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Exploiting Big Earth Data from Space – First Experiences with the TimeScan Processing Chain

Abstract The European Sentinel missions and the latest generation of the United States Landsat satellites provide new opportunities for global environmental monitoring. They acquire imagery at spatial resolutions between 10 and 60 m in a temporal and spatial coverage that could before only be realized on the basis of lower resolution Earth observation data ( 250 m). However, images gathered by these modern missions rapidly add up to data volume that can no longer be handled with standard work stations and software solutions. Hence, this contribution introduces the TimeScan concept which combines pre-existing tools to an exemplary modular pipeline for the flexible and scalable processing of massive image data collections on a variety of (private or public) computing clusters. The TimeScan framework covers solutions for data access to arbitrary mission archives (with different data provisioning policies) and data ingestion into a processing environment (EO2Data module), mission specific pre-processing of multi-temporal data collections (Data2TimeS module), and the generation of a final TimeScan baseline product (TimeS2Stats module) providing a spectrally and temporally harmonized representation of the observed surfaces. Technically, a TimeScan layer aggregates the information content of hundreds or thousands of single images available for the area and time period of interest (i.e. up to hundreds of TBs or even PBs of data) into a higher level product with significantly reduced volume. In first test, the TimeScan pipeline has been used to process a global coverage of 452,799 multispectral Landsat–8 scenes acquired from 2013 to 2015, a global data-set of 25,550 Envisat ASAR radar images collected 2010–2012, and regional Sentinel–1 and Sentinel–2 collections of 1500 images acquired from 2014 to 2016. The resulting TimeScan products have already been successfully used in various studies related to the large-scale monitoring of environmental processes and their temporal dynamics.

Where We Live—A Summary of the Achievements and Planned Evolution of the Global Urban Footprint

The TerraSAR-X (TSX) mission provides a distinguished collection of high resolution satellite images that shows great promise for a global monitoring of human settlements. Hence, the German Aerospace Center (DLR) has developed the Urban Footprint Processor (UFP) that represents an operational framework for the mapping of built-up areas based on a mass processing and analysis of TSX imagery. The UFP includes functionalities for data management, feature extraction, unsupervised classification, mosaicking, and post-editing. Based on >180.000 TSX StripMap scenes, the UFP was used in 2016 to derive a global map of human presence on Earth in a so far unique spatial resolution of 12 m per grid cell: the Global Urban Footprint (GUF). This work provides a comprehensive summary of the major achievements related to the Global Urban Footprint initiative, with dedicated sections focusing on aspects such as UFP methodology, basic product characteristics (specification, accuracy, global figures on urbanization derived from GUF), the user community, and the already initiated future roadmap of follow-on activities and products. The active community of >250 institutions already working with the GUF data documents the relevance and suitability of the GUF initiative and the underlying high-resolution SAR imagery with respect to the provision of key information on the human presence on earth and the global human settlements properties and patterns, respectively.