This paper presents an aligned multi-temporal and multi-resolution satellite image dataset for research in change detection. We expect our dataset to be useful to researchers who want to fuse information from multiple satellites for detecting changes on the surface of the earth that may not be fully visible in any single satellite. The dataset we present was created by augmenting the SpaceNet-7 dataset with temporally parallel stacks of Landsat and Sentinel images. The SpaceNet-7 dataset consists of time-sequenced Planet images recorded over 101 AOIs (Areas-of-Interest). In our dataset, for each of the 60 AOIs that are meant for training, we augment the Planet datacube with temporally parallel datacubes of Landsat and Sentinel images. The temporal alignments between the high-res Planet images, on the one hand, and the Landsat and Sentinel images, on the other, are approximate since the temporal resolution for the Planet images is one month -- each image being a mosaic of the best data collected over a month. Whenever we have a choice regarding which Landsat and Sentinel images to pair up with the Planet images, we have chosen those that had the least cloud cover. A particularly important feature of our dataset is that the high-res and the low-res images are spatially aligned together with our MuRA framework presented in this paper. Foundational to the alignment calculation is the modeling of inter-satellite misalignment errors with polynomials as in NASA's AROP algorithm. We have named our dataset MuRA-T for the MuRA framework that is used for aligning the cross-satellite images and "T" for the temporal dimension in the dataset.

翻译：本文提出了一种用于变化检测研究的对齐多时相多分辨率卫星图像数据集。我们期望该数据集能对希望融合多源卫星信息以检测地球表面变化（这些变化在单一卫星图像中可能无法完全显现）的研究人员有所帮助。该数据集通过使用时间并行的Landsat与Sentinel图像堆栈对SpaceNet-7数据集进行增广而创建。SpaceNet-7数据集包含覆盖101个兴趣区（AOI）的时间序列Planet图像。在我们的数据集中，针对60个用于训练的AOI，我们使用Landsat和Sentinel图像的时间并行数据立方体对Planet数据立方体进行增广。高分辨率Planet图像与Landsat及Sentinel图像之间的时间对齐是近似性的，因为Planet图像的时间分辨率为一个月——每张图像均为一个月内采集的最佳数据镶嵌而成。当需要选择与Planet图像配对的Landsat和Sentinel图像时，我们优先选取云覆盖最少的图像。该数据集的一个关键特征在于，高分辨率与低分辨率图像已通过本文提出的MuRA框架实现空间对齐。该对齐计算的基础是采用类似于NASA的AROP算法中多项式方法对星间配准误差进行建模。我们将该数据集命名为MuRA-T，其中"MuRA"代表用于跨卫星图像对齐的框架，"T"代表数据集的时相维度。