SpaceNet: Accelerating geospatial machine learning SpaceNet Buildings Dataset V1 | SpaceNet on AWS
IARPA CORE3D Public Data Urban 3D Challenge Dataset fMoW Dataset MVS Dataset SpaceNet Off-Nadir Dataset SpaceNet Buildings Dataset V1 SpaceNet Buildings Dataset V2 SpaceNet Imagery SpaceNet POI Dataset SpaceNet Roads Dataset

The SpaceNet Buildings Dataset

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_1_Rio 2,544 382,534
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


The data is hosted on AWS in a requester pays bucket.

aws s3 ls s3://spacenet-dataset/SpaceNet_Buildings_Dataset_Round1/ 

The Metric

In the SpaceNet Roads Challenge, the metric for ranking entries is the SpaceNet Metric.
This metric is an F1-Score based on the intersection over union of two building footprints with a threshold of 0.5

For more information read the full article written by Patrick Hagerty at the DownlinQ.

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 cp s3://spacenet-dataset/spacenet_TrainData/3band.tar.gz .
aws cp s3://spacenet-dataset/spacenet_TrainData/8band.tar.gz .

Test Data

AOI 1 - Rio - Building Extraction Testing

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

aws cp s3://spacenet-dataset/spacenet_TestData/3band.tar.gz .
aws cp s3://spacenet-dataset/spacenet_TestData/8band.tar.gz .

Competition Updates:

For more details about the SpaceNet Building Extraction Challenge: Round 1 visit it’s website

Also check out the Round 2 Competition For more details about the SpaceNet Building Extraction Challenge: Round 2 visit it’s website

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


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