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

An Aligned Multi-Temporal Multi-Resolution Satellite Image Dataset for Change Detection Research