The practice of producing drone videos for hobby or commercial purposes has already created a vast amount of open and free video datasets. When these videos are properly authored, time-stamped and geo-referenced, they receive characteristics of volunteered geographic information (VGI). As alternative forms to user-generated content (UGC), these visually appealing footages attract significant attention, but their production faces different practical and motivational issues that could impose limitation on the value of this kind of VGI. In order to better understand volunteered geographic drone videos (VGDV) from the social media and VGI perspective we conceptualize and discuss prospects and problems that could be explored in further research. This paper contributes to the development of theory about aerial drone videos, exploration of aerial drone video UGC characteristics and to the applicability of drone videos in Digital Earth systems.
Nowadays, drones have become one of the most attractive technologies with unprecedented growth in sales, pushing forward legislation and constantly creating new uses and supporting technologies [1, 2, 3]. Drone is an unofficial term that describes a pilotless aerial vehicle in a form of small helicopter or airplane equipped with camera or other sensors (providing live streaming or recording capabilities); it is operated from the ground by flight controller/smartphone or in some cases it can be preprogrammed to fly autonomously .
The creation of hobby or commercial videos by means of drones is one of the rising fields in drone application. In this respect, this new type of aerial video production has allowed general public and professionals to produce more visually appealing footage when compared to traditional terrestrial filming perspectives. The variety of use scenarios and the frequency of the usage have already contributed to the creation of vast number of videos from all over the globe. Internet community is getting increasingly involved by means of utilization of aerial drones images and videos through platforms, such as Flickr, Instagram or Travel With Drone . The initiatives for drone registration and licensing  will eventually contribute to creation of databases that can become a valuable source providing deeper insight into drone users’ characteristics and usage patterns.
Social media are common platforms for sharing aerial drone videos, together with other types of user-generated content (UGC), such as pictures, audio files or textual posts. Going further, if drone videos shared through social media are authored, time-stamped and geo-referenced or geo-tagged , they will receive characteristics of volunteered geographic information (VGI)  or, more specifically, volunteered geographic video (VGV) [9, 10]. While there is a growing number of literature that deals with extraction of information from general social media videos and shared aerial drone videos for purpose of further analysis and research [7, 11], there is less information on specific characteristics of aerial drone videos with attached spatial information that are principally shared on social media sites. This interesting field of research is still in its infancy as only a handful of studies have examined this type of drone products. For example, a rare study of Hochmair and Zielstra  investigated general patterns of contribution of drone pictures by users on the specialized photo sharing portal for aerial pictures viewed from a drone. Thus, the first motivation of this paper is to explore the basic characteristics of aerial drone videos from the perspective of UGC.
In general, VGI obtained from social media platforms, or social media geographic information (SMGI)  holds high potential for completing Digital Earth (DE) [14, 15]. SMGI is relevant for raising location awareness and for further analysis of spatial and temporal dynamics, but from a new and unprecedented perspective [7, 11, 16, 17]. Unlike other video media types, the making of drone videos is subject to different regulations relating to drone applicability, operational restrictions, administrative actions, technical claims, implementation of ethical constraints and human resource requirements , as wellas different users’ interests and socio-economic characteristics , that can limit accessibility of an area and reduce its value as a form of VGI [20, 21]. On the other hand, one of the main objectives of DE is to achieve ubiquitous coverage, image continuity, and seamlessness of data integration , so that users can access virtually any part of the planet. Therefore, the second motivation of this paper is to explore, based on spatial distribution of shared aerial drone videos, their applicability in completing DE systems.
In line with the above-mentioned research, this paper aims to start filling a gap in contemporary knowledge by conceptualizing problems and prospects of shared and geo-referenced aerial drone videos, which we call volunteered geographic drone videos (VGDV). The analysis is based on the literature review and the exploratory data on shared drone videos presented in seminal workof Stankov and colleagues  for the territory of the UK.
In this article we foreground drone video theory development, exploration of current drone video characteristics, and future perspectives of their application as a form of VGI. The main premise is that VGDV, as a relatively new form of VGI, can have a growing importance for DE applications.
One of the main conveniences of using drones for academic research is the possibility of acquiring high resolution recordings, using various types of lenses, at relatively low financial and time costs. These high definition images, videos or other sensor measurements are valuable sources of data for further analyses. This advantage is coupled with the advantages deriving from flexible drone flying capabilities and from possibility of installment of different instruments that provide innovative applications. Researchers have recognized the importance of drones for various academic fields and this interest is mirrored in the constant increase of published drone-related research. The number of papers related to drones increased from over 500 in 2013 to almost 1600 in 2017 .
