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Multi-Temporal Urban Development Challenge

SN7: Multi-Temporal Urban Development Challenge

The Problem

Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 232 United Nations Sustainable Development Goals, but the World Bank estimates that more than 100 countries currently lack effective Civil Registration systems. The SpaceNet 7 Multi-Temporal Urban Development Challenge aims to help address this deficit and develop novel computer vision methods for non-video time series data. In this challenge, participants will identify and track buildings in satellite imagery time series collected over rapidly urbanizing areas. The competition centers around a new open source dataset of Planet satellite imagery mosaics, which includes 24 images (one per month) covering ~100 unique geographies. The dataset will comprise over 40,000 square kilometers of imagery and exhaustive polygon labels of building footprints in the imagery, totaling over 10 million individual annotations. Challenge participants will be asked to track building construction over time, thereby directly assessing urbanization.

This Challenge has broad implications for disaster preparedness, the environment, infrastructure development, and epidemic prevention. Beyond the humanitarian applications, this competition poses a unique challenge from a computer vision standpoint because of the small pixel area of each object, the high object density within images, and the dramatic image-to-image difference compared to frame-to-frame variation in video object tracking. We believe this challenge will aid efforts to develop useful tools for overhead change detection and object tracking.

The SpaceNet 7 Challenge will be featured as a competition at the 2020 NeurIPS conference in December, where winning results will also be analyzed.

The Data – ~100 locations, spread out across the globe

Category Value
Num AOIs 101
Num Observations 2389
Num Buildings 11,080,000
Total Observed Area (km2) 41,000
Mean Buildings per Observation 4,700
Mean Building Area (m2) 190
Mean GSD (m) 4.0

Catalog

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

To explore the dataset:

aws s3 ls s3://spacenet-dataset/spacenet/SN7_buildings/ 

A sample is available at:

aws s3 cp s3://spacenet-dataset/spacenet/SN6_buildings/tarballs/SN7_buildings_train.tar.gz . 

Training Data

To download ~4km x 4km imagery data cubes with associated buildings footprint labels:

aws s3 cp s3://spacenet-dataset/spacenet/SN7_buildings/tarballs/SN7_buildings_train.tar.gz . 
aws s3 cp s3://spacenet-dataset/spacenet/SN7_buildings/tarballs/SN7_buildings_train_csvs.tar.gz . 

Testing Data

aws s3 cp s3://spacenet-dataset/spacenet/SN7_buildings/tarballs/SN7_buildings_test_public.tar.gz . 

License

Multi-Sensor All-Weather Mapping

SN6: Multi-Sensor All-Weather Mapping

The Problem

Synthetic Aperture Radar (SAR) is a unique form of radar that can penetrate clouds, collect during all- weather conditions, and capture data day and night. Overhead collects from SAR satellites could be particularly valuable in the quest to aid disaster response in instances where weather and cloud cover can obstruct traditional electro-optical sensors. However, despite these advantages, there is limited open data available to researchers to explore the effectiveness of SAR for such applications, particularly at ultra-high resolutions.

The task of SpaceNet 6 was to automatically extract building footprints with computer vision and artificial intelligence (AI) algorithms using a combination of SAR and electro-optical imagery datasets. This openly-licensed dataset features a unique combination of half-meter Synthetic Aperture Radar (SAR) imagery from Capella Space and half-meter electro-optical (EO) imagery from Maxar’s WorldView 2 satellite. The area of interest for the challenge was centered over the largest port in Europe: Rotterdam, the Netherlands. This area features thousands of buildings, vehicles, and boats of various sizes, to make an effective test bed for SAR and the fusion of these two types of data.

In this challenge, the training dataset contained both SAR and EO imagery, however, the testing and scoring datasets contained only SAR data. Consequently, the EO data could be used for pre-processing the SAR data in some fashion, such as colorization, domain adaptation, or image translation, but cannot be used to directly map buildings. The dataset was structured to mimic real-world scenarios where historical EO data may be available, but concurrent EO collection with SAR is often not possible due to inconsistent orbits of the sensors, or cloud cover that will render the EO data unusable.

AWS GPU Credits

The first 30 registrants with a submission equaling or exceeding the set performance threshold received a credit for 10 hours on a p3.2xlarge for training and improving their models.

The Data – Over 120 sq km of both high resolution synthetic aperture radar (SAR) data and electro optical (EO) imagery with ~48,000 building footprint labels of Rotterdam, The Netherlands

AOI Area of Raster (Sq. Km) Building Footprint Labels (Km)
AOI_11_Rotterdam 120 48,000

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/SN6_buildings/

Training Data

AOI 11 – Rotterdam – Buildings

To download processed ~450mx450m tiles with associated buildings footprint labels of AOI 11 Rotterdam (39.0 GB):

aws s3 cp s3://spacenet-dataset/spacenet/SN6_buildings/tarballs/SN6_buildings_AOI_11_Rotterdam_train.tar.gz . 

