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.
Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. We propose a pipeline (SIMRDWN) that evaluates satellite images of arbitrarily large size at native resolution at a rate of > 0.2 km2/s.