Wildlife@Home: Combining Crowd Sourcing and Volunteer Computing to Analyze Avian Nesting Video

Abstract

New camera technology is allowing avian ecologists to perform detailed studies of avian behavior, nesting strategies and predation in areas where it was previously impossible to gather data. Unfortunately, studies have shown mechanical triggers and a variety of sensors to be inadequate in capturing footage of small predators (e.g., snakes, rodents) or events in dense vegetation. Because of this, continuous camera recording is currently the most robust solution for avian monitoring, especially in ground nesting species. However, continuous video footage results in a data deluge, as monitoring enough nests to make biologically significant inferences results in massive amounts of data which is unclassifiable by humans alone. In the summer of 2012, Dr. Ellis-Felege gathered video footage from 63 sharp-tailed grouse (Tympanuchus phasianellus) nests, as well as preliminary interior least tern (Sternula antillarum) and piping plover (Charadrius melodus) nests, resulting in over 20,000 hours of video footage. In order to effectively analyze this video, a project combining both crowd sourcing and volunteer computing was developed, where volunteers can stream nesting video and report their observations, as well as have their computers download video for analysis by computer vision techniques. This provides a robust way to analyze the video, as user observations are validated by multiple views as well as the results of the computer vision techniques. This work provides initial results analyzing the effectiveness of the crowd sourced observations and computer vision techniques.

A computer vision for animal ecology

“…The natural excitement for plants and animals means that gathering further labelled data is possible through online citizen scientist efforts (Van Horn et al., ). In particular, projects on the Zooniverse, iNaturalist and Wildlife @home web platforms provide a way of engaging important user communities (Desell et al., ; Kosmala et al., ). The next step is integrating citizen scientists as a part of greater automation, rather as an alternative to automation.…”

“…The growth in ecological image data is fuelled by its economy, efficiency and scalability (Bowley, Andes, Ellis‐Felege, & Desell, ; Dell et al., ). Massive repositories of image data are available for ecological analysis, uploaded from field‐based cameras (Giraldo‐Zuluaga, Gomez, Salazar, & Diaz‐Pulido, ; Swanson et al., ; Zhang, He, Cao, & Cao, ) or captured by citizen scientists (Desell et al., ; Joly et al., ). For example, research grade datasets from iNaturalist (675,000 images of 5,000 species, Van Horn et al., ) and Zooniverse (1.2 million images of 40 species; Swanson et al., ), highlight the growth in high‐quality images captured by researchers and the public.…”

On the Effectiveness of Crowd Sourcing Avian Nesting Video Analysis at Wildlife@Home

“…A third server handles all webpages for the Wildlife@Home project and the database user information and volunteer computing. This infrasructure is presented in detail in Desell et al [4]. Wildlife@Home has gone through two iterations of crowd sourcing interfaces, which are described in Sections 3.3 and 3.4.…”

Putting the Scientist in the Loop -- Accelerating Scientific Progress with Interactive Machine Learning

“…In the past, biologists would manually code the data, hand-labeling it according to features of interest (such as the presence of relevant species). Some crowdsourcing and citizen science projects have also been shown to be effective at mobilizing large groups of individuals to help with the annotation [7], [8], [9], [10]. Unfortunately, in many scenarios annotations from non-experts can be noisy, and it can thus be expensive to recruit the large numbers of skilled labelers required for big datasets [11].…”