  We present a new dataset, Functional Map of the World (fMoW), which aims to
inspire the development of machine learning models capable of predicting the
functional purpose of buildings and land use from temporal sequences of
satellite images and a rich set of metadata features. The metadata provided
with each image enables reasoning about location, time, sun angles, physical
sizes, and other features when making predictions about objects in the image.
Our dataset consists of over 1 million images from over 200 countries. For each
image, we provide at least one bounding box annotation containing one of 63
categories, including a "false detection" category. We present an analysis of
the dataset along with baseline approaches that reason about metadata and
temporal views. Our data, code, and pretrained models have been made publicly
available.
