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SpaceNet 3: Road Network Detection

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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.

SpaceNet 2: Building Detection v2

By

SpaceNet 2: Building Detection v2

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 685,000 footprints across the Five SpaceNet Areas of Interest.

AOI Area of Raster (Sq. Km) Building Labels (Polygons)
AOI_2_Vegas 216 151,367
AOI_3_Paris 1,030 23,816
AOI_4_Shanghai 1,000 92,015
AOI_5_Khartoum 765 35,503

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/SN2_buildings/

The Metric

In SpaceNet Challenge, the metric for ranking entries is based on the Jaccard Index, also called the Intersection-over-Union (IoU). For more information read the full article on The DownlinQ.

Labeling Guidelines

For more information about the labeling guidelines, please view the SpaceNet Buildings Dataset Labeling Guide

Sample Data

10 Samples from each AOI – Road Network Extraction Samples

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/SN2_buildings/train/tarballs/ SN2_buildings_train_sample.tar.gz .

Training Data

AOI 2 – Vegas – Building Extraction Training

To download processed 200mx200m tiles of AOI 2 (23 GB) with associated building footprints for training do the following:

aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/SN2_buildings_train_AOI_2_Vegas.tar.gz .

AOI 3 – Paris – Building Extraction Training

To download processed 200mx200m tiles of AOI 3 (5.3 GB) with associated building footprints do the following:

aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/SN2_buildings_train_AOI_3_Paris.tar.gz .

AOI 4 – Shanghai – Building Extraction Training

To download processed 200mx200m tiles of AOI 4 (23.4 GB) with associated building footprints do the following:

aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/SN2_buildings_train_AOI_4_Shanghai.tar.gz .

AOI 5 – Khartoum – Building Extraction Training

To download processed 200mx200m tiles of AOI 2 (4.7 GB) with associated building footprints do the following:

aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/SN2_buildings_train_AOI_5_Khartoum.tar.gz .

Testing Data

AOI 2 – Vegas – Building Extraction Testing

To download processed 200mx200m tiles of AOI 2 (7.9 GB) for testing do:

aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/AOI_2_Vegas_Test_public.tar.gz .

AOI 3 – Paris – Building Extraction Testing

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

aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/AOI_3_Paris_Test_public.tar.gz .

AOI 4 – Shanghai – Building Extraction Testing

To download processed 200mx200m tiles of AOI 4 (7.7 GB) for testing do:

aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/AOI_4_Shanghai_Test_public.tar.gz .

AOI 5 – Khartoum – Building Extraction Testing

To download processed 200mx200m tiles of AOI 2 (1.6 GB) for testing do:

aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/AOI_5_Khartoum_Test_public.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.

License

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

SpaceNet 1: Building Detection v1

By

SpaceNet 1: Building Detection v1

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 685,000 footprints across the Five SpaceNet Areas of Interest.

AOI Area of Raster (Sq. Km) Building Labels (Polygons)
AOI_1_Rio 2,544 382,534

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/SN1_buildings/

Training Data

AOI 1 – Rio – Building Extraction Training

To download processed 200mx200m tiles of AOI 1 (23 GB) with associated building footprints for training do the following:

aws s3 cp s3://spacenet-dataset/spacenet/SN1_buildings/tarballs/SN1_buildings_train_AOI_1_Rio_3band.tar.gz .

aws s3 cp s3://spacenet-dataset/spacenet/SN1_buildings/tarballs/SN1_buildings_train_AOI_1_Rio_8band.tar.gz .

aws s3 cp s3://spacenet-dataset/spacenet/SN1_buildings/tarballs/SN1_buildings_train_AOI_1_Rio_geojson_buildings.tar.gz .

Testing Data

AOI 1 – Rio – Building Extraction Testing

To download processed 200mx200m tiles of AOI 1 (7.9 GB) for testing do:

aws s3 cp s3://spacenet-dataset/spacenet/SN1_buildings/tarballs/SN1_buildings_test_AOI_1_Rio_3band.tar.gz .

aws s3 cp s3://spacenet-dataset/spacenet/SN1_buildings/tarballs/SN1_buildings_test_AOI_1_Rio_8band.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.

License

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