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.
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.
The SpaceNet 4: Off-nadir building detection challenge has begun, and participants are vying for $50,000 of prize money by competing to see who can most accurately identify buildings in 27 different WorldView 2 satellite image collects taken at different angles over Atlanta.
Rapid detection of small objects over large areas remains one of the principal drivers of interest in satellite imagery analytics. This blog introduces the Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) framework.