Resources posts

Inspiration and resources for teaching students data science, machine learning, and AI skills with DrivenData competitions.

Key practices from the field of open science for making data science work more transparent, inclusive, and equitable.

How can we use data and AI to help combat the effects of climate change? This second post pulls together real-world datasets from DrivenData challenges for those looking to get hands-on experience.

There is a huge variety of Earth observation datasets that can help to address our world's most pressing challenge. All the options can be hard to navigate. Learn about some key datasets and resources for getting started!

Feeling overwhelmed by the complexity of satellite data? We've been there. Join one of our data scientists on their learning journey to understand what satellite data is all about!

Although Zamba's models are trained with animals from Africa and Europe, they can be used with videos from other locations that show species the models have never seen. We demonstrate with a dataset from New Zealand.

Using probabilistic classifications from Zamba, we can automatically remove a large majority of blank videos while controlling the fraction of wildlife videos we lose. But how do we know where to draw the line?

In this post, we walk through the steps for getting started with the Meta AI Video Similarity Challenge.

We can use Zamba's probablistic classifications to search for videos containing specific animals. Particularly for small animals, this strategy can be highly effective.

Using Zamba's probablistic classifications, you can identify and remove blank videos -- saving viewing time, storage space, and data transfer costs -- while minimizing the loss of videos that contain animals.

In this post, we will introduce the Facebook AI Image Similarity Challenge and highlight some resources to help you get started.