# Functional Map of the World Source: [Functional Map of the World]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:00:11.434Z Authors: Gordon Christie, Neil Fendley, James Wilson, Ryan Mukherjee Published: 2017-11-21T15:28:00.000Z ## 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 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multi-class image classification on NUS-WIDE geo-tagged (test)](): Multi-class Image Classification; NUS-WIDE geo-tagged (test); mAP: 23.8. ## Original sources and implementation [Original paper]() [PDF]()