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.
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
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.
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.