One of the challenges as a researcher is to provide the context in current events. A coastal city floods, a reservoir runs dry, a new dam is created, a river finds a new path, part of a country experiences a drought. For each of these events, relevant geospatial datasets exist. Collecting, processing and visualizing these datasets requires experience, time and resources. These are often not available in the context of current events. What if we could make these EO data available to help journalism, citizen science, and researchers to quickly evaluate current events? Here we show a new tool to create and share interactive video map stories. The tool allows to easily turn multitemporal EO or reanalysis data into a set of georeferenced and tiled videos which can be used to better communicate Earth’s changes in journalism, social media, or to support research. The tool allows concurrent generation of video map stories by multiple users via a map-based interface which can then be easily shared. The open-source app makes use of Video Map tools (built on Google Earth Engine API) to expose multitemporal geospatial datasets as a zoomable playing video story, which can be enriched by markers, and put into the context of a storytelling layout. The story can be shared using a unique url on social media. We find that providing video maps is an effective way to put events into context. We will present several examples of video maps. Several applications optical images (Landsat and Sentinel-2), and surface-water change detection show the context of a day zero event in South Africa. Moving populations based on the night lights of the VIIRS show challenges in the Ethiopian, Sudanese, Eritrean border after the dam was constructed. Ocean and coastal currents derived from the Global Tide and Surge Model and HYCOM reanalysis allow to reconstruct the currents and reconstruct the final days of a ship lost at sea. Video maps based on the TROPOMI mission allow to zoom in and look at the sources of atmospheric pollution (NO2). By making these stories available at the fingertips of journalists, citizens and scientists we hope to help provide more detailed and accurate context for the many challenges that our world is currently facing. @en

The rapid increase in volume, resolution, and frequency of EO datasets poses the need for new scalable, cloud-based, tools for interactive exploration, visualization, and analysis. The size of data increases in several dimensions concurrently: temporal resolution go from the order of several days to several hours (CubeSats, reanalysis), spatial resolution increases from the order of hundreds of meters to order of meters (newer satellites, downscaled indicators), the spatial coverage increases from selected areas of interest to global and the timespan increases from years to decades (satellites, reanalysis). Many datasets (optical satellite images, reanalysis of climate and coastal and ocean currents) provide a new look at the world, in details never seen before. We want to make these datasets visible, show the results, preferably without reducing dimensions and giving up all the newly achieved efforts of gathering the most detailed, most rich datasets ever created. This does leave us with a technical challenge. How can we create interactive animations of PB of geospatial data? Here we show a new toolset and tiling scheme that helps to scale up interactive visualizations of the largest global datasets in a form of georeferenced video maps. Interactive visualisation of sea level and currents (based on the datasets from MEASUReS, HYCOM, GTSM) provides an example of a visualisation covering several decades of sea-level rise measurements and model reanalysis. We present a way to efficiently encode data in video and reuse it in the client-side, to visualize or to perform GPU-enabled analysis. An interactive visualization of Earth’s changes captured by Sentinel and Landsat satellites and turned into video map tiles provides an example of how to disseminate large volume datasets while still being able to run state of the art algorithms on the client-side to detect land-use changes, using libraries such as TensorFlow.js or directly using computational shaders. The tools build on Google Earth Engine as a source of multi-temporal EO and reanalysis data. We show how the tools are integrated into mapping libraries (Mapbox GL, Leaflet). The storage format is based on a standardized layout of tiles (TMS). The client-side and client synchronization of video tiles are based on the emerging efforts of the w3c webtiming group.@en