language: en
license: unknown
task_categories:
- change-detection
paperswithcode_id: oscd
pretty_name: OSCD MSI
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- change-detection
- sentinel-2
dataset_info:
features:
- name: image1
dtype:
array3_d:
dtype: uint16
shape:
- 13
- 10000
- 10000
- name: image2
dtype:
array3_d:
dtype: uint16
shape:
- 13
- 10000
- 10000
- name: mask
dtype: image
splits:
- name: train
num_bytes: 365977532
num_examples: 14
- name: test
num_bytes: 172930908
num_examples: 10
download_size: 397786113
dataset_size: 538908440
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
OSCD MSI
The Onera Satellite Change Detection (OSCD) dataset is a change detection dataset that focuses on multispectral (MS) images taken by the ESA Sentinel-2 satellite. This dataset provides 24 registered image pairs from various continents, captured between the years 2015 and 2018. Along with the image pairs, the dataset includes binary change masks that highlight areas where changes have occurred. Pixel-level change ground truth is provided for all 14 training and 10 test image pairs. The annotated changes focus on urban changes, such as new buildings or new roads. These data can be used for training and setting parameters of change detection algorithms.
- Paper: https://arxiv.org/abs/1810.08468
- Homepage: https://github.com/phelber/OSCD
Description
The dataset is available in two versions: RGB only and all 13 MSI Bands (this repo) Multispectral (MS) Sentinel-2 bands.
- Total Number of Images: 24
- Bands: 13 (MSI)
- Image Size: 10000x10000
- Image Resolution: 10m
- Land Cover Classes: 2
- Classes: no change, change
- Source: Sentinel-2
Usage
To use this dataset, simply use datasets.load_dataset("blanchon/OSCD_MSI").
from datasets import load_dataset
OSCD_MSI = load_dataset("blanchon/OSCD_MSI")
Citation
If you use the EuroSAT dataset in your research, please consider citing the following publication:
@article{daudt2018urban,
title = {Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks},
author = {R. C. Daudt and B. L. Saux and Alexandre Boulch and Y. Gousseau},
journal = {IEEE International Geoscience and Remote Sensing Symposium},
year = {2018},
doi = {10.1109/IGARSS.2018.8518015},
bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/18755de13d2d868255bbf332fa80ec218163c74e}
}