OSCD - Onera Satellite Change Detection

The DatasetThe Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates.It comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018. Locations are picked all over the world, in Brazil, USA, Europe, Middle-East and Asia. For each location, registered pairs of 13-band multispectral satellite images obtained by the Sentinel-2 satellites are provided. Images vary in spatial resolution between 10m, 20m and 60m.Pixel-level change ground truth is provided for 14 of the 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. The BenchmarkThe algorithms can be tested in a benchmark for change detection.The ground truth for the 10 remaining images remain undisclosed. Change prediction maps can be uploaded for evaluation on the IEEE GRSS DASE website. Various metrics such as per-class accuracy and confusion matrices are automatically computed on the website, and are available for participants. Comparison to the best performing methods is provided in the leaderboard associated with this benchmark.ReferencesIf you use this work for your projects, please take the time to cite our paper:Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks, R. Caye Daudt, B. Le Saux, A. Boulch, Y. Gousseau. IEEE International Geoscience and Remote Sensing Symposium (IGARSS’2018). Valencia, Spain. July 2018.[PDF] [BibTeX] The TeamRodrigo Caye Daudt, [e-mail] Le Saux, [e-mail] Boulch, [e-mail] Gousseau, [e-mail]

数据集
Onera卫星变化检测数据集旨在解决不同日期卫星图像间的变化检测问题。它包含2015至2018年间由哨兵2号卫星（Sentinel-2 satellites）拍摄的24对多光谱图像，拍摄地点遍布全球，涵盖巴西、美国、欧洲、中东及亚洲。针对每个地点，数据集提供经配准的13波段多光谱卫星图像对，其空间分辨率介于10米、20米与60米之间。其中14对图像提供像素级变化真值（ground truth），标注变化聚焦于城市场景（如新建建筑或道路），可用于变化检测算法的训练与参数调优。

基准测试
该基准测试可验证变化检测算法性能。剩余10对图像的真值未公开，参与者可将变化预测图上传至IEEE GRSS DASE网站评估。网站自动计算每类准确率、混淆矩阵等指标并提供给参与者，同时在关联排行榜中展示与最优方法的对比结果。

参考文献
若您在研究中使用本成果，请引用以下论文：《基于卷积神经网络（Convolutional Neural Networks）的多光谱对地观测城市变化检测》（Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks），作者R. Caye Daudt、B. Le Saux、A. Boulch、Y. Gousseau，发表于2018年7月西班牙瓦伦西亚举办的IEEE国际地球科学与遥感研讨会（IGARSS’2018）。[PDF] [BibTeX]

团队成员
Rodrigo Caye Daudt，邮箱：[e-mail]
Bertrand Le Saux，邮箱：[e-mail]
Alexandre Boulch，邮箱：[e-mail]
Yann Gousseau，邮箱：[e-mail]

搜集汇总

背景与挑战

背景概述

OSCD数据集是一个用于卫星图像变化检测的开源数据集，包含24对来自Sentinel-2卫星的多光谱图像，覆盖全球多个地区。其中14对图像提供了像素级的城市变化标注，如新建筑或道路，适合用于训练和测试变化检测算法。