SkySearch: Satellite Video Search at Scale

Satellite images play a crucial role in weather analytics, and recent advancements in satellite technology have significantly enhanced the accuracy and reliability of weather predictions. In this paper, we introduce SkySearch, a large-scale satellite video search system deployed at the Korean Meteorological Administration (KMA). SkySearch is designed to aid weather experts in making timely and accurate forecasts by rapidly and precisely searching for satellite videos in the database that resemble current weather conditions. SkySearch employs self-supervised learning to compress large volumes of high-resolution satellite videos into low-dimensional embeddings, addressing the absence of labeled satellite data. Within this latent space, relationships between videos are modeled as a graph, enabling efficient searches. Given a query video, SkySearch rapidly identifies a small subset of similar videos by traversing the graph. When the query video represents the current conditions, it can optionally be augmented with predicted future frames to search for videos that reflect both the current conditions and expected evolution. Finally, the ranked list of videos is provided to weather forecasters through a user-friendly interface. We demonstrate that deployment and empirical effectiveness of SkySearch through both numerical and qualitative evaluations. In summary, SkySearch is: (a) Scalable: processes queries from a large-scale database of satellite images spanning over a decade and delivers results within seconds(b) Accurate: returns numerically and qualitatively similar videos to the query video, and (c) Label-free: does not require labeled videos.