SpaceNet: Accelerating geospatial machine learning SpaceNet Off-Nadir Dataset | 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 Off-Nadir Dataset

The Problem

Can you help us automate mapping from off-nadir imagery? In this challenge, competitors are 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.

Your task will be to extract building footprints from increasingly off-nadir satellite images. The polygon’s you create will be compared to ground truth, and the quality of the solutions will be measured using the SpaceNet metric.

The Data - Over 120,000 Building footprints over 665 sqkm 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:

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 IDPan ResolutionOff Nadir AngleTarget AzimuthCatgory
11030010003D22F000.487.8118.4Nadir
210300100023BC1000.498.378.4Nadir
310300100039930.4910.5148.6Nadir
41030010003CAF1000.4810.657.6Nadir
51030010002B7D8000.4913.9162Nadir
610300100039AB0000.4914.843Nadir
710300100026492000.5216.9168.7Nadir
81030010003C920000.5219.335.1Nadir
910300100031275000.5421.3174.7Nadir
10103001000352C2000.5423.530.7Nadir
11103001000307D8000.5725.4178.4Nadir
1210300100034722000.5827.427.7Off-Nadir
1310300100033153000.6129.1181Off-Nadir
1410300100036D52000.623125.5Off-Nadir
15103001000392F6000.6532.5182.8Off-Nadir
1610300100036974000.683423.8Off-Nadir
1710300100038955000.743722.6Off-Nadir
1810300100038328000.839.621.5Off-Nadir
1910300100035D1B000.874220.7Very Off-Nadir
201030010003CCD7000.9544.220Very Off-Nadir
211030010003713C001.0346.119.5Very Off-Nadir
2210300100033C52001.1347.819Very Off-Nadir
2310300100034927001.2349.318.5Very Off-Nadir
2410300100039E62001.3650.918Very Off-Nadir
251030010003BDDC001.4852.217.7Very Off-Nadir
261030010003CD43001.6353.417.4Very Off-Nadir
271030010003193D001.675417.4Very Off-Nadir

Catalog

The data is hosted on AWS as Public Dataset. An AWS account is required.

aws s3 ls s3://spacenet-dataset/SpaceNet_Off-Nadir_Competition/

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_Off-Nadir_Dataset/SpaceNet-Off-Nadir_Sample/SpaceNet-Off-Nadir_Sample.tar.gz

Training Data

SpaceNet Off-Nadir Training Base Directory:

aws s3 ls s3://spacenet-dataset/SpaceNet_Off-Nadir_Dataset/SpaceNet-Off-Nadir_Train/

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

aws s3 cp s3://spacenet-dataset/SpaceNet_Off-Nadir_Dataset/SpaceNet-Off-Nadir_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_Off-Nadir_Dataset/SpaceNet-Off-Nadir_Train/ . --exclude "*geojson.tar.gz" --recursive

Test 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_Off-Nadir_Dataset/SpaceNet-Off-Nadir_Test/SpaceNet-Off-Nadir_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 segement and then averaging the F1-Score for each segement.

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

Competition Updates:

For information about the currently running challenge visit the competition site on topcoder.

For more details about previous SpaceNet Building Challenges SpaceNet Building Extraction Challenge: Round 2 visit it’s website

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

License

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