Using convolutional neural networks to efficiently extract immense phenological data from community science images

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessibl...

社区科学影像库为物候学研究提供了规模庞大但尚未得到充分开发的观测数据资源。iNaturalist平台的馆藏尤为丰富，收录超4900万份可验证、带地理坐标的开放获取影像，覆盖七大洲与超过27.8万个物种。制约科研人员充分利用这一优质数据源的关键瓶颈在于人工投入：每一张影像均需人工核查并按物候期（phenophase）进行分类，不仅耗时极长且成本高昂。因此，研究人员往往仅能使用数据库中可用影像的一小部分。尽管iNaturalist平台具备为高分辨率、大空间尺度研究提供充足数据的潜力，但仍需更高效的物候数据提取工具。借助深度学习（deep learning）实现影像标注流程自动化则是极具前景的解决方案。近年来深度学习领域的创新已令这类开源工具变得易于获取……