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Remote sensing imagery, despite its broad applications in helping achieveSustainable Development Goals and tackle climate change, has not yet benefitedfrom the recent advancements of versatile, task-agnostic vision language models(VLMs). A key reason is that the large-scale, semantically diverse image-textdataset required for developing VLMs is still absent for remote sensing images.Unlike natural images, remote sensing images and their associated textdescriptions cannot be efficiently collected from the public Internet at scale.In this work, we bridge this gap by using geo-coordinates to automaticallyconnect open, unlabeled remote sensing images with rich semantics covered inOpenStreetMap, and thus construct SkyScript, a comprehensive vision-languagedataset for remote sensing images, comprising 2.6 million image-text pairscovering 29K distinct semantic tags. With continual pre-training on thisdataset, we obtain a VLM that surpasses baseline models with a 6.2% averageaccuracy gain in zero-shot scene classification across seven benchmarkdatasets. It also demonstrates the ability of zero-shot transfer forfine-grained object attribute classification and cross-modal retrieval. We hopethis dataset can support the advancement of VLMs for various multi-modal tasksin remote sensing, such as open-vocabulary classification, retrieval,captioning, and text-to-image synthesis.

SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing