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.
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.
SpaceNet is proud to release the Off-Nadir Imagery dataset, specifically built to explore advanced algorithms capabilities to process high off-nadir imagery. The dataset includes 27 WorldView 2 Satellite images from 7 degrees to 54 degrees off-nadir all captured within five minutes of each other.