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Dr. Levente Juhasz
GIS Center, Florida International University, United States

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0 Spatial Analysis
0 Geocomputation
0 GIScience
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Journal article
Published: 15 December 2020 in ISPRS International Journal of Geo-Information
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Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

ACS Style

Anna Kovacs-Györi; Alina Ristea; Clemens Havas; Michael Mehaffy; Hartwig H. Hochmair; Bernd Resch; Levente Juhasz; Arthur Lehner; Laxmi Ramasubramanian; Thomas Blaschke. Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning. ISPRS International Journal of Geo-Information 2020, 9, 752 .

AMA Style

Anna Kovacs-Györi, Alina Ristea, Clemens Havas, Michael Mehaffy, Hartwig H. Hochmair, Bernd Resch, Levente Juhasz, Arthur Lehner, Laxmi Ramasubramanian, Thomas Blaschke. Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning. ISPRS International Journal of Geo-Information. 2020; 9 (12):752.

Chicago/Turabian Style

Anna Kovacs-Györi; Alina Ristea; Clemens Havas; Michael Mehaffy; Hartwig H. Hochmair; Bernd Resch; Levente Juhasz; Arthur Lehner; Laxmi Ramasubramanian; Thomas Blaschke. 2020. "Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning." ISPRS International Journal of Geo-Information 9, no. 12: 752.

Article commentary
Published: 23 June 2020 in Dialogues in Human Geography
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A proliferation of web-based maps have appeared depicting many different aspects of the spread of the novel coronavirus (SARS-CoV-2). In this commentary, we consider the usage of web-based mapping during the COVID-19 pandemic and argue that web maps have been widely misused for delivering public information on this fast moving, epidemiologically complex, and geographically unbounded process.

ACS Style

Peter Mooney; Levente Juhász. Mapping COVID-19: How web-based maps contribute to the infodemic. Dialogues in Human Geography 2020, 10, 265 -270.

AMA Style

Peter Mooney, Levente Juhász. Mapping COVID-19: How web-based maps contribute to the infodemic. Dialogues in Human Geography. 2020; 10 (2):265-270.

Chicago/Turabian Style

Peter Mooney; Levente Juhász. 2020. "Mapping COVID-19: How web-based maps contribute to the infodemic." Dialogues in Human Geography 10, no. 2: 265-270.

Journal article
Published: 26 March 2020 in ISPRS International Journal of Geo-Information
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User-generated map data is increasingly used by the technology industry for background mapping, navigation and beyond. An example is the integration of OpenStreetMap (OSM) data in widely-used smartphone and web applications, such as Pokémon GO (PGO), a popular augmented reality smartphone game. As a result of OSM’s increased popularity, the worldwide audience that uses OSM through external applications is directly exposed to malicious edits which represent cartographic vandalism. Multiple reports of obscene and anti-semitic vandalism in OSM have surfaced in popular media over the years. These negative news related to cartographic vandalism undermine the credibility of collaboratively generated maps. Similarly, commercial map providers (e.g., Google Maps and Waze) are also prone to carto-vandalism through their crowdsourcing mechanism that they may use to keep their map products up-to-date. Using PGO as an example, this research analyzes harmful edits in OSM that originate from PGO players. More specifically, this paper analyzes the spatial, temporal and semantic characteristics of PGO carto-vandalism and discusses how the mapping community handles it. Our findings indicate that most harmful edits are quickly discovered and that the community becomes faster at detecting and fixing these harmful edits over time. Gaming related carto-vandalism in OSM was found to be a short-term, sporadic activity by individuals, whereas the task of fixing vandalism is persistently pursued by a dedicated user group within the OSM community. The characteristics of carto-vandalism identified in this research can be used to improve vandalism detection systems in the future.

ACS Style

Levente Juhász; Tessio Novack; Hartwig H. Hochmair; Sen Qiao. Cartographic Vandalism in the Era of Location-Based Games—The Case of OpenStreetMap and Pokémon GO. ISPRS International Journal of Geo-Information 2020, 9, 197 .

AMA Style

Levente Juhász, Tessio Novack, Hartwig H. Hochmair, Sen Qiao. Cartographic Vandalism in the Era of Location-Based Games—The Case of OpenStreetMap and Pokémon GO. ISPRS International Journal of Geo-Information. 2020; 9 (4):197.

Chicago/Turabian Style

Levente Juhász; Tessio Novack; Hartwig H. Hochmair; Sen Qiao. 2020. "Cartographic Vandalism in the Era of Location-Based Games—The Case of OpenStreetMap and Pokémon GO." ISPRS International Journal of Geo-Information 9, no. 4: 197.

