The use of ML and satellite data for causal inference provides unique possibilities, but also comes with a unique set of challenges.

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The ability to capture imagery and record data of the same site at different periods of time can be leveraged for multiple purposes.

Spatial resolution refers to the size of the smallest possible feature that can be displayed in a satellite image.

Machine learning has tremendous potential to solve a multitude of developmental challenges faced by governments and nonprofits in the global south.

Machine learning on satellite imagery has a long history, but an understanding of its possible role in the developing world came a little later than it did for other data sources.

Machine learning is a family of techniques that use data and experiences to create an interpretation of patterns in the world and use that interpretation to answer questions about the world.

Convolutional Neural Networks (CNNs) are one important subset of deep learning models that are particularly useful for processing image input data.