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.
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.
Part 3 of a series about the SpaceNet 4: Off-Nadir Dataset and Building Detection Challenge.
Part 2 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.
Discussing the interplay between super-resolution techniques and object detection frameworks, which remains largely unexplored, particularly in the context of satellite or overhead imagery.
Updating maps is currently a highly manual process requiring a large number of human labelers to either create features or rigorously validate automated outputs. We propose that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques.
Puerto Rico suffered severe damage from the category 5 hurricane (Maria) in September 2017. Total monetary damages are estimated to be ~92 billion USD, the third most costly tropical cyclone in US history. The response to this damage has been tempered and slow moving, with recent estimates placing 45% of the population without power three months after the storm.