARNING TECHNIQUES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences VI-4/W1-2020 (September 3, 2020): 159–66. http://dx.doi.org/10.5194/isprs-annals-vi-4-w1-2020-159-2020.

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Abstract. With the rapid global urbanization, several multi-dimensional complex infrastructures have emerged, introducing new challenges in the management of the vertically stratified buildings spaces. 3D indoor cadastral spaces consist a zestful research topic as their complexity and geometry alterations during time, prevents the assignment of the corresponding Rights, Restrictions and Responsibilities (RRR). In the absence of the necessary horizontal spatial data infrastructure/floor plans their determination is weak. In this paper a fit-for-purpose technical framework and a crowdsourced methodology for the implementation of 3D cadastral surveys focused on indoor cadastral spaces, is proposed and presented. As indoor data capturing tool, an open-sourced cadastral mobile application for Android devices, is selected and presented. An Indoor Positioning System based on Bluetooth technology is established while an innovative machine learning architecture is developed, in order to explore its potentials to automatically provide the position of the mobile device within an indoor environment, aiming to add more intelligence to the proposed 3D crowdsourced cadastral framework. A practical experiment for testing the examined technical solution is conducted. The produced results are assessed to be quite promising.

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Cahyono, Ari. "STUDI NAMA GEOGRAFI MELALUI LAYANAN PEMETAAN URUNDAYA DI DESA GIRIPURWO, PURWOSARI, GUNUNGKIDUL D.I. YOGYAKARTA." Jurnal SPATIAL Wahana Komunikasi dan Informasi Geografi 18, no. 2 (November 1, 2018): 105–14. http://dx.doi.org/10.21009/spatial.182.04.

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A geographical name is a name that identify specific feature on the earth. That features could be a settlement, administrative region, natural feature, artificial feature, unbounded region, or virtual region. Under the Law Number 4 of 2011 concerning Geospatial Information, the geographical name is one of the layer that must appear on the base map. The acquisition of geographical names can be facilitated by crowdsourcing map that are conducted by corporations or the public. The objectives of this study are 1) to carry out an inventory of geographic names through crowdsourced maps, and 2) to examine the opportunities and challenges of the study of geographic names in rural areas. We observed data from crowdsourcing maps, e.g., Google Maps, Here Maps, and OpenStreetMaps that cover Giripurwo Village. We used spatial comparison in this research. We also compared its appearances on various mapping scales. A field survey was conducted to get more qualitative information about geographical names and to test the accuracy of maps. The results showed that there were differences between the crowdsource map services in presenting the geographical names at the same scale level. We face constraints in this mapping, i.e. limited accessibility in the entire region and sparsely populated in a karst region. Conversely, the high participation of rural communities is beneficial in this mapping process.

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Vahidi, Hossein, Brian Klinkenberg, Brian Johnson, L. Moskal, and Wanglin Yan. "Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach." Remote Sensing 10, no. 7 (July 18, 2018): 1134. http://dx.doi.org/10.3390/rs10071134.

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This paper presents a collective sensing approach that integrates imperfect Volunteered Geographic Information (VGI) obtained through Citizen Science (CS) tree mapping projects with very high resolution (VHR) optical remotely sensed data for low-cost, fine-scale, and accurate mapping of trees in urban orchards. To this end, an individual tree crown (ITC) detection technique utilizing template matching (TM) was developed for extracting urban orchard trees from VHR optical imagery. To provide the training samples for the TM algorithm, remotely sensed VGI about trees including the crowdsourced data about ITC locations and their crown diameters was adopted in this study. A data quality assessment of the proposed approach in the study area demonstrated that the detected trees had a very high degree of completeness (92.7%), a high thematic accuracy (false discovery rate (FDR) = 0.090, false negative rate (FNR) = 0.073, and F1 score (F1) = 0.918), and a fair positional accuracy (root mean square error(RMSE) = 1.02 m). Overall, the proposed approach based on the crowdsourced training samples generally demonstrated a promising ITC detection performance in our pilot project.

