{"id":825829,"date":"2022-03-11T14:56:31","date_gmt":"2022-03-11T22:56:31","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=825829"},"modified":"2022-12-01T18:26:40","modified_gmt":"2022-12-02T02:26:40","slug":"aide-accelerating-image-based-ecological-surveys-with-interactive-machine-learning","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/aide-accelerating-image-based-ecological-surveys-with-interactive-machine-learning\/","title":{"rendered":"AIDE: Accelerating Image-Based Ecological Surveys with Interactive Machine Learning"},"content":{"rendered":"

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 (opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

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 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