Within the academic research, engineering field is the most prevalent and it is followed by engineering-oriented research areas (computer science, robotics, and automation and control systems) . However, in the recent couple of years there is an increase in application-oriented drone research, especially within the remote sensing and imaging science, measurement and instrumentation science, geosciences, telecommunication, chemistry and electrochemistry, environmental sciences, ecology and wildlife, agriculture and food service . For example, current literature extensively explores the use of drone monitoring in environmental research . In particular, in case of natural hazards for instance, such as forest fires , floods  or landslides , drone images or videos can be used for real-time monitoring, post-disaster damage evaluation, aerial assessment, etc . Furthermore, targeted monitoring of vegetation changes , costal management  or animal management , are just among many examples of the same idea. Other fields that show an increase in drone-related research include meteorology and atmospheric science, operation research and management science,water resources, transportation, optics, energy and fuels, forestry, plant sciences, etc .
Besides the academic field, in the market field, three major drone market sectors can be distinguished: military (70% of sales), consumer (17% of global sales) and commercial/civil (13%) . While the military sector occupies the largest portion of the market, commercial/civil sector is expected to lead a future sales growth . Commercial drone market segment is in essence a mixture of hardware, software and services. While hardware will become more commoditized, the future value will be driven from services in a wide range of applications, such as terrain modelling, delivery, inspections, data transmission or video collection . North America and Asia are currently the largest regional drone markets. Globally, by 2024 it is expected that global drone market will reach 43 billion dollars, from its current 14 billion in 2018 . Interestingly, there are even proposition of model to evaluate the entire country’s’ ecosystem readiness for drone project employment based on factors ranging from the regulatory sphere to the economic and social impacts of drones .
The advent of ubiquitous Web set the ground for emergence of VGI as the consequent phenomenon of citizen science and the role of the amateurs in geographic observation . VGI are usually accessed via geo-collaboration and community mapping sites. Open Street Map and WikiMapia are good examples of websites that provide informal sources of data and local knowledge about the geography of a place .
As the most important enabling technologies for VGI, Goodchild  lists Web 2.0, geo-referencing, geo-tags, GPS, high-quality graphics, and broadband communication. Multifunctional characteristics of drones and their products benefit from the use of almost all the listed technologies. For example, drone waypoint GPS navigation flight plans can be used to produce high-quality maps, images or videos, some of which can be shared on the Web, while drone communications systems can be used to beam or relay first person view (FPV) video.
The value of VGI is often evaluated also by its capability to complement existing spatial data infrastructures [38, 39]. Despite the high resolution of images taken from high altitudes , sometimes a real understanding of Earth’s elements and processes is not completely possible without eyewitnesses on the ground. With the help of drones, on-ground eyewitnesses are empowered to access three-dimensional space with new perspective,mobility, and speed . Anyone can access relevant and high-quality video data available in more frequent intervals or, in some cases, in real time.
Drones can also complement ‘humans as sensors’ concept. Sensors attached to drones can be used in conjunction with human observation to adapt or direct process accordingly, whether in real-time or retrospectively. These practices can be used for hobby or leisure purposes, scientific purposes or even for early warning cases .
The use of drones has introduced a number of changes in video production. Most importantly, drones made aerial filming perspective accessible to the general public. Drone videos allow people to look at a surface from a point of view that they have not done before, making them pay attention. For example, a perspective that is obtained when camera is pointing directly down (‘bird’s-eye’), creates unique shots that capture the attention of the viewer .
Drones allow access to formerly un-shootable locations. Earlier, it was quite ambitious or impractical for amateurs to capture outstanding footage of certain spaces, such as waterfalls, mountain cliffs or volcanoes. However, with the advent of drone use, access to these locations is made much easier .
The use of drones allows multiple data formats to be produced. Apart from taking videos, drone can produce photos, or videos can be enriched by means of sensing or recording other data with added sensors (for example: infrared, 3D, geo-referencing) [20, 45].
The rise of leisure drones democratized areal video production. Before the era of low-priced consumer drones, aerial video production was a costly endeavor that had to be heavily planned. It was usually a commercial activity that required the use of helicopters, or other means to achieve aerial perspective. Nowadays, drones equipped with professional-like capturing devices, are rather accessible to an average consumer . Creation of drone videos introduced even a new genre in aerial filming, both for professionals and amateurs. This opened the door for professional education in this young field of video production .
The appeal of air mobility simulation was exploited by drone videos as flight imagery could evoke the age-old dream of human flight . Today, as ever, the idea of personal vertical flight lives on in public imagination. While in real-life, this is still not a real option, drone video technology allows one to indulge realistically in the dream of flying . Drone videos capitalize on this air-mobility sub-context narratives.
An important problem for drone video production is the existence of current restrictions on filming. Unlike other video media type production, making of drone videos is confronted with peculiar regulative limitations (different constrains for flights over densely populated areas or larger groups of people, "no-flight" zones, ethics of use and privacy issues, etc.) and technical (flight conditions, flight time, maintaining visual contact with a flying drone) that could severely limit access to an area and reduce value of this kind of VGI.