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Pajarito, Diego, and Michael Gould. "Mapping Frictions Inhibiting Bicycle Commuting." ISPRS International Journal of Geo-Information 7, no. 10 (October 3, 2018): 396. http://dx.doi.org/10.3390/ijgi7100396.

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Urban cycling is a sustainable transport mode that many cities are promoting. However, few cities are taking advantage of geospatial technologies to represent and analyse cycling mobility based on the behavioural patterns and difficulties faced by cyclists. This study analyses a geospatial dataset crowdsourced by urban cyclists using an experimental, mobile geo-game. Fifty-seven participants recorded bicycle trips during one week periods in three cities. By aggregating them, we extracted not only the cyclists’ preferred streets but also the frictions faced during cycling. We successfully identified 284 places potentially having frictions: 71 in Münster, Germany; 70 in Castelló, Spain; and 143 in Valletta, Malta. At such places, participants recorded bicycle segments at lower speeds indicating a deviation from an ideal cycling scenario. We describe the potential frictions inhibiting bicycle commuting with regard to the distance to bicycle paths, surrounding infrastructure, and location in the urban area.

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Sun, Y., A. Kruspe, L. Meng, Y. Tian, E. J. Hoffmann, S. Auer, and X. X. Zhu. "TOWARDS LARGE-SCALE BUILDING ATTRIBUTE MAPPING USING CROWDSOURCED IMAGES: SCENE TEXT RECOGNITION ON FLICKR AND PROBLEMS TO BE SOLVED." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 225–32. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-225-2023.

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Abstract. Crowdsourced platforms provide huge amounts of street-view images that contain valuable building information. This work addresses the challenges in applying Scene Text Recognition (STR) in crowdsourced street-view images for building attribute mapping. We use Flickr images, particularly examining texts on building facades. A Berlin Flickr dataset is created, and pre-trained STR models are used for text detection and recognition. Manual checking on a subset of STR-recognized images demonstrates high accuracy. We examined the correlation between STR results and building functions, and analysed instances where texts were recognized on residential buildings but not on commercial ones. Further investigation revealed significant challenges associated with this task, including small text regions in street-view images, the absence of ground truth labels, and mismatches in buildings in Flickr images and building footprints in OpenStreetMap (OSM). To develop city-wide mapping beyond urban hotspot locations, we suggest differentiating the scenarios where STR proves effective while developing appropriate algorithms or bringing in additional data for handling other cases. Furthermore, interdisciplinary collaboration should be undertaken to understand the motivation behind building photography and labeling. The STR-on-Flickr results are publicly available at https://github.com/ya0-sun/STR-Berlin.

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Roelandt, N., P. Aumond, and L. Moisan. "CROWDSOURCED ACOUSTIC OPEN DATA ANALYSIS WITH FOSS4G TOOLS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W1-2022 (August 6, 2022): 387–93. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-387-2022.

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Abstract. NoiseCapture is an Android application developed by the Gustave Eiffel University and the French National Centre for Scientific Research as central element of a participatory approach to environmental noise mapping. The application is open-source, and all its data are available freely. This study presents the results of the first exploratory analysis of 3 years of data collection through the lens of sound sources. This analysis is only based on the tags given by the users and not on the sound spectrum of the measurement, which will be studied at a later stage. The first results are encouraging, we were able to observe well known temporal sound source dynamics like road sounds temporal dynamic related to commuting or bird songs in the dataset. We also found correlations between wind and rain tags and their measurements by the the national meteorological service. The context of the study, the Free and Open Source Software tools and techniques used and literate programming benefits are presented.

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Zhou, Mu, Qiao Zhang, Zengshan Tian, Yiyao Liu, and Zhenyuan Zhang. "Simultaneous pathway mapping and behavior understanding with crowdsourced sensing in WLAN environment." Ad Hoc Networks 58 (April 2017): 160–70. http://dx.doi.org/10.1016/j.adhoc.2016.09.002.

