@article{kellenberger2020aide,
author = {Kellenberger, Benjamin and Tuia, Devis and Morris, Dan},
title = {AIDE: Accelerating Image-Based Ecological Surveys with Interactive Machine Learning},
year = {2020},
month = {November},
abstract = {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. AIDE is open-source and can be downloaded for free at https://github.com/microsoft/aerial_wildlife_detection.},
url = {http://approjects.co.za/?big=en-us/research/publication/aide-accelerating-image-based-ecological-surveys-with-interactive-machine-learning/},
pages = {1716-1727},
journal = {Methods in Ecology and Evolution},
volume = {11},
number = {12},
}