In addition to spatial coverage supremacy of drone video over traditional terrestrial photography there is also the length of flying maneuvers. Notwithstanding the drone’s battery limitations, radio signal reach or obligations for direct, unaided, visual contact with drone, the average consumer drone can reach maximum range of 1000-1500 meters .
The seminal paper of Stankov and colleagues  examined 614 aerial drone videos from the United Kingdom shared on YouTube with added spatial references using TravelbyDrone.com. Firstly, the authors performed Youtube meta-data analysis to determine basic social media characteristics based on measurement metrics of general YouTube videos. Secondly, they performed an overly analysis of drone video locations and population density, land use and nationally designated protected areas in the UK.
Based on the social media characteristics (video titles, description, categories, duration and age of posting) Stankov and colleagues  pointed out the importance of drone video creation procedures for uploaders, suggesting a high ratio of professionally produced videos and, although a relative novelty on the general market, shooting with drones was practiced by the early adopters. An average video length in the sample was 3.45 min. Spatial analysis showed that mostly unpopulated areas that are located far from urbanized centers do not attract many videos. Drone video producers favor artificial surfaces or agricultural areas amongst which discontinuous urban fabric and pastures are most popular places for drone filming. This came as no surprise bearing in mind that the current regulations in the UK limit drone flights over densely populated areas or areas wherea large number of people gather. Moreover, almost 40% of videos were filmed in the protected areas that are mostly dedicated to recreation and that present fewer restrictions for drone usage .
It is important to note that, the above described characteristics of drone videos are not conclusive, but that they rather present an intention to spark a debate on the nature of this type of video content.
Based on the theoretical background, the following section will initiate a discussion to raise important theoretical and practical questions for the current accelerated era of use of drones in VGI creation.
The concept of UGC is frequently used in the marketing literature to describe the consumers’ contributions, such as blog posts, wikis, video comments and similar, that emerged as a form of socialization practices in the Web 2.0 era of Internet development . According to the Organisation for Economic Co-operation and Development (OECD), apart from being created by a user of a system, UGC has to be shared, that is, be publicly available. It should express at least a minimum amount of user’s creative effort and it is usually created outside of professional routines and practices .
Subsequent developments have brought sophisticated recording devices to consumers and, with the massive drone acceptance; consumers even took over the sky. Besides traditional aerial photography, an increasing number of people have a habit to record videos of their surroundings, especially during their vacations, and later share them on social media sites . Using those social media sites, people can now easily search drone videos posted by other individuals or companies for various usage purposes . For instance, Google’s US-based study showed that two out of three travelers watch online travel-related videos during information search for their trip .
Engaging people to create UGC, especially in more complex forms such as videos or music, has become an important issue in the era of omnipresent digital technology . Nevertheless, since the drone video production is more challenging compared to some other forms of video creation, there are additional restricting factors that come into play and could impact users’ participation in drone video creation and production. The general issue of owning and using camera-carrying drones still exists on the edge between hobbyists and mainstream practice, creating a lot of confusion about its general use  and possible future implications. Contrary to traditional VGI, where dissemination of geographic information is the final purpose of the production, most of the types of SMGI are produced implicitly with the help of map-based portals such as TravelwithDrone.com, where contributors upload (from YouTube, Vimeo or other similar social media video hosting services) previously filmed videos and attach a geospatial reference to it. Similar to portals for uploading geo-referenced pictures , these objects/surroundings can be spatially referenced by giving geographic coordinates and/or user-assigned geospatial descriptions of these videos in the form of textual labels. In many cases the intention of drone video producers who shared them on social media is not the diffusion of geographical contents [13, 55].
Insomuch as UGC is diverse in its forms and quantity, researchers applied variety of methods to analyze them accordingly: from the perspective of the crowd, end-users, designers or from multi-focal view [56, 57]. UGC content units consist of (1) core data, that is the content itself, such as text, audio files, pictures, videos or a combination, and of (2) metadata, that is, further information about the content, such as the date of publication or its author . When it comes to videos, its duration, description, time of posting to SNSs (social networking sites) or users’ engagements have become important variables to understand UGC both among the practitioners and academics [50, 52, 59]. The usefulness of UGC metadata has been proven in SNS content analysis, user motivation exploration, users’ engagement, or commercial applications, etc.
