This challenge is no longer accepting new submissions.
functional Map of the World (fMoW) Challenge
Can you build algorithms to classify facility, building, and land use from satellite imagery?
Office of Director of National Intelligence - Intelligence Advanced Research Project Activity
Submission Start: 09/14/2017 12:00 PM ET
Submission End: 12/31/2017 12:00 PM ET
This challenge is externally hosted.
You can view the challenge details here: https://www.iarpa.gov/challenges/fmow.html
DescriptionIntelligence analysts, policy makers, and first responders around the world rely on geospatial land use data to inform crucial decisions about global defense and humanitarian activities. Historically, analysts have manually identified and classified geospatial information by comparing and analyzing satellite images, but that process is time consuming and insufficient to support disaster response. The Functional Map of the World (fMoW) Challenge seeks to foster breakthroughs in the automated analysis of overhead imagery by harnessing the collective power of the global data science and machine learning communities. The challenge will publish one of the largest publicly available satellite-image datasets to date, with more than one million points of interest from around the world. The dataset contains satellite-specific metadata that researchers can exploit to build a competitive algorithm that classifies facility, building, and land use.
Be Part of the InnovationIARPA is conducting this Challenge to invite the broader research community of industry and academia, with or without experience in deep learning and computer vision analysis, to participate in a convenient, efficient and non-contractual way. Participants will develop algorithms that scan satellite data to identify functions based on multiple reference sources, such as overhead and ground-based images, digital elevation data, existing well-understood image collections, surface geology, geography, and cultural information. The goals and objectives of this Challenge are to:
- Promote and benchmark deep learning applications for geospatial data
- Stimulate various communities to develop and enhance automation for image/video geolocation data
- Cultivate and sustain an ongoing collaborative community dedicated to this technology and research