Overhead Geopose Challenge Hosted By NGA

5 weeks left

Working with lab samples

Images shown are from the public Urban Semantic 3D Dataset, provided courtesy of DigitalGlobe


Overhead satellite imagery provides critical time-sensitive information for use areas like disaster response, navigation, and security. Most current methods for using aerial imagery assume images are taken from directly overhead, or “near-nadir”. However, the first images available are often taken from an angle, or are “oblique”. Effects from these camera orientations complicate useful tasks like change detection, vision-aided navigation, and map alignment.

In this challenge, your goal is to make satellite imagery taken from a significant angle more useful for time-sensitive applications like disaster and emergency response.

To take on the challenge, you will transform RGB images taken from a satellite to more accurately determine each object’s real-world structure or “geocentric pose”. Geocentric pose is an object’s height above the ground and its orientation with respect to gravity. Calculating geocentric pose helps with detecting and classifying objects and determining object boundaries.

By contributing to this challenge, you can help advance state-of-the-art methods for using and understanding satellite imagery. On your marks, get set, pose!

This competition will include two stages:

Place Prize Amount
1st $20,000
2nd $12,000
3rd $6,000
4th $3,000

Prediction Contest

Submissions due July 19, 2021, 11:59 p.m. UTC

Results of predictive algorithms evaluated using the competition metric. Final rankings displayed on the private leaderboard.

Prize Amount
Bonus 1 $3,000
Bonus 2 $3,000
Bonus 3 $3,000

Model Write-up Bonus

Submissions due Aug. 2, 2021, 11:59 p.m. UTC

Evaluated on write-ups of modeling approaches. The top 15 finalists from the Prediction Contest are eligible to submit write-ups for judging. Final winners will be selected by a judging panel.

How to compete

  1. Click the “Compete” button in the sidebar to enroll in the competition
  2. Get familiar with the problem through the overview and problem description. You might also want to reference some of the additional resources from the about page.
  3. Download the data from the data tab
  4. Create and train your own model. The benchmark blog post is a good place to start
  5. Use your model to generate predictions that match the submission format
  6. Click “Submit” in the sidebar, and “Make new submission”. You’re in!

This challenge is sponsored by the National Geospatial-Intelligence Agency (NGA)

With support from the Johns Hopkins University Applied Physics Laboratory (JPU/APL) and NASA

*Main image is from the public Urban Semantic 3D Dataset, provided courtesy of DigitalGlobe

Approved for public release, 21-545