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Huynh, Trung Dong, Mark Ebden, Matteo Venanzi, Sarvapali Ramchurn, Stephen Roberts, and Luc Moreau. "Interpretation of Crowdsourced Activities Using Provenance Network Analysis." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 1 (November 3, 2013): 78–85. http://dx.doi.org/10.1609/hcomp.v1i1.13067.

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Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analyzing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein.

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Qin, H., A. O. Aburizaiza, R. M. Rice, F. Paez, and M. T. Rice. "OBSTACLE CHARACTERIZATION IN A GEOCROWDSOURCED ACCESSIBILITY SYSTEM." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 19, 2015): 179–85. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-179-2015.

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Transitory obstacles – random, short-lived and unpredictable objects – are difficult to capture in any traditional mapping system, yet they have significant negative impacts on the accessibility of mobility- and visually-impaired individuals. These transitory obstacles include sidewalk obstructions, construction detours, and poor surface conditions. To identify these obstacles and assist the navigation of mobility- and visually- impaired individuals, crowdsourced mapping applications have been developed to harvest and analyze the volunteered obstacles reports from local students, faculty, staff, and residents. In this paper, we introduce a training program designed and implemented for recruiting and motivating contributors to participate in our geocrowdsourced accessibility system, and explore the quality of geocrowdsourced data with a comparative analysis methodology.

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Parés, M. E., D. Garcia, and F. Vázquez-Gallego. "MAPPING AIR QUALITY WITH A MOBILE CROWDSOURCED AIR QUALITY MONITORING SYSTEM (C-AQM)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 685–90. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-685-2020.

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Abstract. World cities are currently facing one of the major crisis of the last century. Some preliminary studies on COVID-19 pandemia have shown that air pollutants may have a strong impact on virus effects. Improved gas sensors and wireless communication systems open the door to the design of new air monitoring systems based on citizen science to better monitor and communicate the air quality levels. In this paper, we present the Crowdsourced Air Quality Monitoring (C-AQM) system, which relies on Air Quality Monitoring reference stations and a cluster of new low-cost and low-energy sensor nodes, in order to improve the resolution of air quality maps. The data collected by the C-AQM system is stored in a time series database and is available both to city council managers for decision making and to citizens for informative purposes. In this paper, we present the main bases of the C-AQM system as well as the measurements validation campaign carried out.

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Vaz, Eric, and Jamal Jokar Arsanjani. "Crowdsourced mapping of land use in urban dense environments: An assessment of Toronto." Canadian Geographer / Le Géographe canadien 59, no. 2 (March 9, 2015): 246–55. http://dx.doi.org/10.1111/cag.12170.

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Zhou, Mu, Qiao Zhang, Yu Wang, and Zengshan Tian. "Hotspot Ranking Based Indoor Mapping and Mobility Analysis Using Crowdsourced Wi-Fi Signal." IEEE Access 5 (2017): 3594–602. http://dx.doi.org/10.1109/access.2017.2674798.

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Venter, Zander S., Oscar Brousse, Igor Esau, and Fred Meier. "Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data." Remote Sensing of Environment 242 (June 2020): 111791. http://dx.doi.org/10.1016/j.rse.2020.111791.

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Créquit, Perrine, Ghizlène Mansouri, Mehdi Benchoufi, Alexandre Vivot, and Philippe Ravaud. "Mapping of Crowdsourcing in Health: Systematic Review." Journal of Medical Internet Research 20, no. 5 (May 15, 2018): e187. http://dx.doi.org/10.2196/jmir.9330.

