xView Dataset
The xView dataset is one of the largest publicly available datasets of overhead imagery, containing images from complex scenes around the world annotated using bounding boxes. The goal of the xView dataset is to accelerate progress in four computer vision frontiers:
- Reduce minimum resolution for detection.
- Improve learning efficiency.
- Enable discovery of more object classes.
- Improve detection of fine-grained classes.
xView builds on the success of challenges like Common Objects in Context (COCO) and aims to leverage computer vision to analyze the growing amount of available imagery from space in order to understand the visual world in new ways and address a range of important applications.
Key Features
- xView contains over 1 million object instances across 60 classes.
- The dataset has a resolution of 0.3 meters, providing higher resolution imagery than most public satellite imagery datasets.
- xView features a diverse collection of small, rare, fine-grained, and multi-type objects with bounding box annotation.
- Comes with a pre-trained baseline model using the TensorFlow object detection API and an example for PyTorch.
Dataset Structure
The xView dataset is composed of satellite images collected from WorldView-3 satellites at a 0.3m ground sample distance. It contains over 1 million objects across 60 classes in over 1,400 km² of imagery.
Applications
The xView dataset is widely used for training and evaluating deep learning models for object detection in overhead imagery. The dataset's diverse set of object classes and high-resolution imagery make it a valuable resource for researchers and practitioners in the field of computer vision, especially for satellite imagery analysis.
Dataset YAML
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the xView dataset, the xView.yaml file is maintained at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/xView.yaml.
ultralytics/cfg/datasets/xView.yaml
# Ultralytics YOLO 🚀, AGPL-3.0 license
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
# Documentation: https://docs.ultralytics.com/datasets/detect/xview/
# Example usage: yolo train data=xView.yaml
# parent
# ├── ultralytics
# └── datasets
# └── xView ← downloads here (20.7 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/xView # dataset root dir
train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
# Classes
names:
0: Fixed-wing Aircraft
1: Small Aircraft
2: Cargo Plane
3: Helicopter
4: Passenger Vehicle
5: Small Car
6: Bus
7: Pickup Truck
8: Utility Truck
9: Truck
10: Cargo Truck
11: Truck w/Box
12: Truck Tractor
13: Trailer
14: Truck w/Flatbed
15: Truck w/Liquid
16: Crane Truck
17: Railway Vehicle
18: Passenger Car
19: Cargo Car
20: Flat Car
21: Tank car
22: Locomotive
23: Maritime Vessel
24: Motorboat
25: Sailboat
26: Tugboat
27: Barge
28: Fishing Vessel
29: Ferry
30: Yacht
31: Container Ship
32: Oil Tanker
33: Engineering Vehicle
34: Tower crane
35: Container Crane
36: Reach Stacker
37: Straddle Carrier
38: Mobile Crane
39: Dump Truck
40: Haul Truck
41: Scraper/Tractor
42: Front loader/Bulldozer
43: Excavator
44: Cement Mixer
45: Ground Grader
46: Hut/Tent
47: Shed
48: Building
49: Aircraft Hangar
50: Damaged Building
51: Facility
52: Construction Site
53: Vehicle Lot
54: Helipad
55: Storage Tank
56: Shipping container lot
57: Shipping Container
58: Pylon
59: Tower
# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
import json
import os
from pathlib import Path
import numpy as np
from PIL import Image
from tqdm import tqdm
from ultralytics.data.utils import autosplit
from ultralytics.utils.ops import xyxy2xywhn
def convert_labels(fname=Path('xView/xView_train.geojson')):
# Convert xView geoJSON labels to YOLO format
path = fname.parent
with open(fname) as f:
print(f'Loading {fname}...')
data = json.load(f)
# Make dirs
labels = Path(path / 'labels' / 'train')
os.system(f'rm -rf {labels}')
labels.mkdir(parents=True, exist_ok=True)
# xView classes 11-94 to 0-59
xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
shapes = {}
for feature in tqdm(data['features'], desc=f'Converting {fname}'):
p = feature['properties']
if p['bounds_imcoords']:
id = p['image_id']
file = path / 'train_images' / id
if file.exists(): # 1395.tif missing
try:
box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
cls = p['type_id']
cls = xview_class2index[int(cls)] # xView class to 0-60
assert 59 >= cls >= 0, f'incorrect class index {cls}'
# Write YOLO label
if id not in shapes:
shapes[id] = Image.open(file).size
box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
with open((labels / id).with_suffix('.txt'), 'a') as f:
f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt
except Exception as e:
print(f'WARNING: skipping one label for {file}: {e}')
# Download manually from https://challenge.xviewdataset.org
dir = Path(yaml['path']) # dataset root dir
# urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels
# 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images
# 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels)
# download(urls, dir=dir)
# Convert labels
convert_labels(dir / 'xView_train.geojson')
# Move images
images = Path(dir / 'images')
images.mkdir(parents=True, exist_ok=True)
Path(dir / 'train_images').rename(dir / 'images' / 'train')
Path(dir / 'val_images').rename(dir / 'images' / 'val')
# Split
autosplit(dir / 'images' / 'train')
Usage
To train a model on the xView dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.
Train Example
Sample Data and Annotations
The xView dataset contains high-resolution satellite images with a diverse set of objects annotated using bounding boxes. Here are some examples of data from the dataset, along with their corresponding annotations:
- Overhead Imagery: This image demonstrates an example of object detection in overhead imagery, where objects are annotated with bounding boxes. The dataset provides high-resolution satellite images to facilitate the development of models for this task.
The example showcases the variety and complexity of the data in the xView dataset and highlights the importance of high-quality satellite imagery for object detection tasks.
Citations and Acknowledgments
If you use the xView dataset in your research or development work, please cite the following paper:
@misc{lam2018xview,
title={xView: Objects in Context in Overhead Imagery},
author={Darius Lam and Richard Kuzma and Kevin McGee and Samuel Dooley and Michael Laielli and Matthew Klaric and Yaroslav Bulatov and Brendan McCord},
year={2018},
eprint={1802.07856},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
We would like to acknowledge the Defense Innovation Unit (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the xView dataset website.