论文标题
IWildCAM 2020竞赛数据集
The iWildCam 2020 Competition Dataset
论文作者
论文摘要
相机陷阱可以自动收集大量图像数据。世界各地的生物学家使用相机陷阱来监测动物种群。最近，我们在相机陷阱图像中迈向自动物种分类。但是，随着我们试图扩大这些模型的地理范围，我们面临一个有趣的问题：我们如何在训练相机陷阱位置进行新的（在训练）摄像机陷阱位置进行训练良好的模型？我们可以利用其他模式的数据，例如公民科学数据和遥感数据吗？为了解决这个问题，我们已经准备了一个挑战，即训练数据和测试数据来自遍布全球的不同相机。对于每个相机，我们提供一系列与相机位置相关的遥感图像。我们还从数据中看到的一组物种提供了公民科学图像。挑战是正确对测试摄像头陷阱中的物种进行分类。
Camera traps enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor animal populations. We have recently been making strides towards automatic species classification in camera trap images. However, as we try to expand the geographic scope of these models we are faced with an interesting question: how do we train models that perform well on new (unseen during training) camera trap locations? Can we leverage data from other modalities, such as citizen science data and remote sensing data? In order to tackle this problem, we have prepared a challenge where the training data and test data are from different cameras spread across the globe. For each camera, we provide a series of remote sensing imagery that is tied to the location of the camera. We also provide citizen science imagery from the set of species seen in our data. The challenge is to correctly classify species in the test camera traps.