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Background Crowdsourcing involves obtaining ideas, needed services, or content by soliciting Web-based contributions from a crowd. The 4 types of crowdsourced tasks (problem solving, data processing, surveillance or monitoring, and surveying) can be applied in the 3 categories of health (promotion, research, and care). Objective This study aimed to map the different applications of crowdsourcing in health to assess the fields of health that are using crowdsourcing and the crowdsourced tasks used. We also describe the logistics of crowdsourcing and the characteristics of crowd workers. Methods MEDLINE, EMBASE, and ClinicalTrials.gov were searched for available reports from inception to March 30, 2016, with no restriction on language or publication status. Results We identified 202 relevant studies that used crowdsourcing, including 9 randomized controlled trials, of which only one had posted results at ClinicalTrials.gov. Crowdsourcing was used in health promotion (91/202, 45.0%), research (73/202, 36.1%), and care (38/202, 18.8%). The 4 most frequent areas of application were public health (67/202, 33.2%), psychiatry (32/202, 15.8%), surgery (22/202, 10.9%), and oncology (14/202, 6.9%). Half of the reports (99/202, 49.0%) referred to data processing, 34.6% (70/202) referred to surveying, 10.4% (21/202) referred to surveillance or monitoring, and 5.9% (12/202) referred to problem-solving. Labor market platforms (eg, Amazon Mechanical Turk) were used in most studies (190/202, 94%). The crowd workers’ characteristics were poorly reported, and crowdsourcing logistics were missing from two-thirds of the reports. When reported, the median size of the crowd was 424 (first and third quartiles: 167-802); crowd workers’ median age was 34 years (32-36). Crowd workers were mainly recruited nationally, particularly in the United States. For many studies (58.9%, 119/202), previous experience in crowdsourcing was required, and passing a qualification test or training was seldom needed (11.9% of studies; 24/202). For half of the studies, monetary incentives were mentioned, with mainly less than US $1 to perform the task. The time needed to perform the task was mostly less than 10 min (58.9% of studies; 119/202). Data quality validation was used in 54/202 studies (26.7%), mainly by attention check questions or by replicating the task with several crowd workers. Conclusions The use of crowdsourcing, which allows access to a large pool of participants as well as saving time in data collection, lowering costs, and speeding up innovations, is increasing in health promotion, research, and care. However, the description of crowdsourcing logistics and crowd workers’ characteristics is frequently missing in study reports and needs to be precisely reported to better interpret the study findings and replicate them.

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Liu, L., B. Zhou, and X. Yi. "A PILOT STUDY OF URBAN POI MAPPING USING CROWDSOURCED STREET-LEVEL IMAGERY AND DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 1, 2022): 261–66. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-261-2022.

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Abstract. Point-of-interest (POI) data contains rich semantic and spatial information, having a wide range of applications including land use, transport planning and driving navigation. However, urban POI mapping traditionally requires a lot of manpower and material resources, which only few institutions or enterprises can afford to. With the increasing amount of street-level imagery, it is possible to directly extract POI-related information from such data and automatically map the distribution of urban POIs. In the pilot study, we mainly focused on extracting POIs from billboards in street-level imagery. Firstly, the you only look once (YOLO) algorithm was considered to locate billboards in the imagery, then an optical character recognition (OCR) model was adopted to extract POI-related semantic information from the detected billboard, and finally the extracted semantic text was further processed to obtain POI results. The preliminary study shows that it is a promising way of mapping urban POIs from crowdsourced street-level data using deep learning techniques.

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Dixon, Barnali, RebeccaA Johns, and Amada Fernandez. "The role of crowdsourced data, participatory decision-making and mapping of flood related events." Applied Geography 128 (March 2021): 102393. http://dx.doi.org/10.1016/j.apgeog.2021.102393.

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Brown, Greg, Clive McAlpine, Jonathan Rhodes, Daniel Lunney, Ross Goldingay, Kelly Fielding, Scott Hetherington, et al. "Assessing the validity of crowdsourced wildlife observations for conservation using public participatory mapping methods." Biological Conservation 227 (November 2018): 141–51. http://dx.doi.org/10.1016/j.biocon.2018.09.016.

