In this final post of our series about the challenge, I’ll explore the types of buildings that models identified well and geographic features that presented a challenge to the competitors.
The SpaceNet Challenge Round 4: Off-Nadir Building Detection Challenge is complete! This blog highlights a few key differentiators that improved segmentation in the winning algorithms.
Highlights results from The SpaceNet Challenge: Round 4, Off-Nadir Building Footprint Extraction. The winning solutions represented a 1.5-fold improvement over the initial baseline model’s performance.
The SIMRDWN framework extends popular object detection algorithms to operate in the overhead imagery domain. This blog discusses training a model to detect cars in overhead images.
A deeper dive into CosmiQ’s study on super-resolution and object detection performance class specific results.
Part 3 of a discussion about an introductory outline for some of our work in exploring the relationships between super-resolution (SR) and object detection algorithms in satellite imagery.