Journal article
Published: 01 January 2020 in GI_Forum
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SafeGraph is a commercial provider of massive Point of Interest (POI) data, including visitation patterns in North America. Although the data source does not share specific travel trajectories, the data available includes daily and monthly POI visitation numbers for over 160 categories, as well as information about where visitors come from and which other POI categories they visit. This allows analysts to gain insight into travel behavior in a geographic region over time. This study analyzes various aspects of visitation patterns that can be derived from the SafeGraph dataset for Florida. Using three major Florida cities, namely Miami, Orlando and Jacksonville, temporal patterns of daily and monthly visit numbers are correlated between various POI categories, and the effect of a short event (Hurricane Irma) on daily visitation numbers around the event is explored. In addition, travel distances from home to POIs are compared between different POI categories, and Ordinary Least Squares (OLS) regression models are used to identify factors associated with increased or decreased distance between home and a specific POI category. The study concludes that the aggregated data provided on the SafeGraph platform helps the GIScience community to learn more about travel patterns in both the spatial and the temporal domains.

ACS Style

Levente Juhász; Hartwig Hochmair. Studying Spatial and Temporal Visitation Patterns of Points of Interest Using SafeGraph Data in Florida. GI_Forum 2020, 1, 119 -136.

AMA Style

Levente Juhász, Hartwig Hochmair. Studying Spatial and Temporal Visitation Patterns of Points of Interest Using SafeGraph Data in Florida. GI_Forum. 2020; 1 ():119-136.

Chicago/Turabian Style

Levente Juhász; Hartwig Hochmair. 2020. "Studying Spatial and Temporal Visitation Patterns of Points of Interest Using SafeGraph Data in Florida." GI_Forum 1, no. : 119-136.

Journal article
Published: 11 October 2019 in ISPRS International Journal of Geo-Information
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Due to the high demand for cheap construction materials, clay-made brick manufacturing has become a thriving industry in Bangladesh, with manufacturing kilns heavily concentrated in the peripheries of larger cities and towns. These manufacturing sites, known as brickfields, operate using centuries-old technologies which expel dust, ash, black smoke and other pollutants into the atmosphere. This in turn impacts the air quality of cities and their surroundings and may also have broader impacts on health, the environment, and potentially contribute to global climate change. Using remotely sensed Landsat imagery, this study identifies brickfield locations and areal expansion between 1990 and 2015 in Dhaka, and employs spatial statistics methods including quadrat analysis and Ripley’s K-function to analyze the spatial variation of brickfield locations. Finally, using nearest neighbor distance as density functions, the distance between brickfield locations and six major geographical features (i.e., urban, rural settlement, wetland, river, highway, and local road) were estimated to investigate the threat posed by the presence of such polluting brickfields nearby urban, infrastructures and other natural areas. Results show significant expansion of brickfields both in number and clusters between 1990 and 2015 with brickfields increasing in number from 247 to 917 (total growth rate 271%) across the Dhaka urban center. The results also reveal that brickfield locations are spatially clustered: 78% of brickfields are located on major riverbanks and 40% of the total are located in ecologically sensitive wetlands surrounding Dhaka. Additionally, the average distance from the brick manufacturing plant to the nearest urban area decreased from 1500 m to 500 m over the study period. This research highlights the increasing threats to the environment, human health, and the sustainability of the megacity Dhaka from brickfield expansion in the immediate peripheral areas of its urban center. Findings and methods presented in this study can facilitate data-driven decision making by government officials and city planners to formulate strategies for improved brick production technologies and decreased environmental impacts for this urban region in Bangladesh.

ACS Style

Mohammad Mehedy Hassan; Levente Juhász; Jane Southworth. Mapping Time-Space Brickfield Development Dynamics in Peri-Urban Area of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information 2019, 8, 447 .

AMA Style

Mohammad Mehedy Hassan, Levente Juhász, Jane Southworth. Mapping Time-Space Brickfield Development Dynamics in Peri-Urban Area of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information. 2019; 8 (10):447.

Chicago/Turabian Style

Mohammad Mehedy Hassan; Levente Juhász; Jane Southworth. 2019. "Mapping Time-Space Brickfield Development Dynamics in Peri-Urban Area of Dhaka, Bangladesh." ISPRS International Journal of Geo-Information 8, no. 10: 447.

Journal article
Published: 01 January 2019 in GI_Forum
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ACS Style

Levente Juhász; Hartwig Hochmair. Comparing the Spatial and Temporal Activity Patterns between Snapchat, Twitter and Flickr in Florida. GI_Forum 2019, 1, 134 -147.