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Lwin, May O., Santosh Vijaykumar, Owen Noel Newton Fernando, Siew Ann Cheong, Vajira Sampath Rathnayake, Gentatsu Lim, Yin-Leng Theng, Subhasis Chaudhuri, and Schubert Foo. "A 21st century approach to tackling dengue: Crowdsourced surveillance, predictive mapping and tailored communication." Acta Tropica 130 (February 2014): 100–107. http://dx.doi.org/10.1016/j.actatropica.2013.09.021.

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Huang, Xiao, Di Yang, Yaqian He, Peder Nelson, Russanne Low, Shawna McBride, Jessica Mitchell, and Michael Guarraia. "Land cover mapping via crowdsourced multi-directional views: The more directional views, the better." International Journal of Applied Earth Observation and Geoinformation 122 (August 2023): 103382. http://dx.doi.org/10.1016/j.jag.2023.103382.

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Cheng, Kai, Yanjun Su, Hongcan Guan, Shengli Tao, Yu Ren, Tianyu Hu, Keping Ma, Yanhong Tang, and Qinghua Guo. "Mapping China’s planted forests using high resolution imagery and massive amounts of crowdsourced samples." ISPRS Journal of Photogrammetry and Remote Sensing 196 (February 2023): 356–71. http://dx.doi.org/10.1016/j.isprsjprs.2023.01.005.

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Schnebele, E., G. Cervone, and N. Waters. "Road assessment after flood events using non-authoritative data." Natural Hazards and Earth System Sciences Discussions 1, no. 4 (August 22, 2013): 4155–79. http://dx.doi.org/10.5194/nhessd-1-4155-2013.

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Abstract. This research proposes a methodology that leverages non-authoritative data to augment flood extent mapping and the evaluation of transportation infrastructure. The novelty of this approach is the application of freely available, non-authoritative data and its integration with established data and methods. Crowdsourced photos and volunteered geographic data are fused together using a geostatistical interpolation to create an estimation of flood damage in New York City following Hurricane Sandy. This damage assessment is utilized to augment an authoritative storm surge map as well as to create a road damage map for the affected region.

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Schnebele, E., G. Cervone, and N. Waters. "Road assessment after flood events using non-authoritative data." Natural Hazards and Earth System Sciences 14, no. 4 (April 28, 2014): 1007–15. http://dx.doi.org/10.5194/nhess-14-1007-2014.

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Abstract. This research proposes a methodology that leverages non-authoritative data to augment flood extent mapping and the evaluation of transportation infrastructure. The novelty of this approach is the application of freely available, non-authoritative data and its integration with established data and methods. Crowdsourced photos and volunteered geographic data are fused together using a geostatistical interpolation to create an estimation of flood damage in New York City following Hurricane Sandy. This damage assessment is utilized to augment an authoritative storm surge map as well as to create a road damage map for the affected region.

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Zourlidou, Stefania, Monika Sester, and Shaohan Hu. "Recognition of Intersection Traffic Regulations from Crowdsourced Data." ISPRS International Journal of Geo-Information 12, no. 1 (December 23, 2022): 4. http://dx.doi.org/10.3390/ijgi12010004.

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In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO2 emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%.

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Wang, Sherrie, Stefania Di Tommaso, Joey Faulkner, Thomas Friedel, Alexander Kennepohl, Rob Strey, and David B. Lobell. "Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning." Remote Sensing 12, no. 18 (September 11, 2020): 2957. http://dx.doi.org/10.3390/rs12182957.

