Mapping cherry blossoms from geotagged street-level photos

Abstract

Floral phenology is useful information as an indicator on climate change and ecosystem services, however its observation is not straightforward over space and time. Satellite remote sensing and official and volunteer-based in-situ observations have been conducting, but the long-term and accurate data collection is challenging due to the insufficient quality and quantity of observations and the lack of financial and human resources to sustain. Here, we demonstrate a flower detection model from street-level photos, which can be the core function of a semi-automatic observation system to tackle those issues above. We detected cherry blossoms by this model from geotagged images with the observation date, obtained from Mapillary, which is one of social sensing data sources, and mapped dates of flowering in a study site, Aizuwakamatsu, Japan in April 2018. This approach enables us to collect floral phenology information semi-automatically as a data-driven approach. It is expected to collect a large number of observations with a certain level of quality by avoiding human-induced biases for the observations.