Mobilization of trait data from digital image files by deep learning approaches

通过深度学习方法从数字图像文件中调动特征数据

• 批准号: 316452578
• 负责人: Dr. Stefan Dressler (†)
• 金额: --
• 依托单位: Max-Planck-Institut für Biogeochemie (MPI-BGC)
• 依托单位国家: 德国
• 项目类别: Research data and software (Scientific Library Services and Information Systems)
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/316452578?language=en
• 关键词: Mobilization; trait; data; digital; image

Based upon digital images of plants (photos, specimen scans) and related taxon- and trait data we will develop and train deep-learning algorithms to recognize (a) traits in other image files and (b) use the identified traits as a step equivalent to a conventional determination process and using deep learning again to recognize taxa in image files. This approach will be used for massive mobilization of trait data for use in ecology and taxonomy, as well as for the automatic identification of species in digitized collections and citizen science portals. As part of the project will be demonstrated how (1) photos from citizen science portals and photo communities can be used by automated detection of flower and fruit traits to document the impact of climate change on phenology, (2) feature recognition can be used as a first step towards taxon recognition in citizen science portals, (3) the taxon recognition can be used for quality control and extended documentation in digitized collection data and (4) may serve to track the spread of invasive species through civil science portals and photo communities. The newly mobilized trait data will be made available to the science community via the contributing information infrastructures and via long term data repositories in the frame of GFBio.

基于植物（照片，标本扫描）和相关分类单元和特征数据的数字图像，我们将开发和训练深度学习算法，以识别（a）其他图像文件中的特征，（b）将确定的性状用作阶跃等效的特征进行常规的确定过程，并再次使用深度学习来识别图像文件中的分类单元。这种方法将用于大规模动员特征数据，以用于生态和分类法，以及在数字化收藏和公民科学门户中自动识别物种。作为项目的一部分，将证明（1）如何通过自动检测花和水果特征来使用公民科学门户和照片群落的照片来记录气候变化对物候的影响，（2）可以用作特征识别。 （3）分类单元识别可用于质量控制和扩展文档，在数字化收集数据中迈出了迈向分类单元识别的第一步，并且（4）可以用于跟踪入侵物种通过内科科学和照片社区的传播。新动员的性状数据将通过贡献信息基础架构以及GFBIO框架中的长期数据存储库提供给科学界。