Testing Data

AOI 11 – Rotterdam – Buildings

To download processed 450mx450m tiles of AOI 11 Rotterdam (16.9 GB):

aws s3 cp s3://spacenet-dataset/spacenet/SN6_buildings/tarballs/SN6_buildings_AOI_11_Rotterdam_test_public.tar.gz .

Expanded Dataset

In September 2020, the SpaceNet partners released an expanded version of the SpaceNet 6 dataset. The dataset is untiled and distributed in its maximum extent to enable research using combinations of SAR and optical imagery. All of the SAR data comes from Capella Space’s X-band quad-pol sensor mounted on an aircraft. We distribute 202 SAR image strips in two formats: one with minimal pre-processing (Single Look Complex) as well as a second set of new six-band georeferenced products that include 4 channels of intensity and 2 channels derived from a Pauli decomposition. The decomposition channels show different types of scattering behavior. Many of these strips overlap to create a dense stack of SAR data with multiple revisits spanning a three-day time period in August 2019. The extent of these image strips covers a large portion of Rotterdam and 120 km² of total area, with each strip spanning approximately 0.7 km by 10 km.

Complimentary to our SAR data, we also release our untiled Maxar WorldView 2 image spanning ~92 km² at 0.5m spatial resolution. We distribute 4 image products including the panchromatic band, pan-sharpened RGB and RGBNIR data (0.5m) and RGBNIR data (2.0m). As in the challenge, we hold back the optical data over the final testing area but distribute these optical data for validation and training.

Visit our blog for more information on the SpaceNet 6 Expanded Dataset.

AOI 11 – Rotterdam – Buildings

To explore the full expanded dataset in AOI 11 Rotterdam:

aws s3 ls s3://spacenet-dataset/AOIs/AOI_11_Rotterdam/ 

The Metric

In the SpaceNet Multi-Sensor All-Weather Mapping Challenge, the metric for ranking entries was 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 F1-Score is calculated by taking the total True Positives, False Positives, and False Negatives for the total number of building footprints present in the testing datasets.

Citation Instructions

Shermeyer, J., Hogan, D., Brown, J., Etten, A.V., Weir, N., Pacifici, F., Hänsch, R., Bastidas, A., Soenen, S., Bacastow, T.M., & Lewis, R. (2020). SpaceNet 6: Multi-Sensor All Weather Mapping Dataset. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 768-777.

License

Automated Road Network Extraction and Route Travel Time Estimation from Satellite Imagery

SN5: Automated Road Network Extraction and Route Travel Time Estimation from Satellite Imagery

The Problem

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 Data – Over 8,100km of Road Network Labels Across Four New SpaceNet AOIs

AOI Area of Raster (Sq. Km) Road Network Labels (Km)
AOI_7_Moscow 1,353 3,066
AOI_8_Mumbai 1,021 1,951
AOI_9_San Juan 285 1,139
AOI_10_Mystery City ??? ???

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/SN5_roads/
In building off of the results from SpaceNet 3, this challenge will use a modified version of the Average Path Length Similarity (APLS) metric that is tuned to optimize travel times between nodes of interest. CosmiQ Works’ blog, The DownLinQ, provides additional information including an APLS metric overview and detailed description of SpaceNet 5 motivation and structure.

Training Data

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.

Post-challenge release

Citation Instructions

The SpaceNet Partners, “SpaceNet5: Automated Road Network Extraction and Route Travel Time Estimation from Satellite Imagery”, https://spacenet.ai/sn5-challenge/

License

SpaceNet 4: Off-Nadir Buildings

SpaceNet 4: Off-Nadir Buildings

The Problem

Can you help us automate mapping from off-nadir imagery? In this challenge, competitors were tasked with finding automated methods for extracting map-ready building footprints from high-resolution satellite imagery from high off-nadir imagery. In many disaster scenarios the first post-event imagery is from a more off-nadir image than is used in standard mapping use cases. The ability to use higher off-nadir imagery will allow for more flexibility in acquiring and using satellite imagery after a disaster. Moving towards more accurate fully automated extraction of building footprints will help bring innovation to computer vision methodologies applied to high-resolution satellite imagery, and ultimately help create better maps where they are needed most.

The main purpose of this challenge was to extract building footprints from increasingly off-nadir satellite images. The created polygons were compared to ground truth, and the quality of the solutions were measured using the SpaceNet metric.

Read more about the Challenge winners from our blog!

The Data – Over 120,000 Building footprints over 665 sq km of Atlanta, GA with 27 associated WV-2 images.