Given the scarcity of academic studies about the use of shared aerial drone videos, new studies should be based on the current variables for assessing social media videos and measurement of users’ engagement (e.g, title, description, length, age, views, likes, dislikes, total number of comments per video or the content of the comments) . As in the case of study of Stankov et al , the results of social media examination could show interesting findings from the perspective of possible separation of VGDV from other types of UGC and VGI and their applicability for completing DE. For example, YouTube has extensively been associated with short video lengths . ComScore  announced that the duration of the average online content video was 4.4 minutes. In case of YouTube, users can upload videos that are up to 15 minutes long, or in specific conditions they can upload longer ones. Looking from the point of view of drone video creation, if drone shooting session is limited to one flight, the total length of a video will be limited by drone flying time, flight condition or regulatory restrictions . From the point of view of UGC, it can be relevant for people’s attention when consuming digital media and from the point of view of VGI, video length can be also related to video production procedures and to the amount of content provided within a video .
More than one billion people a month visit YouTube to watch more than six billion hours of video . Despite the fact that the number of views is not always the adequate measure of online video performance, for benchmarking purposes it is reasonable to ascertain how well video content is doing against others in the same vertical . For example, according to Marshall , ‘People and Blogs’ and ‘Travel and Events’ get the lowest average amounts of views per video at 2,354, and 3,070 respectively. Because total number of views increases over time with various speed, the time of posting can be a significant indicator for some further analysis (older videos have more opportunity to be accessed, liked or commented) (Cheng, Liu, and Dale 2013). Thus, further research could test if average VGDVs are more relevant and if they could be separated and classified apart from other video content .
It would be interesting to find out if there is a more positive reception among viewers of VGDV based on the number of likes, dislikes or sentiment expressed in comments to videos? An online community made of amateurs, professionals and expert authorities  is forming around drone videos, similar to GoPro videos . It will not be not surprising to notice greater engagement in this community, as similar communities tend to comment on each other’s videos. This is also the reason why it is important to provide technical descriptions of the drone videos, as the results of Stankov et colleagues  analysis suggests. There is often professional element involved and it is important for establishing expertise or influence in the community .
The future studies that should be based on big data about drone videos could go deeper in exploring what components of aerial drone videos cause the highest reaction among viewers. For example, empirical research based on video content analysis is needed to test the effect of drone video on viewers. Moreover, from the point of view of effectiveness, the important question would be: Are there some other social media variables that influence VGDV more than the context of the video itself?
Aerial drone video sharing sites strive to provide seamless coverage of an area and they have several uses that actually reflect the most frequent purposes of VGDV, including, but not limiting to location monitoring, pedestrian navigation, event and human trajectory analysis, completion of institutional data sources  and further analysis in view of further extraction and mapping, landscape identification and classification [52, 53, 54, e.g]. They can also be used for entertainment, evaluation of aesthetic values, company’s promotion or tourism destination marketing promotion [70, 71, 72]. In essence, the main strength of VGVD in DE systems would be to support spatial presentation, recognition, and awareness by adding a new visual perspective.
Despite the fact that shared drone videos clearly pin point the area in which the drone was used for filming, VGDV are not primarily intended for this purpose . However, the existence of annotations and event descriptions in aerial drone video metadata can be important. This is important as, for example, 80% of YouTube searches for specific places focus on names and local features .
The results of Stankov et al  analysis suggest that VGVDs in general are not exclusively related to certain geographical feature but their location tends to be close to human settlements. Similarly, a simple overlay of drone registration location data in the USA, published by the Federal Aviation Administration, and the population distribution map revealed that people who buy and fly drones are not limited by some specific geographical environment, but they fall under fairly normal distribution of buyers’ behavior . As the demand for information on settlements is increasing, VGDV can be seen as a new paradigmfor understanding urban problems from the citizens’ perspective.
The traditional spatial analysis tools and descriptive statistics can be used to produce spatial distribution maps and to gain exploratory insight into drone video data. While these methods can be of use for initiating studies, more sophisticated measurements and advanced tools should be deployed to discover statistically significant underlying patterns of VGDV. For example, topic modeling and topic phrase mining of metadata and comments could potentially provide insight into spatial causalities.
Based on the presented characteristics of VGDV, it is evident that portals of DE can benefit from this trend of producing aerial videos by various drone users. Same as in the case of other social media types, where some online repositories took the role of content curator, VGDV are becoming more and more mainstream VGI. It must be noted that the advance of virtual reality could lead aerial drone videos to become a ‘common currency’. For example, Google Maps now allow touring Street View and virtual reality. On the other hand, the omnipresence of VR could induce expectations among general public for similar content to be provided in marketing or other types of communications.
Since DE concept can easily be understood by multiple audiences, from general public to decision-makers, and since it can also be fun and used to engage the younger generations , the attractiveness of VGDV becomes apparent. The ‘wow’ effect with the new visual perspective can also be achieved with various levels of video productions.
As previously stated, shared aerial drone videos that are later georeferenced, are not products of an unmanned aerial system (UAS) consisting of the drone and all equipment (e.g, camera, GPS and software), data links and ground control stations . Therefore, there are numerous challenges to use them with authoritative geographic information. In case of drone videos, common issues can also be: poor editing, ‘roller-coaster’ like or low quality footage, too long videos, videos overly promotional in nature, with animated intros or endings, military related footage, political, religious or other personal content, to mention a few . In general, information from social media comes with different problems attached and needs to be filtered to be of relevance for specific usage, efficient indexing, and later querying or establishing relationships between the video content and accurate locations [7, 76].
