SpaceNet 3: Road Network Detection

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. CosmiQ Works, Radiant Solutions 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.

RELATED BLOGS

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

See the labeling guide and schema for details about the creation of the dataset

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

Road Type Breakdown (km of Road)

Road Type	Vegas	Paris	Shanghai	Khartoum	Total
Motorway	115	9	102	13	240
Primary	365	14	192	98	669
Secondary	417	58	501	66	1042
Tertiary	3	11	34	68	115
Residential	1646	232	939	485	3301
Unclassified	1138	95	1751	165	3149
Cart track	2	6	19	135	162
Total	3685	425	3537.9	1030	8677

Catalog

The data is hosted on AWS as a Public Dataset. It is free to download, but an AWS account is required.

aws s3 ls s3://spacenet-dataset/spacenet/SN3_roads/

Sample Data

10 Samples from each AOI – Road Network Extraction Sample

To download processed 400mx400m tiles of AOI 2 (728.8 MB) with associated road centerlines for training do the following:

aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_sample.tar.gz .

Training Data

AOI 2 – Vegas – Road Network Extraction Training

To download processed 400mx400m tiles of AOI 2 (25 GB) with associated building footprints for training do the following:


aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_train_AOI_2_Vegas.tar.gz .

aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_train_AOI_2_Vegas_geojson_roads_speed.tar.gz .

AOI 3 – Paris – Road Network Extraction Training

To download processed 400mx400m tiles of AOI 3 (5.6 GB) with associated road centerlines for training do the following:


aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_train_AOI_3_Paris.tar.gz .

aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_train_AOI_3_Paris_geojson_roads_speed.tar.gz .

AOI 4 – Shanghai – Road Network Extraction Training

To download processed 400mx400m tiles of AOI 4 (25 GB) with associated road centerlines for training do the following:


aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_train_AOI_4_Shanghai.tar.gz .

aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_train_AOI_4_Shanghai_geojson_roads_speed.tar.gz .

AOI 5 – Khartoum – Road Network Extraction Training

To download processed 400mx400m tiles of AOI 5 (25 GB) with associated road centerlines for training do the following:


aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_train_AOI_5_Khartoum.tar.gz .

aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_train_AOI_5_Khartoum_geojson_roads_speed.tar.gz .

Testing Data

AOI 2 – Vegas – Road Network Extraction Testing

To download processed 400mx400m tiles of AOI 2 (8.1 GB) for testing do:


aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_test_public_AOI_2_Vegas.tar.gz .

AOI 3 – Paris – Road Network Extraction Testing

To download processed 400mx400m tiles of AOI 3 (1.9 GB) for testing do:


aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_test_public_AOI_3_Paris.tar.gz .

AOI 4 – Shanghai – Road Network Extraction Testing

To download processed 400mx400m tiles of AOI 4 (8.1 GB) for testing do:


aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_test_public_AOI_4_Shanghai.tar.gz .

AOI 5 – Khartoum – Road Network Extraction Testing

To download processed 400mx400m tiles of AOI 5 (8.1 GB) for testing do:


aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_test_public_AOI_5_Khartoum.tar.gz .

Citation Instructions

If you are using data from SpaceNet in a paper, please use the following citation:

Van Etten, A., Lindenbaum, D., & Bacastow, T.M. (2018). SpaceNet: A Remote Sensing Dataset and Challenge Series. ArXiv, abs/1807.01232.

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.

For more information read the full article written by Adam Van Etten at The DownlinQ.

License

The SpaceNet Dataset by SpaceNet Partners is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.