1. 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.
2. 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.
3. 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.
4. 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.

中文翻译：

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AIDE：通过交互式机器学习加速基于图像的生态调查

1. 生态调查越来越依赖大型图像数据集，单次调查通常需要数TB的图像。收集大量数据的能力允许以前所未有的规模进行调查，但要付出大量的图片解释劳动。
2. 我们介绍了数据驱动生态系统的注释接口（AIDE），这是一个开放源代码的Web框架，旨在减轻生态调查中图像注释的任务。AIDE使用易于使用且可自定义的标签界面，该界面支持多个用户，数据库存储以及对云和/或多台计算机的可伸缩性。
3. 此外，AIDE将用户和机器学习模型紧密集成到一个反馈环中，在该反馈环中，用户提供的注释用于重新训练模型，然后将后者应用于未标记的图像以识别野生动植物。然后，根据可自定义的主动学习准则，以最佳顺序将这些预测呈现给用户。AIDE内置了许多深度学习模型，但也接受自定义模型实现。
4. 数据驱动生态学的注释界面有潜力极大地加速注释任务的应用，从而广泛应用图像数据。AIDE是开源的，可以从https://github.com/microsoft/aerial_wildlife_detection免费下载。