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深度学习框架，该框架擅长处理卫星图像。这可以被地球观测从业者用于卫星图像的下游，用于许多不同的任务。我们将通过采用自监督学习来避免昂贵的卫星图像手动注释[3]，这是一种我们可以创建借口任务来训练网络以产生有用特征的范例。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]。该项目将涉及精心设计适合于卫星图像的托词任务（例如，这可以是时间或空间填充的混合），以及确定用于培训这些网络的最突出的数据。我们还将使用神经架构搜索算法[6，7]，以便底层网络架构针对这些图像而不是日常照片进行优化。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，应用我们的下游网络来解决两个重要的环境问题。我们将考虑的问题是非常不同的性质，这是我们的方法的鲁棒性的一个重要证明。

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.