Geo-localization of videos is still a challenging effort as the majority of existing computer vision solutions are limited to highly-visited urban regions while large and ordinary geo-spatial regions are left behind . Therefore, the spatial coverage capability of user-shared video repositories is an important issue in spatial representation .
The established limitations of drone usage for specific locations or for certain events, could pose a serious challenge for accomplishment of seamless continuity in Earth representation to some extent. Spatial coverage can also be limited by terrain inaccessibility to launch drone in the first place. The concept of digital divide  is of relevance here. Apart from being less populated, and thus having potentially smaller numbers of available drones, remote areas can often be underdeveloped. That consequently results in lower purchasing power and lower technical education of the population and, in general, poorer technological infrastructure. Despite the apparent openness of VGI, it remains largely the privilege of those fortunate enough to have access to modern information and communication technologies. While a growing fraction of citizens in developed countries have such access, it is largely unavailable to the majority of the world’s population living in developing countries, which leads to uneven VGI practices [8, 78].
None of the DE systems can actually cover any given area to full extent due to privacy and security restrictions. The existing geofences that serve as an invisible buffer around objects or protected/sensitive areas and prevent drones from flying in , cover areas that are already out of the general interest of public. On the other hand, some congested areas that are of public interest, as well as areas with organized open-air assembly of people (such as public events), that are restricted to drone flights, can still be covered by professional drone filming .
Many of the drone videos that are professionally produced (some in the form of authoritative geographic information) are not considered a proper UGC by some theoretical approaches [34, e.g]. Yet, if they are open to public, regardless of the main motivation to showcase company’s or entrepreneur’s business activities, they add equally to the current VGDI datasets. Unlike other types of UGC (e.g pictures or terrestrial videos), the production of areal drone videos requires, from the start, extra conditions and efforts from the users, and therefore it is sometimes hard to distinguish professional from UGC . However, from the perspective of VGDV, this delineation is not essential as the drone video value will depend on individual video’s characteristics and the specific application .
Finally, in addition to limitations of VGDV, we can add a general constraint on drone usage, such as social issues concerning privacy, safety, data security and psychological wellbeing when it comes to reaching the proximity of human settlements and properties with drones . Again, there is a public discourse that earned drones a bad reputation and publicity and they are considered a disturbing technology [80, 81].
As aerial drone videos production is a relatively novel practice, this conceptual study is focused on discussing the phenomenon from UGC and VGI perspective and how it can contribute to conventional sources of DE systems. Based on the outlined research areas, more opportunities are offered in this era of proliferation of VGDI for understanding issues such are:what motivates people to do this, how it can be used in DE systems, how this data can be approached for further analysis, etc.
This research was supported by Project 176020 of the Serbian Ministry of Education, Science and Technological Development.
We confirm that all the authors made an equal contribution to the study’s development.
 Campos V.S., European Union Policies and Civil Drones. In: de-Miguel-Molina M., Campos V.S. (Eds.), Ethics and Civil Drones - European Policies and Proposals for the Industry. SpringerBriefs Law, Cham, 2018, 35–41 Search in Google Scholar
 Awange J., Unmanned Aircraft Vehicles. In: Awange J. (Ed.), GNSS Environmental Sensing, Revolutionizing Environmental Monitoring. Springer, Cham, 2018, 423–443 Search in Google Scholar
 M. McNabb, Drones in 2018: Thought Leaders Make Predictions, 2018 https://dronelife.com/2018/01/02/drones-2018-thought-leaders-predict-new-trends Search in Google Scholar