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High resolution satellite imagery and modern machine learning methods hold the potential to fill existing data gaps in where crops are grown around the world at a sub-field level. However, high resolution crop type maps have remained challenging to create in developing regions due to a lack of ground truth labels for model development. In this work, we explore the use of crowdsourced data, Sentinel-2 and DigitalGlobe imagery, and convolutional neural networks (CNNs) for crop type mapping in India. Plantix, a free app that uses image recognition to help farmers diagnose crop diseases, logged 9 million geolocated photos from 2017–2019 in India, 2 million of which are in the states of Andhra Pradesh and Telangana in India. Crop type labels based on farmer-submitted images were added by domain experts and deep CNNs. The resulting dataset of crop type at coordinates is high in volume, but also high in noise due to location inaccuracies, submissions from out-of-field, and labeling errors. We employed a number of steps to clean the dataset, which included training a CNN on very high resolution DigitalGlobe imagery to filter for points that are within a crop field. With this cleaned dataset, we extracted Sentinel time series at each point and trained another CNN to predict the crop type at each pixel. When evaluated on the highest quality subset of crowdsourced data, the CNN distinguishes rice, cotton, and “other” crops with 74% accuracy in a 3-way classification and outperforms a random forest trained on harmonic regression features. Furthermore, model performance remains stable when low quality points are introduced into the training set. Our results illustrate the potential of non-traditional, high-volume/high-noise datasets for crop type mapping, some improvements that neural networks can achieve over random forests, and the robustness of such methods against moderate levels of training set noise. Lastly, we caution that obstacles like the lack of good Sentinel-2 cloud mask, imperfect mobile device location accuracy, and preservation of privacy while improving data access will need to be addressed before crowdsourcing can widely and reliably be used to map crops in smallholder systems.

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Gardner, Z., P. Mooney, S. De Sabbata, and L. Dowthwaite. "Quantifying gendered participation in OpenStreetMap: responding to theories of female (under) representation in crowdsourced mapping." GeoJournal 85, no. 6 (June 29, 2019): 1603–20. http://dx.doi.org/10.1007/s10708-019-10035-z.

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Abstract This paper presents the results of an exploratory quantitative analysis of gendered contributions to the online mapping project OpenStreetMap (OSM), in which previous research has identified a strong male participation bias. On these grounds, theories of representation in volunteered geographic information (VGI) have argued that this kind of crowdsourced data fails to embody the geospatial interests of the wider community. The observed effects of the bias however, remain conspicuously absent from discourses of VGI and gender, which proceed with little sense of impact. This study addresses this void by analysing OSM contributions by gender and thus identifies differences in men’s and women’s mapping practices. An online survey uniquely captured the OSM IDs as well as the declared gender of 293 OSM users. Statistics relating to users’ editing and tagging behaviours openly accessible via the ‘how did you contribute to OSM’ wiki page were subsequently analysed. The results reveal that volumes of overall activity as well editing and tagging actions in OSM remain significantly dominated by men. They also indicate subtle but impactful differences in men’s and women’s preferences for modifying and creating data, as well as the tagging categories to which they contribute. Discourses of gender and ICT, gender relations in online VGI environments and competing motivational factors are implicated in these observations. As well as updating estimates of the gender participation bias in OSM, this paper aims to inform and stimulate subsequent discourses of gender and representation towards a new rationale for widening participation in VGI.

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Samsonov, Timofey, Anastasia Shurygina, Mikhail Varentsov, Pavel Kargashin, Yulia Yarynich, and Pavel Konstantinov. "Interactive web mapping for urban climate monitoring and research based on reference and crowdsourced observations." Abstracts of the ICA 6 (August 12, 2023): 1–2. http://dx.doi.org/10.5194/ica-abs-6-219-2023.

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Etherington, Thomas. "Mapping uncertain spatial object extents from point samples using fuzzy alpha-shapes." Journal of Spatial Information Science, no. 26 (May 17, 2023): 79–98. http://dx.doi.org/10.5311/josis.2023.26.254.

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Mapping the extent of spatial objects from point samples is a fundamental process in geographical analysis. Computational geometry methods are commonly used, and one method that has been proposed is the alpha-shape as it is insensitive to both bias and errors that are common in crowdsourced geographic data and big geographic data more generally. However, many spatial objects are uncertain in nature, with vague boundaries that are not well represented by the current use of discrete alpha-shapes. Fuzzy alpha-shapes are presented as a highly generic and adaptable methodology that can produce maps of spatial objects that recognise the vague and uncertain nature of many geographies. A series of virtual geography experiments demonstrate that fuzzy alpha-shapes avoid the need for binary thresholds, create a model that better represents the uncertain boundaries of some spatial objects, while also retaining the robustness to errors and bias that motivated the original use of alpha-shapes for mapping spatial objects.

