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
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 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.
