Yet Another Computer Vision Index To Datasets (YACVID) - Details

Stand: 2020-06-05 000000m 07:49:30 - Overview

Attribute Current Content New
Name (Institute + Shorttitle)Topcoder FMOW - Functional Map of the World Challenge 
Description (include details on usage, files and paper references)Intelligence 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 Innovation
IARPA 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

Participants will be scored based on their ability to correctly classify known portions of satellite images. Submissions will be scored automatically through a scoring algorithm, and then final algorithms will be independently evaluated for speed and accuracy. The competition will launch in July:

Begins Jul 2017
Register for the challenge at
Datasets & Training
Jul - Oct 2017
Download the data and build your solution.
Challenge Opens
Sep 2017
Find out how your solution performs on the challenge leaderboard and retune your algorithm as necessary to increase accuracy.
Final Submission
Dec 2017
The top 10 algorithms will be scored against a hidden data set and validated by the IARPA team for award.
Awards & Recognition
Feb 2018
Present your solutions to IARPA and other key leaders in the computer vision industry in Washington, D.C. and receive cash awards!
Challenge Opens
Sep 2017
Register for the challenge at

Datasets & Training
Jul - Oct 2017
IARPA has released a large satellite imagery dataset with training, validation, and testing imagery subsets to support the fMoW Challenge. The visualization tool and benchmark example can be found here. The testing and training data are availabe for download via two options: 1) Amazon Web Services (AWS) and 2) BitTorrent. Below are detailed instructions for downloading the data:

To obtain the data via AWS, you must utilize Requestor Pays. The data is available in two versions:

RGB JPG Data Set: arn:aws:s3:::fmow-rgb | s3://fmow-rgb
Multispectral TIFF Data Set: arn:aws:s3:::fmow-full | s3://fmow-full
A full set of AWS CLI resources can be found here:
Some example commands appear below:

There is a manifest.json.bz2 file in each bucket that can be downloaded to get a json that lists everyfile in the bucket

aws s3api get-object --bucket fmow-rgb --key manifest.json.bz2 --request-payer requester
aws s3api get-object --bucket fmow-full --key manifest.json.bz2 --request-payer requester
Commands like these can be used to get a directory listing

aws s3 ls s3://fmow-rgb --request-payer requester
aws s3 ls s3://fmow-full --request-payer requester
To obtain the data via BitTorrent, will require you to download, install, and ensure the correct configuration of your own BitTorrent client. Once you have a client installed and properly configured, downloading the data sets is relatively simple. Make sure you have a copy of the full data set, then with the torrent files of your choosing downloaded, simply open them within the client of your choosing and follow your client’s instructions to begin downloading the data.

We encourage contestants who choose to use this BitTorrent to continue to seed the data after their download is complete to help build and maintain the healthy population of seed nodes. 
URL Link 
Files (#)10000000 
References (SKIPPED)
Category (SKIPPED) 
Tags (single words, spaced)aerial segmentation semantic topcoder rgb satellite urban building 
Last Changed2020-06-05 
Turing (2.12+3.25=?) :-)