From Crops to Glaciers: A Deep Learning Framework for Earth Observation

该项目的目标是满足这一需求，并以神经网络的形式创建一个擅长处理卫星图像的深度学习框架。这可以被下游的地球观测从业者使用卫星图像来完成许多不同的任务。我们将通过使用自监督学习[3]来避免对卫星图像进行昂贵的手动注释的需要，这是一个范例，我们可以创建借口任务来训练网络产生有用的特征。对于日常照片，这些借口任务包括预测旋转[4]，和解决拼图[5]。该项目将涉及仔细设计适合卫星图像的借口任务（例如，这可能是时间或空间填充的混合），以及确定训练这些网络的最重要数据。我们还将使用神经架构搜索算法[6,7]，以便对底层网络架构进行优化，以用于这些图像，而不是日常照片。为了证明我们框架的有效性，我们将利用PlanetScope和Sentinel-1/2卫星的数据，在下游应用我们的网络来解决两个重要的环境问题。我们将考虑的问题在本质上是非常不同的；这是我们方法稳健性的重要证明。

The goal of this project is to satisfy this need and create a deep learning framework in the form of a neural network that excels at working with satellite imagery. This can be used downstream by earth observation practitioners working with satellite images, for a host of disparate tasks. We will avoid the need for expensive manual annotation of satellite images by employing self-supervised learning [3], a paradigm where we can create pretext tasks to train networks to produce useful features. For everyday photos these pretext tasks include predicting rotations [4], and solving jigsaw puzzles [5]. This project will involve the careful design of pretext tasks suitable for satellite images (this could be e.g. a mixture of temporal or spatial infilling) as well as identifying the most salient data for training these networks. We will also use neural architecture search algorithms [6, 7] so that the underlying network architecture is optimised for use with these images, rather than everyday photos. To demonstrate the effectiveness of our framework we will apply our network downstream to tackle two important environmental problems, employing data from the PlanetScope and Sentinel-1/2 satellites. The problems we will consider are very different in nature; this is an important demonstrator for the robustness of our approach.