This post discusses how to apply these trained deep learning object detection models to large test images and visualize results. It also discusses some of the new features of the recently released SIMRDWN v2, such as incorporation of the latest TensorFlow models and YOLO v3.
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.