SN5: Automated Road Network Extraction and Route Travel Time Estimation from Satellite Imagery
Determining optimal routing paths in near real-time is at the heart of many humanitarian, civil, military, and commercial challenges. This statement is as true today as it was two years ago when the SpaceNet Partners announced the SpaceNet Challenge 3 focused on road network detection and routing. In a disaster response scenario, for example, pre-existing foundational maps are often rendered useless due to debris, flooding, or other obstructions. Satellite or aerial imagery often provides the first large-scale data in such scenarios, rendering such imagery attractive.
The SpaceNet 5 challenge sought to build upon the advances from SpaceNet 3 and test challenge participants to automatically extract road networks and routing information from satellite imagery, along with travel time estimates along all roadways, thereby permitting true optimal routing.
The task of this challenge was to output a detailed graph structure with edges corresponding to roadways and nodes corresponding to intersections and end points, with estimates for route travel times on all detected edges. You can find a detailed description of CosmiQ Works’ algorithmic baseline on their blog at The DownLinQ.
SpaceNet open sourced new data sets for the following cities: Moscow, Russia; Mumbai, India; and San Juan, Puerto Rico. For the first time in SpaceNet history, the final submissions were tested on a mystery city dataset that was revealed and open sourced at the end of the Challenge.
Earn AWS GPU Credits!
The first 20 competitors to reach a score of 50 (out of a possible 100) received a credit for 10 hours on a p3.2xlarge for training and improving their models. To further aid competitors, the SpaceNet 5 baseline is fully open source, and yields a score of 54. If you have any questions, please reach out through the Topcoder Forum (https://www.topcoder.com/challenges/30099956).
- The SpaceNet 5 Baseline — Part 3: Extracting Road Speed Vectors from Satellite Imagery
- The SpaceNet 5 Baseline — Part 2: Training a Road Speed Segmentation Model
- The SpaceNet 5 Baseline — Part 1: Imagery and Label Preparation
- Computer Vision With OpenStreetMap and SpaceNet — A Comparison
- SpaceNet 5 Dataset Release
- Announcing SpaceNet 5: Road Networks and Optimized Routing
aws s3 ls s3://spacenet-dataset/spacenet/SN5_roads/
AOI 7 – Moscow – Road Network Extraction
To download processed ~400mx400m tiles of AOI 7 with associated road network labels for training do the following:
aws s3 cp s3://spacenet-dataset/spacenet/SN5_roads/tarballs/SN5_roads_train_AOI_7_Moscow.tar.gz .
AOI 9 – San Juan – Road Network Extraction
To download processed ~400mx400m tiles of AOI 9 for public testing do the following:
aws s3 cp s3://spacenet-dataset/spacenet/SN5_roads/tarballs/SN5_roads_test_public_AOI_9_San_Juan.tar.gz .
AOI 8 – Mumbai – Road Network Extraction
To download processed ~400mx400m tiles of AOI 8 with associated road network labels for training do the following:
aws s3 cp s3://spacenet-dataset/spacenet/SN5_roads/tarballs/SN5_roads_train_AOI_8_Mumbai.tar.gz .
AOI 10 – Mystery City – Road Network Extraction
To be released after the conclusion of the challenge.
In the SpaceNet Roads Challenge, the metric for ranking entries is the APLS metric. This metric is based on graph theory and emphasizes 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.
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 SpaceNet Dataset by SpaceNet Partners is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.