Estimating the global distribution of field size using crowdsourcing

Abstract

There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.

Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

“…The first six campaigns are documented in See et al (2015a) and resulted in the visual interpretation of around 250 K locations around the world . The campaigns were used to validate a map of the land availability for biofuels (Fritz et al 2013), to develop maps of cropland and agricultural field size (Fritz et al 2015;Lesiv et al 2018a), wilderness ) and global land cover ). The campaigns employed gamification and incentives such as Amazon vouchers and coauthorship.…”

“…Geo-Wiki campaigns have made extensive use of an expert or control data set, i.e., a subset of the sample that is classified by a group of experts; this control data set is then used to calculate the performance of the crowd Fritz et al 2017;Laso Bayas et al 2017a;Lesiv et al 2018a). The information about performance can then be used to weight the data when used in subsequent applications, e.g., giving less weight to those interpreters who performed less well (e.g., Lesiv et al 2018a). The control data set has also been used during campaigns to calculate a score based on both quality and quantity of interpretations, which was then used to determine the award of prizes or other incentives such as coauthorship.…”

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

“…Partially to address this need, several recent efforts have been devoted to producing extremely large training datasets that can be used across a wide range of mapping applications, and to serve as comprehensive benchmarks [72,73]. Similarly, a recent trend has emerged in large-scale mapping projects to employ large teams of TD interpreters, often within citizen science campaigns that rely on web-based data creation tools [22,[74][75][76].…”

“…Additionally, the increasingly popular large-scale, high-complexity NNs require substantially more TD than traditional statistical models, and like many ML approaches are sensitive to noisy and biased data, producing the logistical difficulty of creating very large, "clean" training datasets [69][70][71].Partially to address this need, several recent efforts have been devoted to producing extremely large training datasets that can be used across a wide range of mapping applications, and to serve as comprehensive benchmarks [72,73]. Similarly, a recent trend has emerged in large-scale mapping projects to employ large teams of TD interpreters, often within citizen science campaigns that rely on web-based data creation tools [22,[74][75][76].…”

Extracting field boundaries from satellite imagery with a convolutional neural network to enable smart farming at scale

“…Despite the availability of edge-based and region-based methods, there seems to have been a low uptake of these methods by the user community, suggesting a lack of fitness for purpose. For instance, the only global map of field size was obtained from crowdsourced, manually-digitised polygons (Lesiv et al, 2019).…”