Earth observation (EO) scientists use sophisticated algorithms, large datasets, and high-performance data analytics (HPDA) clusters to develop and execute complex EO workflows. To improve portability and reusability within the domain, the Open Geospatial Consortium (OGC) published a set of best practices for developing EO workflows. Even though EO data products are often shared openly, representative EO workflow implementations are hardly available.In this paper, we contribute a case study on implementing and scaling a complex EO workflow, adhering to the OGC best practices and demonstrating its portability by deploying it both locally and on the HPDA platform "terrabyte" at the Leibniz Supercomputing Centre. Contentwise, the contributed workflow analyzes settlement patterns by first delineating coherent settlements derived from leveraged satellite data that maps builtup areas and subsequently calculating centrality measures to characterize the spatial arrangement and hierarchy of settlements in a given region.We demonstrate the workflow’s scalability and variance in resource demands by analyzing its time-to-result, total CPU time, and resource efficiency under different inputs and configurations. Interestingly, implicit parameters hidden in the input data semantics, like the number and area of settlements in the region of interest, significantly impact the time required to complete the processing. Concurrent processing is restricted to connected components of the settlement graph in the analysis stage, leading to an unbalanced workload distribution when analyzing large urban areas, showcasing the scalability challenges EO scientists face.

The processing of large amounts of data in central high performance data analytics (HPDA) systems is playing an increasingly important role in science and business. However, many HPDA systems exhibit a low utilization of their available resources during normal operation. An important reason for this underutilization is that too many resources are reserved for individual jobs. This is often a consequence of the common practice of reserving a uniform amount of resources such as CPU or memory for the entire execution time of a job. Given that many data intensive (DI) jobs consist of different phases with different resource demands, resources are normally reserved according to the demand of the most resource-intensive phase. This results in more resources being reserved over a long period of time than are actually needed.

Recent events in conflict zones around the world show the importance for humanitarian operations to have access to reliable information. Aid agencies, in particular, are confronted with a multitude of information sources that are not necessarily structured or tailored to their needs.The presented study uses the conflict in Ukraine to illustrate the development of user-adapted geospatial information products. They require (1) the collection of satellite, web and other freely available geospatial data from different sources, and (2) the extraction of relevant situational information using AI analysis to combine it in a meaningful way so that (3) demand-driven information products are available in an interactive situation display for humanitarian aid activities. The analyses focus on identifying and qualifying changes in settlement areas, on detecting damage on buildings and infrastructure, on quantifying the number of people affected and on how to visualize the information for humanitarian aid purposes.