122 research outputs found

    Engaging Citizens in Environmental Monitoring via Gaming

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    Citizen science is quickly becoming one of the most effective tools for the rapid and low-cost collection of environmental information, filling a long recognized gap in in-situ data. Incentivizing citizens to participate, however, remains a challenge, with gaming being widely recognized as an effective solution to overcome the participation barrier. Building upon well-known gaming mechanics, games provide the user with a competitive and fun environment. This paper presents three different applications that employ game mechanics and have generated useful information for environmental science. Furthermore, it describes the lessons learnt from this process to guide future efforts

    Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology

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    Citizens are increasingly becoming involved in data collection, whether for scientific purposes, to carry out micro-tasks, or as part of a gamified, competitive application. In some cases, volunteered data collection overlaps with that of mapping agencies, e.g., the citizen-based mapping of features in OpenStreetMap. LUCAS (Land Use Cover Area frame Sample) is one source of authoritative in-situ data that are collected every three years across EU member countries by trained personnel at a considerable cost to taxpayers. This paper presents a mobile application called FotoQuest Austria, which involves citizens in the crowdsourcing of in-situ land cover and land use data, including at locations of LUCAS sample points in Austria. The results from a campaign run during the summer of 2015 suggest that land cover and land use can be crowdsourced using a simple protocol based on LUCAS. This has implications for remote sensing as this data stream represents a new source of potentially valuable information for the training and validation of land cover maps as well as for area estimation purposes. Although the most detailed and challenging classes were more difficult for untrained citizens to recognize, the agreement between the crowdsourced data and the LUCAS data for basic high level land cover and land use classes in homogeneous areas (ca. 80%) shows clear potential. Recommendations for how to further improve the quality of the crowdsourced data in the context of LUCAS are provided so that this source of data might one day be accurate enough for land cover mapping purposes

    The Picture Pile Tool for Rapid Image Assessment: A Demonstration using Hurricane Matthew

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    In 2016, Hurricane Matthew devastated many parts of the Caribbean, in particular the country of Haiti. More than 500 people died and the damage was estimated at 1.9billionUSD. At the time, the Humanitarian OpenStreetMap Team (HOT) activated their network of volunteers to create base maps of areas affected by the hurricane, in particular coastal communities in the path of the storm. To help improve HOT’s information workflow for disaster response, one strand of the Crowd4Sat project, which was funded by the European Space Agency, focussed on examining where the Picture Pile Tool, an application for rapid image interpretation and classification, could potentially contribute. Satellite images obtained from the time that Hurricane Matthew occurred were used to simulate a situation post-event, where the aim was to demonstrate how Picture Pile could be used to create a map of building damage. The aim of this paper is to present the Picture Pile tool and show the results from this simulation, which produced a crowdsourced map of damaged buildings for a selected area of Haiti in 1 week (but with increased confidence in the results over a 3 week period). A quality assessment of the results showed that the volunteers agreed with experts and the majority of individual classifications around 92% of the time, indicating that the crowd performed well in this task. The next stage will involve optimizing the workflow for the use of Picture Pile in future natural disaster situations

    The Journal of BSN Honors Research, Volume 5, Issue 1, Summer 2012

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    University of Kansas School of Nursing. Bachelor of Science in Nursing Honors ProgramExploration Of Health Care Needs Among Sudanese Refugee Women - Albin, J M, Domian, E. Is There An App For That? Developing An Evaluation Rubric For Apps For Use With Adults With Special Needs - Buckler, T, Peterson, M. The Relationship Between Nursing Characteristics And Pain Care Quality - Davis, E, Dunton, N. The Relationship Between Sleep And Night Eating On Weight Loss In Individuals With Severe Mental Illness - Huynh, Thu Nhi, Hamera, E. Examining Nurse Leader/Manager-Physician Communication Strategies: A Pilot Study - Jantzen, M, Ford, D J. Comparison Of Personal, Health And Family Characteristic Of Children With And Without Autism - Martin, A, Bott, M J. Association Between Obstructive Sleep Apnea And Postoperative Adverse Events - Nielsenshultz, Y, Smith, C, Bott, M, Schultz, M P, Cole, C. Challenges Associated With Partnering With Sudanese Refugee Women In Addressing Their Health Issues - Pauls, K L, Baird, M B. Complementary Therapy To Relieve Pediatric Cancer Therapy-Related Symptoms In The Usa - Slaven, A, Williams, P D

    Assessing the quality of crowdsourced in-situ land-use and land cover data from FotoQuest Austria application

