Abstract
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 countries1
. 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.