Today the National Geospatial-Intelligence Agency announced five winners in its $50,000 global competition to search geographic areas and accurately identify a specific shape.
The Circle Finder competition sought novel automated approaches to detect, delineate and describe circular-shaped features varying in size and physical composition in satellite imagery. Examples include agricultural irrigation areas, fuel storage tanks, buildings, traffic circles and fountains.
"NGA is always seeking new and innovative solutions to forward the geospatial tradecraft,” said Jack Brandy, NGA’s project manager for the challenge. “I'm very pleased with the results of the competition and how quickly the solver community was able to tackle the problem.”
Submissions required a working algorithm and white paper description of the solution. Solvers were able to submit and test their code solutions, view their quantitative score and participate in a leaderboard indicating solver rank. The winners were determined on accuracy rank of their submission.
The competition received 27 submissions from U.S. and international innovators in industry and academia.
The five winners and the technical descriptions of their machine learning solutions are:
- 1st Place: Selim Seferbekov– $20,000
- Seferbekov’s winning solution approached the challenge as a binary segmentation problem of object body and object contours and used a convolutional neural network (CNN) with a pretrained EfficientNet B3encoder and a U-Net like decoder