Optimizing Crowdsourced Land Use and Land Cover Data Collection: A Two-Stage Approach

Abstract

Citizen science has become an increasingly popular approach to scientific data collection, where classification tasks involving visual interpretation of images is one prominent area of application, e.g., to support the production of land cover and land-use maps. Achieving a minimum accuracy in these classification tasks at a minimum cost is the subject of this study. A Bayesian approach provides an intuitive and reasonably straightforward solution to achieve this objective. However, its application requires additional information, such as the relative frequency of the classes and the accuracy of each user. While the former is often available, the latter requires additional data collection. In this paper, we present a two-stage approach to gathering this additional information. We demonstrate its application using a hypothetical two-class example and then apply it to an actual crowdsourced dataset with five classes, which was taken from a previous Geo-Wiki crowdsourcing campaign on identifying the size of agricultural fields from very high-resolution satellite imagery. We also attach the R code for the implementation of the newly presented approach.

A comparative study on LinkedIn and Sina Weibo users’ perceptions of the carbon-neutral city

“…Others might shed light on social media such as Facebook, Instagram or Twitter. Crowdsourcing that outsources tasks to the crowd in analysing and interpreting the data may contribute to citizen science (Moltchanova et al, 2022). Besides, studying traditional media with a strong focus on climate and carbon neutrality could be the next step.…”

Integration of Sentinel-1 and Sentinel-2 Data for Ground Truth Sample Migration for Multi-Temporal Land Cover Mapping

“…For instance, the FAST dataset, which was primarily gathered in 2015, might not be useful for LC mapping in the years after or before 2015, given the changes in landscapes and LCs due to natural and human-oriented interventions [15]. Furthermore, Geo-Wiki is assembled based on visual interpretations by different experts, and therefore might be unsuitable to be scaled down to the regional or local scale or in different time intervals [24][25][26]. These reference samples were mainly prepared based on the visual interpretation of the interpreter and images with high spatial resolution (for example, Google Earth images).…”