Predicting Rain from Satellite Images (Part 1)


Predicting and understanding weather has become crucial in a number of industries, including agriculture, autonomous driving, aviation, or the energy sector. For example, weather conditions play a significant role for aviation and logistics companies in planning the fastest and safest route. Similarly, renewable energy companies need to be able to predict the amount of energy they will produce at a given day. As a consequence various weather models have been developed and are being applied all over the world. Unfortunately, these models often require highly specific information about the atmosphere and exact conditions.

For this reason, Meteomatics, a weather API that delivers fast, direct and simple access to an extensive range of global weather, climate projections and environmental data, has reached out to us for help. Their goal: To predict precipitation accurately in regions where data is sparse and they have to rely on satellite imagery. In this blog post, we show how we developed a neural network to predict the amount of rainfall in a given region based on infrared satellite data.

This is part one of a two-part blog:

Data collection and analysis

If you have ever worked with neural networks you know that they can be data hungry. For this reason it’s crucial to set up a data pipeline that allows you to collect, manage, and understand the assembled data. Our collaboration partner, Meteomatics, offers an easy-to-use API which enables us to quickly gather training and ground-truth data. For example, to get an infra-red picture of Europe (coordinates from 65, -15 to 35, 20) on the seventh of July 2021 and at a resolution of 800x600 pixels we can simply make the following query:

We ran a Python script every quarter hour for a few days collecting infra-red images over Europe, North America, and Mexico at different wavelengths. We then locally combined the different images for each timestamp into an RGB image. To make the task easier we masked out stratiform precipitation in a first step. However, as we will see later on, this only has a small effect on the accuracy of the model. We also collected ground-truth data for training and evaluating the accuracy of our model. Note that ground-truth data was only available for Europe and North America. Below you can see a pair of input and ground-truth data over Europe:

Example of the satellite image (left), the input to the neural network (middle) and the ground-truth data (right). The light-gray areas on the input and ground-truth images are masked out based on the lifted index. The data was collected over Europe on Tuesday, 13. July 2021 20:15:02.

Following the notorious “garbage in garbage out” mantra, we wanted to understand and curate the collected data before we trained a machine learning algorithm on it. For this, we used our free-to-use exploration tool Lightly enables quick and easy ways to analyze a dataset as well as more in-depth algorithms to pick the most relevant training points. After uploading our dataset to Lightly we immediately noticed a crucial property of the collected data: The images over Europe, North America, and Mexico were visually and semantically separated. This resulted in a simple strategy to test the generalization capacity of the algorithm: If we trained it on the data from Europe and it performed well on unseen data from North America and Mexico, the algorithm would generalize well. Note that if we had picked the training dataset and the test dataset to be very similar, then all we would test is the memory of the neural network.

Embeddings of the input images from Europe (blue), North America (green), and Mexico (grey) as shown by the data curation application The images are well separated in the embedding space.

Another key insight we gained was that there were many small clusters of extremely similar images. This is due to the fact that we collected data over a relatively short period of time. Because of this, there were a lot of similar images in the dataset which made it harder for the model to generalize well. Lightly helped us with removing these redundancies with a method called “coreset sampling” which aims to maximize the diversity of the dataset.

Before curating the dataset with Lightly, we had 1158 images in our training dataset (Europe). After data curation, we are left with 578 images. The validation dataset (North America) consists of 1107 images and the test dataset (Mexico) consists of only 43 images as we began data collection later.

We download the images from Lightly and and now we are ready to do some machine learning. Head to Part 2 to see the results!

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