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.

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The importance of floral phenology as a critical indicator of regional climate change and ecosystem services is widely recognized. The annual blooming of cherry blossoms is a nationally celebrated event in Japan, and historical phenological records have been used to document regional climate change. The cultural ecosystem services provided by this phenomenon are important as they not only signal the arrival of spring but also offer a picturesque spring landscape. Despite its importance, constructing a spatiotemporal record of cherry blossom blooming is challenging due to the limited coverage of traditional stationary observations. To address this issue, citizen-based observation programs and remote sensing applications have been implemented; nevertheless, these strategies are still limited by infrequent and insufficient observations throughout space and time. To compensate, we developed a flower detection model for geographically and temporally dispersed street-level photos that may be used as the core component of a semi-automatic observation system. Specifically, we developed a customized YOLOv4 model for cherry blossom detection from street-level photos obtained through Mapillary, one of the social sensing data repositories. The detection model achieved an overall accuracy, recall, and precision of 86.7%, 70.3%, and 90.1%, respectively. By using observation coordinates and dates attached to Mapillary photos, we mapped the probability of cherry trees blooming in a spatial grid of dimensions 10 m x 10 m on a daily basis. With sufficient observations, start, peak, and end of blooming were estimated through time series analysis. A case study conducted at Saitama University's main campus in 2022 confirmed the possibility of mapping the presence of cherry blossoms and their blooming timing automatically. Since our approach relies solely on geotagged street-level photos that can be taken by anyone with no prior knowledge of cherry tree species identification, we anticipate that it will be easier to build blooming records over space and time than conventional stationary observations or citizen-based observation programs. This novel approach also has potential applications for detecting other species as well.