DIUx xView 2018 Detection Challenge

1. Object Classes

Here are the parent and child denominations for all 60 object classes in xView. Parent classes are at the headings of each column. The only exception is the last column 'None', which corresponds to classes that have no parent.

Fixed-Wing Aircraft
Small Aircraft
Cargo Plane

Passenger Vehicle
Small Car
Bus

Building
Hut/Tent
Shed
Aircraft Hangar
Damaged Building
Facility

Truck
Pickup Truck
Utility Truck
Cargo Truck
Truck w/Box
Truck Tractor Trailer
Truck w/Flatbed
Truck w/Liquid

Railway Vehicle
Passenger Car
Cargo Car
Flat Car
Tank Car
Locomotive

Maritime Vessel
Motoboat
Sailboat
Tugboat
Barge
Fishing Vessel
Ferry
Yacht
Container Ship
Oil Tanker

Engineering Vessel
Tower Crane
Container Crane
Reach Stacker
Straddle Carrier
Mobile Crane
Dump Truck
Haul Truck
Scraper/Tractor
Front Loader
Excavator
Cement Mixer
Ground Grader
Crane Truck

None
Helipad
Pylon
Shipping Container
Shipping Container Lot
Storage Tank
Vehicle Lot
Construction Site
Tower Structure
Helicopter

2. Examples

Example input, just image/pixel data:

Example output, with bounding boxes rendered visually:

3. Publication

Abstract

xView is a new large-scale dataset for the advancement of object detection techniques and overhead object detection research. This satellite imagery dataset enables research progress pertaining to four key computer vision frontiers. We utilize a novel process for geospatial category detection and bounding box annotation with three stages of quality control. Our data is collected from WorldView-3 satellites at 0.3m ground sample distance, providing higher resolution imagery than most public satellite imagery datasets. We compare xView to other object detection datasets in both natural and overhead imagery domains and then provide a baseline analysis using the Single Shot MultiBox Detector. xView is one of the largest and most diverse publicly available object detection datasets to date, with over 1 million objects across 60 classes in over 1,400 km2 of imagery.

The xVIEW dataset has been fully described in a paper which is freely available via arXiv.org: We encourage you to download and read the paper for more details.

Link to paper (arxiv.org)

Full satellite images gathered from DigitalGlobe’s WorldView 3 (WV3) satellites span a total area of over 1,400 square kilometers at 0.3 meter ground sample distance (GSD) resolution. The dataset was prepared in ways that are typical for satellite imagery, including ortho-rectification, pan-sharpening, RGB dynamic range adjustment, and atmospheric correction. xView contains more than 1 million labeled instances across 60 object categories. The fields in the geoJSON files include:

Field Name	Description
TYPE_ID	The bounding box label class ID
CAT_ID	DigitalGlobe's unique ID for image strips
IMAGE_ID	The image chip filename on which a feature is marked
BOUNDS_IMCOORDS	Bounding box in pixel coordinates [xmin, ymin, xmax, ymax] of the image chip in which it is marked
COORDINATES	Coordinates in longitude-latitude form for bounding box points

The GeoTIFF files are broken into two sets: RGB and 8-band. The 8-band set contains 8-band multispectral GeoTIFF images labeled as image_id.tif where image_id is a unique integer. Similarly, the RGB set containes pan-sharpened 3-band RGB GeoTIFF images with the same naming convention.

The dataset was segmented into three sets, training, validation, and test, which have roughly 60, 20, and 20 percent of the images respectively. Restrictions were placed on the splits so that each set had enough instances of each category to perform a fair analysis. As with many challenges, all participants will have access to the train and validation images, but during the challenge, labels are provided for only the train dataset. The private test set will be used to evaluate final submissions after the Challenge deadline.

ZIP and TGZ files contain identical data, the only difference is the compression software used to create the archive. The size of correponding ZIP/TGZ files is approximately equal, choose the one that is more convenient for you. Please do not waste bandwidth downloading both.

Click the link (or "Save as...") to start downloading:

ZIP files

Download Training Images (zip)
SHA1: dce357ce3e7fff6ee28107e9211b793054979166

Download Training Labels (zip)
SHA1: 1897556cdc8cb24f7b137988c488768ed7d461b7

Download Validation Images (zip)
SHA1: 42c44878c4003c9411617ca05bf537aab41f420c

TGZ files (gzipped tarball)

Download Training Images (tgz)
SHA1: 1cdd5af68ec3a696f696104efb4730928cde524e

Download Training Labels (tgz)
SHA1: 693abfe34beabcb1bb87242241e0d0e273c36f0c

Download Validation Images (tgz)
SHA1: 1e030f1b2b35cdb8deb408a6d227a6713c32f7ba

Verify file integrity

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