 Custers B., Drones Here, There and Everywhere Introduction and Overview. In: Custers B (Ed.), The Future of Drone Use Opportunities and Threats from Ethical and Legal Perspectives. T.M.C. Asser Press, The Hague, 2016, 3–20 Search in Google Scholar
 Birtchnell T., Drones in human geography. In:Warf B. (Ed.), Handbook on Geographies of Technology. Edward Elgar Publishing, Cheltenham, 2017, 231–241 Search in Google Scholar
 Fohringer J., Dransch D., Kreibich H., Schröter K., Social media as an information source for rapid flood inundation mapping. Nat. Hazards Earth Syst. Sci., 2015, 15 2725–2738, 10.5194/nhess-15-2725-2015 Search in Google Scholar
 Saunders M., Lewis P., Thornhill A., Research methods for business students. Pearson, Harlow, 2012 Search in Google Scholar
 Lewis Q.W., Park E., Volunteered Geographic Videos in Physical Geography: Data Mining from YouTube. Ann. Am. Assoc. Geogr., 2018, 108, 52–70, 10.1080/24694452.2017.1343658 Search in Google Scholar
 Le Coz J., Patalano A., Collins D., Guillén N.F., García C.M., Smart G.M. et al., Crowdsourced data for flood hydrology: Feedback from recent citizen science projects in Argentina, France and New Zealand. J. Hydrol., 2016, 541, 766–777, 10.1016/J.JHYDROL.2016.07.036 Search in Google Scholar
 Hochmair H.H., Zielstra D., Analysing user contribution patterns of drone pictures to the dronestagram photo sharing portal. J. Spat. Sci., 2015, 60, 79–98, 10.1080/14498596.2015.969340 Search in Google Scholar
 Campagna M., Floris R., Massa P., Girsheva A., Ivanov K., The Role of Social Media Geographic Information (SMGI) in Spatial Planning. In: Geertman S., Ferreira J.J, Goodspeed R., Stillwell J. (Eds.), Planning Support Systems and Smart Cities. Springer, Cham, 2015, 41–60 Search in Google Scholar
 Elwood S., Goodchild M.F., Sui D.Z., Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice., Ann. Assoc. Am. Geogr., 2012, 102, 571–590, 10.2307/23275544 Search in Google Scholar
 Kreps S.E., Drones: What everyone needs to know. Oxford University Press, New York, 2016 Search in Google Scholar
 Luppicini R., So A., A technoethical review of commercial drone use in the context of governance, ethics, and privacy. Technol. Soc., 2016, 46, 109–119, 10.1016/j.techsoc.2016.03.003 Search in Google Scholar
 Bartha G., Kocsis S., Standardization of geographic data: The European INSPIRE Directive. Eur. J. Geogr., 2011, 2, 79–89 Search in Google Scholar
 Stankov U., Kennell J., Morrison A.M., Vujičić M.D., The view from above: the relevance of shared aerial drone videos for destination marketing. J. Travel Tour. Mark., 2019, 1–15, 10.1080/10548408.2019.1575787 Search in Google Scholar
 Chabot D., Trends in drone research and applications as the Journal of Unmanned Vehicle Systems turns five. J. Unmanned Veh. Syst., 2018, 6(vi–xv), 10.1139/juvs-2018-0005 Search in Google Scholar
 Elliott K.C., Montgomery R., Resnik D.B., Goodwin R., Mudumba T., Booth J., et al., Drone Use for Environmental Research [Perspectives]. IEEE Geosci. Remote Sens. Mag., 2019, 7, 106–111, 10.1109/MGRS.2018.2876451 Search in Google Scholar
 Morar C., Lukić T., Basarin B., Vujičić M.D., Vasiljević Ð.A., Stankov U. et al., Factors triggering landslide occurrence in Oradea area - Ciuperca Hill case Study, Romania. In: Marković S.B., Pavkov-Hrvojević M., Lazić L. (Eds.), International Conference Natural Hazards. University of Novi Sad, Faculty of Sciences, Novi Sad, 2018, 49–50 Search in Google Scholar
 Estrada M.A.R., Ndoma A., The uses of unmanned aerial vehicles –UAV’s- (or drones) in social logistic: Natural disasters response and humanitarian relief aid. Procedia Comput. Sci., 2019, 149, 375–383, 10.1016/J.PROCS.2019.01.151 Search in Google Scholar
 Sankey T.T., Leonard J.M., Moore M.M., Unmanned Aerial Vehicle - Based Rangeland Monitoring: Examining a Century of Vegetation Changes. Rangel. Ecol. Manag., 2019, 10.1016/J.RAMA.2019.04.002 Search in Google Scholar
 Vayssade J.-A., Arquet R., Bonneau M., Automatic activity tracking of goats using drone camera. Comput. Electron. Agric., 2019, 162, 767–772. 10.1016/J.COMPAG.2019.05.021 Search in Google Scholar
 Goldman Sachs, Drones: Reporting for Work, 2016 http://www.goldmansachs.com/our-thinking/technology-driving-innovation/drones Search in Google Scholar
 Research and Markets, Commercial and Military Drone Market Assessment and Forecasts 2016 - 2025, 2016 https://www.researchandmarkets.com/reports/3804656/commercial-and-military-drone-market-assessment Search in Google Scholar
