Uncertainty-Aware Enrichment of Animal Movement Trajectories by VGI

Abstract

AbstractCombining data from different sources and modalities can unlock novel insights that are not available by analyzing single data sources in isolation. We investigate how multimodal user-generated data, consisting of images, videos, or text descriptions, can be used to enrich trajectories of migratory birds, e.g., for research on biodiversity or climate change. Firstly, we present our work on advanced visual analysis of GPS trajectory data. We developed an interactive application that lets domain experts from ornithology naturally explore spatiotemporal data and effectively use their knowledge. Secondly, we discuss work on the integration of general-purpose image data into citizen science platforms. As part of inter-project cooperation, we contribute to the development of a classifier pipeline to semi-automatically extract images that can be integrated with different data sources to vastly increase the number of available records in citizen science platforms. These works are an important foundation for a dynamic matching approach to jointly integrate geospatial trajectory data and user-generated geo-referenced content. Building on this work, we explore the joint visualization of trajectory data and VGI data while considering the uncertainty of observations. BirdTrace, a visual analytics approach to enable a multi-scale analysis of trajectory and multimodal user-generated data, is highlighted. Finally, we comment on the possibility to enhance prediction models for trajectories by integrating additional data and domain knowledge.