xView 2018 Object Detection Challenge: Dataset

From: 

http://xviewdataset.org/

https://challenge.xviewdataset.org/

目录

1. Object Classes

2 Format

3 Download

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 Format

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.

3 Download

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train

链接：https://pan.baidu.com/s/1r3jlzIK8uQ-VqfVr4CEgDA 提取码：7jpo

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