 F. Castellano, The Drone Market and Industry Trends, 2019 https://www.toptal.com/finance/market-research-analysts/drone-market Search in Google Scholar
 Research and Markets, The Drone Market Report 2019: Commercial Drone Market Size and Forecast (2019-2024), 2019 https://www.researchandmarkets.com/reports/4764173/the-drone-market-report-2019-commercial-drone?utm_source=BW&utm_medium=PressRelease&utm_code=gcn3vb&utm_campaign=1238097+-+Global+Drone+Market+Report+2019%3A+Commercial+%26+Private+Drone+Market+Size+and+F Search in Google Scholar
 Nzaramba S., Kabagamba R., Garba A., Chandler K., Drone readiness index. In: Proceedings of ITU Kaleidoscope 2017: Challenges for a data-driven society. IEEE, Nanjing, 2017, 1–8 Search in Google Scholar
 Agrios B., Mann K., Getting in Touch with Volunteered Geographic Information, Esri ArcUser, 2010 http://www.esri.com/news/arcuser/0610/files/vgi-tutorial.pdf Search in Google Scholar
 Bordogna G., Kliment T., Frigerio L., Brivio P., Crema A., Stroppiana D., M., et al., A Spatial Data Infrastructure Integrating Multi-source Heterogeneous Geospatial Data and Time Series: A Study Case in Agriculture. Int. J. Geo-Information., 2016, 5, 73, 10.3390/ijgi5050073 Search in Google Scholar
 Craglia M., Shanley L., Data democracy – increased supply of geospatial information and expanded participatory processes in the production of data. Int. J. Digit. Earth., 2015, 8, 679–693, 10.1080/17538947.2015.1008214 Search in Google Scholar
 Varga K., Szabó S., Szabó G., Dévai G., Tóthmérész B., Improved land cover mapping using aerial photographs and satellite images. Open Geosci., 2014, 7, 10.1515/geo-2015-0002 Search in Google Scholar
 Smith C., The Photographer’s Guide to Drones. Rocky Nook, San Rafael, 2016 Search in Google Scholar
 Innga Y., Drone Videos Lift up the Travel Industry’s Charm, 2016 http://avb.asia/drone-videos-lift-travel-industrys-charm Search in Google Scholar
 Johnson J., How drones are changing the landscape of travel video, 2016 https://matadoru.com/drones-changing-landscape-travel-video Search in Google Scholar
 What is the range of a drone?, FPV Frenzy, 2017 http://fpvfrenzy.com/what-is-the-range-of-a-drone/#ixzz4qP0KZrBm Search in Google Scholar
 Stankov U., Jovanović T., Pavluković V., Kalinić Č., Drakulić-Kovačević N. Cimbaljević, M., A regional survey of current practices on destination marketing organizations’ Facebook Pages: the case of EU and US. Geogr. Pannonica., 2018, 22, 81–96, 10.5937/22-16673 Search in Google Scholar
 Participative Web and User-Created Content - Web 2.0, Wikis and Social Networking, OECD Publishing, Paris, 2007 http://www.oecd.org/internet/ieconomy/38393115.pdf Search in Google Scholar
 Lim Y., Chung Y., Weaver P.A., The impact of social media on destination branding: Consumer-generated videos versus destination marketer-generated videos. J. Vacat. Mark., 2012, 18, 197–206, 10.1177/1356766712449366 Search in Google Scholar
 Crowel H., Gribben H., Loo J., Travel Content Takes Off on YouTube, 2014 https://www.thinkwithgoogle.com/consumer-insights/travel-content-takes-off-on-youtube Search in Google Scholar
 Daugherty T., Eastin M.S., Bright L., Exploring Consumer Motivations for Creating User-Generated Content., J. Interact. Advert., 2008, 8, 16–25, 10.1080/15252019.2008.10722139 Search in Google Scholar
 Cheng E., Aerial photography and videography using drones. Pearson Education, Peachpit., San Francisco, 2015 Search in Google Scholar
 Momeni E., Cardie C., Diakopoulos N., A Survey on Assessment and Ranking Methodologies for User-Generated Content on the Web. ACM Comput. Surv., 2015, 48, 1–49, 10.1145/2811282 Search in Google Scholar
 Lu W., Stepchenkova S., User-Generated Content as a Research Mode in Tourism and Hospitality Applications: Topics, Methods, and Software., J. Hosp. Mark. Manag., 2015, 24, 119–154, 10.1080/19368623.2014.907758 Search in Google Scholar
 Wyrwoll C., Social Media, Fundamentals, Models, and Ranking of User-Generated Content. Springer Vieweg, Wiesbaden, 2014 Search in Google Scholar
 Mariani M.M., Di Felice M., Mura M., Facebook as a destination marketing tool: Evidence from Italian regional Destination Management Organizations. Tour. Manag., 2016, 54, 321–343, 10.1016/j.tourman.2015.12.008 Search in Google Scholar