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    With the proliferation of mobile phones and the rise of citizen science, the question of whether citizens can be used to complement traditional land surveys, e.g. the Land Use / Cover Area frame statistical Survey (LUCAS), needs further consideration. LUCAS is the European reference dataset for land use and land cover statistics. It is produced every three years using paid surveyors to collect information on land cover and land use at more than 270,000 point locations across all EU states. LUCAS has very strict protocols on data collection and a two-step system to ensure the quality of the data collected. To complement LUCAS, IIASA has developed the FotoQuest Austria (http://fotoquest.at/) app, which aims to engage citizens in exploring Austrian landscapes, geo-tagging land use and land cover across the country using a simplified version of the LUCAS protocol. The app shows the location of nearby points, and once at the location, volunteers take pictures in four cardinal directions and at the point location, recording the type of land use and land cover from a list of options in the app. Implementation of the simplified protocol uses the mobile technology to record the location, the angles of inclination of the phone when taking the pictures, the compass directions and the precision of the GPS to restrict when users can take photographs. These measures were employed to ensure high quality data collection. FotoQuest Austria has been running since the summer of 2015 with more than 2500 points on the ground and more than 12500 pictures collected by volunteers. Advantages of such an approach include the collection of a denser sample and a more frequent revisit time than the 3 year update cycle of LUCAS, which may then be used to detect ongoing change. Additionally, the involvement of citizens in getting to know their surrounding landscapes is a very valuable process and can be a positive vehicle for raising awareness of possible environmental conflicts and issues. This paper compares the results from this ongoing campaign with data from LUCAS. The presentation also outlines the lessons learned and highlights the minimum requirements needed to collect high quality data from volunteers. Recommendations for use of the app to complement LUCAS surveying and its application to other domains will also be discussed

    Lessons learned in developing reference data sets with the contribution of citizens: the Geo-Wiki experience

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    The development of remotely sensed products such as land cover requires large amounts of high-quality reference data, needed to train remote sensing classification algorithms and for validation. However, due to the lack of sharing and the high costs associated with data collection, particularly ground-based information, the amount of reference data available has not kept up with the vast increase in the availability of satellite imagery, e.g. from Landsat, Sentinel and Planet satellites. To fill this gap, the Geo-Wiki platform for the crowdsourcing of reference data was developed, involving visual interpretation of satellite and aerial imagery. Here we provide an overview of the crowdsourcing campaigns that have been run using Geo-Wiki over the last decade, including the amount of data collected, the research questions driving the campaigns and the outputs produced such as new data layers (e.g. a global map of forest management), new global estimates of areas or percentages of land cover/land use (e.g. the amount of extra land available for biofuels) and reference data sets, all openly shared. We demonstrate that the amount of data collected and the scientific advances in the field of land cover and land use would not have been possible without the participation of citizens. A relatively conservative estimate reveals that citizens have contributed more than 5.3 years of the data collection efforts of one person over short, intensive campaigns run over the last decade. We also provide key observations and lessons learned from these campaigns including the need for quality assurance mechanisms linked to incentives to participate, good communication, training and feedback, and appreciating the ingenuity of the participants

    Crowd-driven tools for the calibration and validation of Earth Observation products

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    In recent years there has been a rapid diffusion in open access Earth Observation (EO) data available at global scales to help scientists address planetary challenges including climate change, food security and disaster management. For example, since 2016 the European Space Agency (ESA), via its Sentinel-2 satellites, has been providing frequent (5 day repeat cycle) and fine-grained (10 meter resolution) optical imagery for open and public use. As such, the EO community is faced with the need to design methods for transforming this abundance of EO data into well-validated environmental monitoring products. To help facilitate the training and validation of these products (i.e. land cover, land use), several crowd-driven tools that engage stakeholders (within and outside the scientific community) in various tasks, including satellite image interpretation, and online interactive mapping, have been developed. This paper will highlight the new results and potential of a series of such tools developed at the International Institute for Applied Systems Analysis (IIASA), namely the Geo-Wiki engagement platform, the LACO-Wiki validation tool, and Picture Pile, a mobile application for rapid image assessment and change detection. Through various thematic data collection campaigns, these tools have helped to collect citizen-observed information to improve global maps of cropland and agricultural field size, to validate various land cover products and to create post natural disaster damage assessment maps. Furthermore, Picture Pile is designed as a generic and flexible tool that is customizable to many different domains and research avenues that require interpreted satellite images as a data resource. Such tools, in combination with the recent emergence of Citizen Observatories (i.e. LandSense, GROW, GroundTruth 2.0, SCENT funded by Horizon2020), present clear opportunities to integrate citizen-driven observations with established authoritative data sources to further extend GEOSS and Copernicus capacities, and support comprehensive environmental monitoring systems. In addition, these applications have considerable potential in lowering expenditure costs on in-situ data collection and current calibration/validation approaches within the processing chain of environmental monitoring activities both within and beyond Europe
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