AMA Style

Levente Juhász, Hartwig Hochmair. Comparing the Spatial and Temporal Activity Patterns between Snapchat, Twitter and Flickr in Florida. GI_Forum. 2019; 1 ():134-147.

Chicago/Turabian Style

Levente Juhász; Hartwig Hochmair. 2019. "Comparing the Spatial and Temporal Activity Patterns between Snapchat, Twitter and Flickr in Florida." GI_Forum 1, no. : 134-147.

Article
Published: 15 March 2018 in ISPRS International Journal of Geo-Information
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This paper presents the results of a study that explored if and how an OpenStreetMap (OSM) data import task can contribute to OSM community growth. Different outreach techniques were used to introduce a building import task to three targeted OSM user groups. First, existing OSM members were contacted and asked to join the data import project. Second, several local community events were organized with Maptime Miami to engage local mappers in OSM contribution activities. Third, the import task was introduced as an extra credit assignment in two GIS courses at the University of Florida. The paper analyzes spatio-temporal user contributions of these target groups to assess the effectiveness of the different outreach techniques for recruitment and retention of OSM contributors. Results suggest that the type of prospective users that were contacted through our outreach efforts, and their different motivations play a major role in their editing activity. Results also revealed differences in editing patterns between newly recruited users and already established mappers. More specifically, long-term engagement of newly registered OSM mappers did not succeed, whereas already established contributors continued to import and improve data. In general, we found that an OSM data import project can add valuable data to the map, but also that encouraging long-term engagement of new users, whether it be within the academic environment or outside, proved to be challenging.

ACS Style

Levente Juhász; Hartwig H. Hochmair. OSM Data Import as an Outreach Tool to Trigger Community Growth? A Case Study in Miami. ISPRS International Journal of Geo-Information 2018, 7, 113 .

AMA Style

Levente Juhász, Hartwig H. Hochmair. OSM Data Import as an Outreach Tool to Trigger Community Growth? A Case Study in Miami. ISPRS International Journal of Geo-Information. 2018; 7 (3):113.

Chicago/Turabian Style

Levente Juhász; Hartwig H. Hochmair. 2018. "OSM Data Import as an Outreach Tool to Trigger Community Growth? A Case Study in Miami." ISPRS International Journal of Geo-Information 7, no. 3: 113.

Conference paper
Published: 09 December 2017 in Lecture Notes in Geoinformation and Cartography
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A variety of location based services, including navigation, geo-gaming, advertising, and vacation planning, rely on Point of Interest (POI) data. Mapping platforms and social media apps oftentimes host their own geo-datasets which leads to a plethora of data sources from which POIs can be extracted. Therefore it is crucial for an analyst to understand the nature of the data that are available on the different platforms, their purpose, their characteristics, and their data quality. This study extracts POIs for seven urban regions from seven mapping and social media platforms (Facebook, Foursquare, Google, Instagram, OSM, Twitter, and Yelp). It analyzes the POI data quality regarding coverage, point density, content classification, and positioning accuracy, and also examines the spatial relationship (e.g. segregation) between POIs from different platforms.

ACS Style

Hartwig H. Hochmair; Levente Juhász; Sreten Cvetojevic. Data Quality of Points of Interest in Selected Mapping and Social Media Platforms. Lecture Notes in Geoinformation and Cartography 2017, 293 -313.

AMA Style

Hartwig H. Hochmair, Levente Juhász, Sreten Cvetojevic. Data Quality of Points of Interest in Selected Mapping and Social Media Platforms. Lecture Notes in Geoinformation and Cartography. 2017; ():293-313.

Chicago/Turabian Style

Hartwig H. Hochmair; Levente Juhász; Sreten Cvetojevic. 2017. "Data Quality of Points of Interest in Selected Mapping and Social Media Platforms." Lecture Notes in Geoinformation and Cartography , no. : 293-313.

Journal article
Published: 03 July 2017 in Geo-spatial Information Science
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ACS Style

Levente Juhász; Hartwig H. Hochmair. Where to catch ‘em all? – a geographic analysis of Pokémon Go locations. Geo-spatial Information Science 2017, 20, 241 -251.

AMA Style

Levente Juhász, Hartwig H. Hochmair. Where to catch ‘em all? – a geographic analysis of Pokémon Go locations. Geo-spatial Information Science. 2017; 20 (3):241-251.

Chicago/Turabian Style

Levente Juhász; Hartwig H. Hochmair. 2017. "Where to catch ‘em all? – a geographic analysis of Pokémon Go locations." Geo-spatial Information Science 20, no. 3: 241-251.

