SpaceNet: Accelerating geospatial machine learning Road Extraction and Routing | SpaceNet on AWS
Building Foot Print Extraction Road Extraction and Routing

SpaceNet Challenge: Road Extraction and Routing

The Problem

The commercialization of the geospatial industry has led to an explosive amount of data being collected to characterize our changing planet. One area for innovation is the application of computer vision and deep learning to extract information from satellite imagery at scale. DigitalGlobe, CosmiQ Works, and NVIDIA have partnered to release the SpaceNet data set to the public to enable developers and data scientists to work with this data.

Today, map features such as roads, building footprints, and points of interest are primarily created through manual techniques. We believe that advancing automated feature extraction techniques will serve important downstream uses of map data including humanitarian and disaster response, as observed by the need to map road networks during the response to recent flooding in Bangladesh and Hurricane Maria in Puerto Rico. Furthermore, we think that solving this challenge is an important stepping stone to unleashing the power of advanced computer vision algorithms applied to a variety of remote sensing data applications in both the public and private sector.

The Metric

In the SpaceNet Roads Challenge, the metric for ranking entries is the APLS metric. This metric is based on graph theory and empahsizes the creation of a valid road network

The current version of the metric is open sourced on github: Average Path Length Similarity (APLS) metric For more information read the SpaceNet Road Detection and Routing Challenge Series, Part 1, and Part 2, written by Adam Van Etten at The DownlinQ.

Winning solutions:

The top five solutions have been open sourced at the SpaceNet Challenge Github Repository

Place Entrant Country Average Score Las Vegas Paris Shanghai Khartoum
1 albu Russia 0.6663 0.7977 0.604 0.6543 0.6093
2 cannab Russia 0.6661 0.7804 0.6446 0.6398 0.5996
2 pfr France 0.666 0.8009 0.6008 0.6646 0.5975
4 selim_sef Germany 0.6567 0.7884 0.5991 0.6472 0.5922
5 fbastani America 0.6284 0.771 0.5474 0.6326 0.5628
6 ipraznik Germany 0.6215 0.7578 0.5668 0.6078 0.5537
7 tcghanareddy India 0.6182 0.7591 0.571 0.6014 0.5415
8 hasan.asyari Norway 0.6097 0.7407 0.5557 0.5952 0.5472
9 aveysov Russia 0.5943 0.7426 0.5805 0.5751 0.4789

Top 5 Solutions:

  1. Albu

  2. (tie)cannab

  3. (Tie)pfr

  4. selim_sef

  5. fbastani

Click Here For the full leaderboard and submission history

The Data - Over 8000 Km of roads across the four SpaceNet Areas of Interest

AOI Area of Raster (Sq. Km) Road Centerlines (LineString)
AOI_2_Vegas 216 3685 km
AOI_3_Paris 1,030 425 km
AOI_4_Shanghai 1,000 3537 km
AOI_5_Khartoum 765 1030 km

For more details on the dataset visit the SpaceNet Roads Dataset website

Competition Updates:

The Roads competition is now over and the top 5 Algorithms have been open sourced. To see the code visit the SpaceNet Road Network Extraction and Routing Challenge Github repository

For competition email updates, Sign up here

Check out CosmiQ Work’s Blog, The DownLinQ or follow the SpaceNetUtilities Github Page

For general details about SpaceNet Competitions see the SpaceNet Competition Summary