The spatial distribution of cropland is an important input to many applications including food security monitoring and economic land use modeling. Global land cover maps derived from remote sensing are one source of c...The spatial distribution of cropland is an important input to many applications including food security monitoring and economic land use modeling. Global land cover maps derived from remote sensing are one source of cropland but they are currently not accurate enough in the cropland domain to meet the needs of the user community. Moreover, when compared with one another, these land cover products show large areas of spatial disagreement, which makes the choice very difficult regarding which land cover product to use. This paper takes an entirely different approach to mapping cropland, using crowdsourcing of Google Earth imagery via tools in Geo-Wiki. Using sample data generated by a crowdsourcing campaign for the collection of the degree of cultivation and settlement in Ethiopia, a cropland map was created using simple inverse distance weighted interpolation. The map was validated using data from the GOFC-GOLD validation portal and an independent crowdsourced dataset from Geo-Wiki. The results show that the crowdsourced cropland map for Ethiopia has a higher overall accuracy than the individual global land cover products for this country. Such an approach has great potential for mapping cropland in other countries where such data do not currently exist. Not only is the approach inexpensive but the data can be collected over a very short period of time using an existing network of volunteers.展开更多
Raw observations(carrier-phase and code observations)from the Global Navigation Satellite System(GNSS)can now be accessed from Android mobile phones(Version 7.0 onwards).This paves the way for GNSS data to be utilized...Raw observations(carrier-phase and code observations)from the Global Navigation Satellite System(GNSS)can now be accessed from Android mobile phones(Version 7.0 onwards).This paves the way for GNSS data to be utilized for low-cost precise positioning or in ionospheric or tropospheric applications.This paper presents results from data collection campaigns using the CAMALIOT mobile app.In the frst campaign,116.3 billion measurements from 11,828 mobile devices were collected from all continents.Although participation decreased during the second campaign,data are still being collected globally.In this contribution,we demonstrate the potential of volunteered geographic information(VGl)from mobile phones to fill data gaps in geodetic station networks that collect GNSS data,e.g.in Brazil,but also how the data can provide a denser set of observations than current networks in countries across Europe.We also show that mobile phones capable of dual-frequency reception,which is an emerging technology that can provide a richer source of GNSS data,are contributing in a substantial way.Finally,we present the results from a survey of participants to indicate that participation is diverse in terms of backgrounds and geography,where the dominant motivation for participation is to contribute to scientific research.展开更多
Floods affect more people globally than any other type of natural hazard. Great potential exists for new technologies to support flood disaster risk reduction. In addition to existing expert-based data collection and ...Floods affect more people globally than any other type of natural hazard. Great potential exists for new technologies to support flood disaster risk reduction. In addition to existing expert-based data collection and analysis, direct input from communities and citizens across the globe may also be used to monitor, validate, and reduce flood risk. New technologies have already been proven to effectively aid in humanitarian response and recovery. However, while ex-ante technologies are increasingly utilized to collect information on exposure, efforts directed towards assessing and monitoring hazards and vulnerability remain limited. Hazard model validation and social vulnerability assessment deserve particular attention. New technologies offer great potential for engaging people and facilitating the coproduction of knowledge.展开更多
Volunteered geographic information(VGI)is the assembly of spatial information based on public input.While VGI has proliferated in recent years,assessing the quality of volunteer-contributed data has proven challenging...Volunteered geographic information(VGI)is the assembly of spatial information based on public input.While VGI has proliferated in recent years,assessing the quality of volunteer-contributed data has proven challenging,leading some to question the efficiency of such programs.In this paper,we compare several quality metrics for individual volunteers’contributions.The data were the product of the‘Cropland Capture’game,in which several thousand volunteers assessed 165,000 images for the presence of cropland over the course of 6 months.We compared agreement between volunteer ratings and an image’s majority classification with volunteer self-agreement on repeated images and expert evaluations.We also examined the impact of experience and learning on performance.Volunteer self-agreement was nearly always higher than agreement with majority classifications,and much greater than agreement with expert validations although these metrics were all positively correlated.Volunteer quality showed a broad trend toward improvement with experience,but the highest accuracies were achieved by a handful of moderately active contributors,not the most active volunteers.Our results emphasize the importance of a universal set of expert-validated tasks as a gold standard for evaluating VGI quality.展开更多
文摘The spatial distribution of cropland is an important input to many applications including food security monitoring and economic land use modeling. Global land cover maps derived from remote sensing are one source of cropland but they are currently not accurate enough in the cropland domain to meet the needs of the user community. Moreover, when compared with one another, these land cover products show large areas of spatial disagreement, which makes the choice very difficult regarding which land cover product to use. This paper takes an entirely different approach to mapping cropland, using crowdsourcing of Google Earth imagery via tools in Geo-Wiki. Using sample data generated by a crowdsourcing campaign for the collection of the degree of cultivation and settlement in Ethiopia, a cropland map was created using simple inverse distance weighted interpolation. The map was validated using data from the GOFC-GOLD validation portal and an independent crowdsourced dataset from Geo-Wiki. The results show that the crowdsourced cropland map for Ethiopia has a higher overall accuracy than the individual global land cover products for this country. Such an approach has great potential for mapping cropland in other countries where such data do not currently exist. Not only is the approach inexpensive but the data can be collected over a very short period of time using an existing network of volunteers.
