An article of Benjamin Kellenberger, Devis Tuia and Dan Morris: AIDE: Accelerating image‐based ecological surveys with interactive machine learning, has been published in Methods in Ecology and Evolution.
Ecological surveys increasingly rely on large‐scale image datasets, typically terabytes of imagery for a single survey. The ability to collect this volume of data allows surveys of unprecedented scale, at the cost of expansive volumes of photo‐interpretation labour. We present Annotation Interface for Data‐driven Ecology (AIDE), an open‐source web framework designed to alleviate the task of image annotation for ecological surveys. AIDE employs an easy‐to‐use and customisable labelling interface that supports multiple users, database storage and scalability to the cloud and/or multiple machines. Moreover, AIDE closely integrates users and machine learning models into a feedback loop, where user‐provided annotations are employed to re‐train the model, and the latter is applied over unlabelled images to e.g. identify wildlife. These predictions are then presented to the users in optimised order, according to a customisable active learning criterion. AIDE has a number of deep learning models built‐in, but also accepts custom model implementations. Annotation Interface for Data‐driven Ecology has the potential to greatly accelerate annotation tasks for a wide range of researches employing image data.