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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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