This dataset contains 27 8-Band WorldView-2 images taken over Atlanta, GA on December 22nd, 2009. They range in off-nadir angle from 7 degrees to 54 degrees.

For the competition, the 27 images are broken into 3 segments based on their off-nadir angle:

  • Nadir: 0-25 degrees
  • Off-nadir: 26 degrees – 40 degrees
  • Very Off-nadir 40-55 degrees

The entire set of images was then tiled into 450m x 450m tiles.

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

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/SN4_buildings/

Sample Data

2 Samples from each Off-Nadir Image – Off-Nadir Imagery Samples

To download processed 450mx450m tiles of AOI_6_Atlanta (728.8 MB) with associated building footprints:

aws s3 cp s3://spacenet-dataset/spacenet/SN4_buildings/tarballs/summaryData.tar.gz .

Training Data

SpaceNet Off-Nadir Training Base Directory:

aws s3 ls s3://spacenet-dataset/spacenet/SN4_buildings/tarballs/train/

SpaceNet Off-Nadir Building Footprint Extraction Training Data Labels (15 mb)

aws s3 cp s3://spacenet-dataset/spacenet/SN4_buildings/tarballs/train/geojson.tar.gz .

SpaceNet Off-Nadir Building Footprint Extraction Training Data Imagery (186 GB)

To download processed 450mx450m tiles of AOI 6 Atlanta.

Each of the 27 Collects forms a separate .tar.gz labeled “Atlanta_nadir{nadir-angle}_catid_{catid}.tar.gz”. Each .tar.gz is ~7 GB

aws s3 cp s3://spacenet-dataset/spacenet/SN4_buildings/tarballs/train/ . --exclude "*geojson.tar.gz" --recursive

Testing Data

AOI 6 Atlanta – Building Footprint Extraction Testing Data

To download processed 450mx450m tiles of AOI 6 Atlanta (5.8 GB):

aws s3 cp s3://spacenet-dataset/spacenet/SN4_buildings/tarballs/SN4_buildings_AOI_6_Atlanta_test_public.tar.gz .

The Metric

In the SpaceNet Off-Nadir Building Extraction 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

F1-Score is calculated by taking the total True Positives, False Positives, and False Negatives for each nadir segment and then averaging the F1-Score for each segment.

F1-Score Total = mean(F1-Score-Nadir, F1-Score-Off-Nadir, F1-Score-Very-Off-Nadir)

Collection Details

Catalog ID Pan Resolution (m) Off Nadir Angle (deg) Target Azimuth (deg) Category
1 1030010003D22F00 0.48 7.8 118.4 Nadir
2 10300100023BC100 0.49 8.3 78.4 Nadir
3 103001000399300 0.49 10.5 148.6 Nadir
4 1030010003CAF100 0.48 10.6 57.6 Nadir
5 1030010002B7D800 0.49 13.9 162 Nadir
6 10300100039AB000 0.49 14.8 43 Nadir
7 1030010002649200 0.52 16.9 168.7 Nadir
8 1030010003C92000 0.52 19.3 35.1 Nadir
9 1030010003127500 0.54 21.3 174.7 Nadir
10 103001000352C200 0.54 23.5 30.7 Nadir
11 103001000307D800 0.57 25.4 178.4 Nadir
12 1030010003472200 0.58 27.4 27.7 Off-Nadir
13 1030010003315300 0.61 29.1 181 Off-Nadir
14 10300100036D5200 0.62 31 25.5 Off-Nadir
15 103001000392F600 0.65 32.5 182.8 Off-Nadir
16 1030010003697400 0.68 34 23.8 Off-Nadir
17 1030010003895500 0.74 37 22.6 Off-Nadir
18 1030010003832800 0.8 39.6 21.5 Off-Nadir
19 10300100035D1B00 0.87 42 20.7 Very Off-Nadir
20 1030010003CCD700 0.95 44.2 20 Very Off-Nadir
21 1030010003713C00 1.03 46.1 19.5 Very Off-Nadir
22 10300100033C5200 1.13 47.8 19 Very Off-Nadir
23 1030010003492700 1.23 49.3 18.5 Very Off-Nadir
24 10300100039E6200 1.36 50.9 18 Very Off-Nadir
25 1030010003BDDC00 1.48 52.2 17.7 Very Off-Nadir
26 1030010003CD4300 1.63 53.4 17.4 Very Off-Nadir
27 1030010003193D00 1.67 54 17.4 Very Off-Nadir

Citation Instructions

Weir, N., Lindenbaum, D., Bastidas, A., Etten, A.V., McPherson, S., Shermeyer, J., Vijay, V.K., & Tang, H. (2019). SpaceNet MVOI: A Multi-View Overhead Imagery Dataset. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 992-1001.

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SpaceNet 3: Road Network Detection

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.

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

SpaceNet 2: Building Detection v2

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

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