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Karasov, Oleksandr, Stien Heremans, Mart Külvik, Artem Domnich, and Igor Chervanyov. "On How Crowdsourced Data and Landscape Organisation Metrics Can Facilitate the Mapping of Cultural Ecosystem Services: An Estonian Case Study." Land 9, no. 5 (May 19, 2020): 158. http://dx.doi.org/10.3390/land9050158.

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Social media continues to grow, permanently capturing our digital footprint in the form of texts, photographs, and videos, thereby reflecting our daily lives. Therefore, recent studies are increasingly recognising passively crowdsourced geotagged photographs retrieved from location-based social media as suitable data for quantitative mapping and assessment of cultural ecosystem service (CES) flow. In this study, we attempt to improve CES mapping from geotagged photographs by combining natural language processing, i.e., topic modelling and automated machine learning classification. Our study focuses on three main groups of CESs that are abundant in outdoor social media data: landscape watching, active outdoor recreation, and wildlife watching. Moreover, by means of a comparative viewshed analysis, we compare the geographic information system- and remote sensing-based landscape organisation metrics related to landscape coherence and colour harmony. We observed the spatial distribution of CESs in Estonia and confirmed that colour harmony indices are more strongly associated with landscape watching and outdoor recreation, while landscape coherence is more associated with wildlife watching. Both CES use and values of landscape organisation indices are land cover-specific. The suggested methodology can significantly improve the state-of-the-art with regard to CES mapping from geotagged photographs, and it is therefore particularly relevant for monitoring landscape sustainability.

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Brovelli, M. A., and E. Guilbert. "PREFACE – ISPRS WORKSHOP ON COLLABORATIVE CROWDSOURCED CLOUD MAPPING AND GEOSPATIAL BIG DATA (C3M&GBD 2019)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1493–94. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1493-2019.

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Mascitelli, A., M. Ravanelli, S. Mattoccia, C. Berardocco, and A. Mazzoni. "A COMPLETE FOS APPROACH FOR INDOOR CROWDSOURCED MAPPING: CASE STUDY ON SAPIENZA UNIVERSITY OF ROME FACULTIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 361–65. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-361-2020.

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Abstract. Indoor mapping is an essential process in several applications such as the visualization of space and its utilization, security and resource planning, emergency planning and location-based alerts and, last but not least, indoor navigation. In this work, a completely free and open-source (FOS) approach to map indoor environments, and to navigate through them, is presented. Our tests were carried out within Sapienza University of Rome public buildings; in detail, Letters and Philosophy faculty and Engineering faculty indoor environments were mapped. To reach this goal, only open source software such as Quantum GIS (QGIS) and open-source platforms like Open Street Map (OSM) and its indoor viewer, Open Level Up (OLU) were adopted. A database of indoor environments of the two faculties, completely compatible with OLU, was created through QGIS. In this way, a public territorial information system of classrooms, offices and laboratories is accessible to everyone who can, hence, add or modify the information, following the principle of crowdsourcing and of Volunteered Geographic Information (VGI). The developed procedure is now standard and its outputs accepted by the OSM community. Hence, the long-term developments of this project are the proposal for the volunteered and cooperative indoor mapping and design of strategic buildings and infrastructures (hospitals, schools, public offices, shopping centers, stations, airports etc.), starting from the available information (indoor layouts) and knowledge acquired through experience of people who normally work inside them and/or visit them frequently. In this context it is possible to state that the development of VGI for internal maps for strategic buildings, infrastructures and denied GNSS environments, not only supports and improves internal and external navigation without interruption, but can also have a significant positive impact on security and emergency management.

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