Data descriptor
Published: 24 September 2016 in Data
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An increasing number of Volunteered Geographic Information (VGI) and social media platforms have been continuously growing in size, which have provided massive georeferenced data in many forms including textual information, photographs, and geoinformation. These georeferenced data have either been actively contributed (e.g., adding data to OpenStreetMap (OSM) or Mapillary) or collected in a more passive fashion by enabling geolocation whilst using an online platform (e.g., Twitter, Instagram, or Flickr). The benefit of scraping and streaming these data in stand-alone applications is evident, however, it is difficult for many users to script and scrape the diverse types of these data. On 14 June 2016, a pre-conference workshop at the AGILE 2016 conference in Helsinki, Finland was held. The workshop was called “LINK-VGI: LINKing and analyzing VGI across different platforms”. The workshop provided an opportunity for interested researchers to share ideas and findings on cross-platform data contributions. One portion of the workshop was dedicated to a hands-on session. In this session, the basics of spatial data access through selected Application Programming Interfaces (APIs) and the extraction of summary statistics of the results were illustrated. This paper presents the content of the hands-on session including the scripts and guidelines for extracting VGI data. Researchers, planners, and interested end-users can benefit from this paper for developing their own application for any region of the world.

ACS Style

Levente Juhász; Adam Rousell; Jamal Jokar Arsanjani. Technical Guidelines to Extract and Analyze VGI from Different Platforms. Data 2016, 1, 15 .

AMA Style

Levente Juhász, Adam Rousell, Jamal Jokar Arsanjani. Technical Guidelines to Extract and Analyze VGI from Different Platforms. Data. 2016; 1 (3):15.

Chicago/Turabian Style

Levente Juhász; Adam Rousell; Jamal Jokar Arsanjani. 2016. "Technical Guidelines to Extract and Analyze VGI from Different Platforms." Data 1, no. 3: 15.

Journal article
Published: 01 June 2016 in Journal of Environmental Geography
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In recent years, the increased frequency of inland excess water in the Carpathian Basin gets more and more attention. The authors developed a web based pilot application for disaster management, with special emphasis on inland excess water hazard management. Free and open source software was used to generate a model, and our work was based on Web GIS standards (OGC), which makes further development possible. The developed Web GIS application provides functions to support the data collection regarding channels and ditches, and on-line hydrological analysis based on OGC Web Processing Services (WPS). Hydrological analysis aims to visualize the areas potentially at risk, depending on different precipitation quantities and various values of influencing factors. In order to run the prototype a sample data set was gathered including reference maps, technical parameters and current condition of canals and ditches. The methodology of crowdsourcing can produce valuable Volunteered Geographic Information (VGI) that can fulfill the data requirements of disaster management applications. The prototype supports Crowdsourcing in the following aspects: free user access to the system’s analysis functionality, stakeholders may digitize the position of ditches, modify the status of the existing ditch system according to current conditions and add or modify parameters relevant for the analysis. The application demonstrated the usability of stakeholder generated geographic information and web processing for disaster management. The idea of integrating user-generated data into the various tasks of a disaster management agency is promising. However, maintaining data quality and standards compliance remain important issues.

ACS Style

Levente Juhász; Ádám Podolcsák; János Doleschall. Open Source Web GIS Solutions in Disaster Management – with Special Emphasis on Inland Excess Water Modeling. Journal of Environmental Geography 2016, 9, 15 -21.

AMA Style

Levente Juhász, Ádám Podolcsák, János Doleschall. Open Source Web GIS Solutions in Disaster Management – with Special Emphasis on Inland Excess Water Modeling. Journal of Environmental Geography. 2016; 9 (1-2):15-21.

Chicago/Turabian Style

Levente Juhász; Ádám Podolcsák; János Doleschall. 2016. "Open Source Web GIS Solutions in Disaster Management – with Special Emphasis on Inland Excess Water Modeling." Journal of Environmental Geography 9, no. 1-2: 15-21.

Conference paper
Published: 15 May 2016 in Geospatial Data in a Changing World
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Mapillary is a VGI platform which allows users to contribute crowdsourced street level photographs from all over the world. Due to unique information that can be extracted from street level photographs but not from aerial or satellite imagery, such as the content of road signs, users of other VGI Web 2.0 applications start to utilize Mapillary for collecting and editing data. This study assesses to which extent OpenStreetMap (OSM) feature edits use Mapillary data, based on tag information of added or edited features and changesets. It analyzes how spatial contribution patterns of individual users vary between OSM and Mapillary. A better understanding of cross-linkage patterns between different VGI platforms is important for data quality assessment, since cross-linkage can lead to better quality control of involved data sources.