基金supported by the European Space Agency’s Navigation Science Office through the NAVISP Element 1 Program in the CAMALIOT(Application of Machine Learning Technology for GNSS IoT Data Fusion)project(NAVISP-EL1-038.2).
文摘Raw observations(carrier-phase and code observations)from the Global Navigation Satellite System(GNSS)can now be accessed from Android mobile phones(Version 7.0 onwards).This paves the way for GNSS data to be utilized for low-cost precise positioning or in ionospheric or tropospheric applications.This paper presents results from data collection campaigns using the CAMALIOT mobile app.In the frst campaign,116.3 billion measurements from 11,828 mobile devices were collected from all continents.Although participation decreased during the second campaign,data are still being collected globally.In this contribution,we demonstrate the potential of volunteered geographic information(VGl)from mobile phones to fill data gaps in geodetic station networks that collect GNSS data,e.g.in Brazil,but also how the data can provide a denser set of observations than current networks in countries across Europe.We also show that mobile phones capable of dual-frequency reception,which is an emerging technology that can provide a richer source of GNSS data,are contributing in a substantial way.Finally,we present the results from a survey of participants to indicate that participation is diverse in terms of backgrounds and geography,where the dominant motivation for participation is to contribute to scientific research.
基金Funding from the global Zurich Flood Resilience Alliance
文摘Floods affect more people globally than any other type of natural hazard. Great potential exists for new technologies to support flood disaster risk reduction. In addition to existing expert-based data collection and analysis, direct input from communities and citizens across the globe may also be used to monitor, validate, and reduce flood risk. New technologies have already been proven to effectively aid in humanitarian response and recovery. However, while ex-ante technologies are increasingly utilized to collect information on exposure, efforts directed towards assessing and monitoring hazards and vulnerability remain limited. Hazard model validation and social vulnerability assessment deserve particular attention. New technologies offer great potential for engaging people and facilitating the coproduction of knowledge.
基金supported by the European Research Council[grants 617754 and 603719]a IIASA postdoctoral fellowship to Carl Salk.
文摘Volunteered geographic information(VGI)is the assembly of spatial information based on public input.While VGI has proliferated in recent years,assessing the quality of volunteer-contributed data has proven challenging,leading some to question the efficiency of such programs.In this paper,we compare several quality metrics for individual volunteers’contributions.The data were the product of the‘Cropland Capture’game,in which several thousand volunteers assessed 165,000 images for the presence of cropland over the course of 6 months.We compared agreement between volunteer ratings and an image’s majority classification with volunteer self-agreement on repeated images and expert evaluations.We also examined the impact of experience and learning on performance.Volunteer self-agreement was nearly always higher than agreement with majority classifications,and much greater than agreement with expert validations although these metrics were all positively correlated.Volunteer quality showed a broad trend toward improvement with experience,but the highest accuracies were achieved by a handful of moderately active contributors,not the most active volunteers.Our results emphasize the importance of a universal set of expert-validated tasks as a gold standard for evaluating VGI quality.
