RarePlanes is a unique open-source machine learning dataset from CosmiQ Works and AI.Reverie that incorporates both real and synthetically generated satellite imagery.

 The RarePlanes dataset specifically focuses on the value of AI.Reverie synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. Although other synthetic/real combination datasets exist, RarePlanes is the largest openly-available very-high resolution dataset built to test the value of synthetic data from an overhead perspective. Previous research has shown that synthetic data can reduce the amount of real training data needed and potentially improve performance for many tasks in the computer vision domain. The real portion of the dataset consists of 253 Maxar WorldView-3 satellite scenes spanning 112 locations and 2,142 km^2 with 14,700 hand-annotated aircraft. The accompanying synthetic dataset is generated via AI.Reverie’s novel simulation platform and features 50,000 synthetic satellite images with ~630,000 aircraft annotations. Both the real and synthetically generated aircraft feature 10 fine grain attributes including: aircraft length, wingspan, wing-shape, wing-position, wingspan class, propulsion, number of engines, number of vertical-stabilizers, presence of canards, and aircraft role. Finally, we conduct extensive experiments to evaluate the real and synthetic datasets and compare performances. By doing so, we show the value of synthetic data for the task of detecting and classifying aircraft from an overhead perspective.

RarePlanes also included an experimental portion using an expanded version of the public dataset.  The experiments focused on addressing these two areas:

1. The performance tradeoffs of computer vision algorithms for detection and classification of aircraft type / model using blends of synthetic and real training data.
2. The performance tradeoffs of computer vision algorithms for identification of rare aircraft that are infrequently observed in satellite imagery using blends of synthetic and real training data.

The RarePlanes blog series includes 4 on the intial experiments and a penultimate blog featuring the dataset release:

1. RarePlanes — An Introduction
2. RarePlanes — Training our Baselines and Initial Results
3. RarePlanes — Exploring the Value of Synthetic Data: Part 1
4. RarePlanes — Exploring the Value of Synthetic Data: Part 2
5. RarePlanes — Dataset, Paper, and Code Release

SpaceNet accelerates research and innovation in geospatial machine learning by developing and providing publicly available commercial satellite imagery and labeled training data, as well as open sourcing computer vision algorithms and tools.

The SpaceNet 5 challenge focused on road network detection and routing information and travel time extraction. Optimized routing is crucial to a number of challenges, from humanitarian to military. Satellite imagery may aid greatly in determining efficient routes, particularly in cases of natural disasters or other dynamic events where the high revisit rate of satellites may be able to provide updates far faster than terrestrial methods.

Learn more at www.spacenet.ai

Can mapping be automated from off-nadir imagery?

In many time-sensitive applications, such as humanitarian response operations, overhead imagery is often taken “off-nadir” – that is, not from directly overhead – particularly immediately following an event or in other urgent collection contexts. Despite significant advances in using machine learning and computer vision to automate detection of objects like automobiles, aircraft, and vehicles in overhead imagery, no one had tested if the approaches would work on off-nadir images. CosmiQ led the SpaceNet 4 Challenge which asked participants to develop machine learning algorithms to identify buildings in images from the new SpaceNet Atlanta Off-Nadir Dataset. The dataset comprises 27 distinct images over Atlanta, GA taken during a single overhead pass of the DigitalGlobe WorldView-2 satellite. These images range from 7º (nearly directly overhead) to 54º off-nadir (very off-angle and consistent with urgent collection data) to include both North and South-facing views. Alongside the imagery we released building labels for the same 665 km² area covered by the imagery. Shadows, distortion, and resolution vary dramatically across these images, presenting a complete picture of the challenges posed by off-nadir imagery.

Nearly 250 competitors registered for the two-month challenge, and the winners improved baseline performance by about 40%. Once the challenge was completed, we performed a deep dive into their solutions to determine how their algorithms optimized building footprint extraction from off-nadir images, where they failed, and where future research should focus to address this difficult task. Results from these analyses can be found in our blog posts and published papers.

Learn more at www.spacenet.ai.

Millions of kilometers of the worlds’ roadways remain unmapped. In fact, there are large organizations such as the Humanitarian OpenStreetMap Team (HOT) Missing Maps Project whose entire goal is to map missing areas. The SpaceNet 3 Road Detection and Routing Challenge was designed to assist the development of techniques for generating road networks from satellite imagery. The deployment of these techniques will hopefully expedite the development and publication of accurate maps.

The Challenge specifically asked participants to turn satellite imagery into usable road network vectors. For this challenge, we created a new metric, Average Path Length Similarity (APLS) for evaluating the similarity between a ground truth and proposal road network. We also created new feature labels specifically for this challenge. The new dataset consists of 8,000 km of road centerlines with associated attributes such as road type, surface type, and number of lanes. All roads were digitized from existing SpaceNet data — 30 cm GSD WorldView 3 satellite imagery over Las Vegas, Paris, Shanghai, and Khartoum.

The challenge was conducted from November 2017 to February 2018 and hosted on the Topcoder platform. It received 342 submissions from 33 challenge participants from the across the world. The code for the top five submissions were open sourced under the Apache 2 License on SpaceNet Github repository.

CosmiQ Works conducted this project in coordination with the other SpaceNet Partners: Radiant Solutions, Amazon Web Services, and NVIDIA.

Learn more at www.spacenet.ai.