 Cheng X., Lai K., Wang D., Liu J., UGC Video Sharing: Measurement and Analysis. In: Chen C.W., Li Z., Lian S. (Eds.), ntelligent Multimedia Communication: Techniques and Applications. Springer, Berlin, Heidelberg, 2010, 367–402, 10.1007/978-3-642-11686-5_12 Search in Google Scholar
 ComScore Releases January 2014 U.S. Online Video Rankings, 2017 http://www.comscore.com/Insights/Press-Releases/2014/2/comScore-Releases-January-2014-US-Online-Video-Rankings Search in Google Scholar
 Cheng X., Liu J., Dale C., Understanding the Characteristics of Internet Short Video Sharing: A YouTube-Based Measurement Study. IEEE Trans. Multimed., 2013, 15, 1184–1194, 10.1109/TMM.2013.2265531 Search in Google Scholar
 Coleman D.J., Georgiadou Y., Labonte J., Volunteered Geographic Information: the nature and motivation of produsers. J. Spat. Data Infrastructures Res., 2009, 4, 332–358, 10.2902/1725-0463.2009.04.art16 Search in Google Scholar
 Dinhopl A., Gretzel U., GoPro panopticon: performing in the surveyed leisure experience. In: Carnicelli S., McGillivray D., McPherson G. (Eds.), Digital Leisure Cultures - Critical perspectives. Routledge, New York, 2016, 66–79 Search in Google Scholar
 Simensen T., Halvorsen R., Erikstad L., Methods for landscape characterisation and mapping: A systematic review. Land Use Policy., 2018, 75, 557–569, 10.1016/J.LANDUSEPOL.2018.04.022 Search in Google Scholar
 Oteros-Rozas E., Martín-López B., Fagerholm N., Bieling C., Plieninger T., Using social media photos to explore the relation between cultural ecosystem services and landscape features across five European sites. Ecol. Indic., 2018, 94, 74–86. 10.1016/J.ECOLIND.2017.02.009 Search in Google Scholar
 Johnson P., Ricker B., Harrison S., Volunteered Drone Imagery: Challenges and constraints to the development of an open shared image repository. In: Proceedings of the 50th Hawaii International Conference on System Sciences. Hawaii, 2017, 1995–2004 Search in Google Scholar
 Hay B., Drone tourism: A study of the current and potential use of drones in hospitality and tourism. In: Scerri M., Hui L.K. (Eds.), CAUTHE 2016: The Changing Landscape of Tourism and Hospitality: The Impact of Emerging Markets and Emerging Destinations. Blue Mountains International HotelManagement School, Sydney, 2016, 49–68 Search in Google Scholar
 Kim S.J., Jeong Y., Park S., Ryu K., Oh G., A Survey of Drone use for Entertainment and AVR (Augmented and Virtual Reality). In: Jung T., tom Dieck M.C (Eds.), Augmented Reality and Virtual Reality. Springer, Cham, 2018, 339–352 Search in Google Scholar
 Lieskovský J., Rusňák T., Klimantová A., Izsóff M., Gašparovičová P., Appreciation of landscape aesthetic values in Slovakia assessed by social media photographs. Open Geosci., 2017, 9, 593–599, 10.1515/geo-2017-0044 Search in Google Scholar
 Atherton K.D., Here’s The Map Of All of America’s Registered Drone Users, 2016 http://www.popsci.com/map-registered-drone-users-resembles-population-map-united-states Search in Google Scholar
 Craglia M., de Bie K., Jackson D., Pesaresi M., Remetey-Fülöpp G., Wang C., et al., Digital Earth 2020: towards the vision for the next decade. Int. J. Digit. Earth., 2012, 4–21, 10.1080/17538947.2011.638500 Search in Google Scholar
 Laurini R., Farah I.R., Toward Citizen-Edited Image-Populated Ontologies for Earth Observation—A Position Paper. In: Bordogna G., Carrara P. (Eds.), Mobile Information Systems Leveraging Volunteered Geographic Information for Earth Observation. Springer, Cham, 2018, 73–92. Search in Google Scholar
 Zamir A.R., Hakeem A., Van Gool L., Shah M., Szeliski R., Introduction to Large-Scale Visual Geo-localization. In: Zamir A.R., Hakeem A., Van Gool L., Shah M., Szeliski R. (Eds.), Large-Scale Visual Geo-Localization. Springer, Cham, 2016, 1–18 Search in Google Scholar
 Sui D., Goodchild M., Elwood S., Volunteered Geographic Information, the Exaflood, and the Growing Digital Divide. In: Sui D., Elwood S., Goodchild M. (Eds.), Crowdsourcing Geographic Knowledge. Springer, Dordrecht, 2013, 1–12 Search in Google Scholar
 Finn R., Donovan A., Big Data, Drone Data: Privacy and Ethical Impacts of the Intersection Between Big Data and Civil Drone Deployments. In: Custers B. (Ed.), The Future of Drone Use. T.M.C. Asser Press, The Hague, 2016, 47–67 Search in Google Scholar
 Mancosu M., 4 Ways Drones Are Changing The Marketing Industry, 2016 https://skytango.com/how-drones-are-changing-the-marketing-industry Search in Google Scholar
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