RESUMO
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 accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.
RESUMO
The objective of this work is to demonstrate a method for examining the competing effects of secular trends in body size, seat size and configuration, and the increased load factor of aeroplanes. The method uses statistical modelling and virtual fit testing to provide a flexible environment for exploring the impact of various parameters on passenger accommodation. A case study demonstrates the method by exploring the effect of seat width on the accommodation of US civilians (based on seated hip breadth). The case study demonstrates that recent trends of decreasing seat widths and increasing load factors lead to higher disaccommodation. Based on anthropometry and virtual fit, women are also shown to be disproportionately disaccommodated compared to men. Practitioner summary: Airlines are reducing seat width at the same time that individuals worldwide are getting larger. Flights are increasingly crowded, with load factor at a record high. This paper explores the effects of seat width on passenger accommodation under several scenarios involving load factor, demographics, and passenger seating allocation strategies.
