Each building is assigned a unique identifier (i. e. address), which permits tracking of individual objects over time.
RarePlanes is a unique open-source machine learning dataset that incorporates both real and synthetically generated satellite imagery.
2 code implementations • 14 Apr 2020 • Jacob Shermeyer, Daniel Hogan, Jason Brown, Adam Van Etten, Nicholas Weir, Fabio Pacifici, Ronny Haensch, Alexei Bastidas, Scott Soenen, Todd Bacastow, Ryan Lewis
The dataset and challenge focus on mapping and building footprint extraction using a combination of these data sources.
Identification of road networks and optimal routes directly from remote sensing is of critical importance to a broad array of humanitarian and commercial applications.
To address this problem, we present an open source Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks from a broad range of viewing angles (-32. 5 degrees to 54. 0 degrees).
We also quantify the performance of object detection as a function of native resolution and object pixel size.
Based upon our approach we can identify and evaluate: 1) the recovery of electrical power compared to pre-storm levels, 2) the location of potentially damaged infrastructure that has yet to recover from the storm, and 3) the number of persons without power over time.