ACS Style

Levente Juhász; Hartwig H. Hochmair. Cross-Linkage Between Mapillary Street Level Photos and OSM Edits. Geospatial Data in a Changing World 2016, 141 -156.

AMA Style

Levente Juhász, Hartwig H. Hochmair. Cross-Linkage Between Mapillary Street Level Photos and OSM Edits. Geospatial Data in a Changing World. 2016; ():141-156.

Chicago/Turabian Style

Levente Juhász; Hartwig H. Hochmair. 2016. "Cross-Linkage Between Mapillary Street Level Photos and OSM Edits." Geospatial Data in a Changing World , no. : 141-156.

Journal article
Published: 25 January 2016 in Transactions in GIS
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Mapillary is a Web 2.0 application which allows users to contribute crowdsourced street level photographs from all over the world. In the first part of the analysis this article reviews Mapillary data growth for continents and countries as well as the contribution behavior of individual mappers, such as the number of days of active mapping. In the second part of the analysis the study assesses Mapillary data completeness relative to a reference road network dataset at the country level. In addition, a more detailed completeness analysis is conducted for selected urban and rural areas in the US and part of northern Europe for which the completeness of Mapillary data will also be compared with that of Google Street View. Results show that Street View provides generally a better coverage on almost all road categories with some exceptions for pedestrian and cycle paths in selected cities. However, Mapillary data can be conveniently collected from any mobile device that is equipped with a photo camera. This gives Mapillary the potential to reach better coverage along off-road segments than Google Street View.

ACS Style

Levente Juhász; Hartwig H. Hochmair. User Contribution Patterns and Completeness Evaluation of Mapillary, a Crowdsourced Street Level Photo Service. Transactions in GIS 2016, 20, 925 -947.

AMA Style

Levente Juhász, Hartwig H. Hochmair. User Contribution Patterns and Completeness Evaluation of Mapillary, a Crowdsourced Street Level Photo Service. Transactions in GIS. 2016; 20 (6):925-947.

Chicago/Turabian Style

Levente Juhász; Hartwig H. Hochmair. 2016. "User Contribution Patterns and Completeness Evaluation of Mapillary, a Crowdsourced Street Level Photo Service." Transactions in GIS 20, no. 6: 925-947.

Journal article
Published: 01 January 2016 in GI_Forum
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Twitter and Instagram are social networking services that allow users to share images. To some extent, both platforms provide means for the user to annotate images with geographic location information. Using a selection of images shared through these two platforms, this study compares the photographers position, which is manually estimated from the scene in the image, with the annotated location information associated with the image and the position of the object being photographed. This approach provides an initial insight into the Twitter users movement between the location where a picture is taken and the place from where it is uploaded to Twitter. Furthermore, the distance between the photographers position and the location of the object shown in a Twitter or Instagram photograph can be used to assess the visual prominence of a photographed urban object in relation to its surroundings. Finally, the dataset generated in the research allows us to assess the positional accuracy of location labels in Instagram through comparison of the label position and the true position of the referenced object. For each of the different analyses, this paper discusses sources that could potentially lead to positional errors of images in Twitter and Instagram, and provides a comprehensive set of illustrative examples from different cities.

ACS Style

Sreten Cvetojevic; Levente Juhasz; Hartwig Hochmair. Positional Accuracy of Twitter and Instagram Images in Urban Environments. GI_Forum 2016, 4, 191 -203.

AMA Style

Sreten Cvetojevic, Levente Juhasz, Hartwig Hochmair. Positional Accuracy of Twitter and Instagram Images in Urban Environments. GI_Forum. 2016; 4 (1):191-203.

Chicago/Turabian Style

Sreten Cvetojevic; Levente Juhasz; Hartwig Hochmair. 2016. "Positional Accuracy of Twitter and Instagram Images in Urban Environments." GI_Forum 4, no. 1: 191-203.

Journal article
Published: 01 January 2015 in GI_Forum
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ACS Style

Levente Juhász; Hartwig Hochmair. Exploratory Completeness Analysis of Mapillary for Selected Cities in Germany and Austria. GI_Forum 2015, 1, 535 -545.

AMA Style

Levente Juhász, Hartwig Hochmair. Exploratory Completeness Analysis of Mapillary for Selected Cities in Germany and Austria. GI_Forum. 2015; 1 ():535-545.

Chicago/Turabian Style

Levente Juhász; Hartwig Hochmair. 2015. "Exploratory Completeness Analysis of Mapillary for Selected Cities in Germany and Austria." GI_Forum 1, no. : 535-545.