8-7-2026 –, HoC Lila Taal: English
Efficient and reproducible geospatial workflows require systematic performance monitoring and benchmarking. This hands-on tutorial introduces Geobench, an open-source framework designed to monitor and benchmark geospatial workflows across different tools and computing environments (https://github.com/ITC-CRIB/geobench). You will learn how to design benchmarking scenarios, execute workflows with automated monitoring, and interpret performance metrics to improve workflow efficiency.
Geospatial analyses often involve complex processing chains implemented using different tools such as Python scripts, QGIS processes, command-line utilities, or Jupyter notebooks. Evaluating the performance of these workflows can be difficult, especially when multiple parameters, datasets, or computing environments must be compared.
This short workshop introduces Geobench, an open-source benchmarking framework developed to simplify performance evaluation of geospatial workflows. Geobench provides a structured approach for defining experiments, executing workflows, and collecting detailed performance metrics at both system and process levels.
The workshop will cover:
- Installing and running Geobench from the source repository,
- Defining benchmarking scenarios using YAML configuration files,
- Running experiments with multiple parameter combinations,
- Monitoring workflow execution and system resources,
- Collecting and interpreting benchmarking outputs (JSON and HTML reports), and
- Comparing alternative workflows or parameter settings.
You will work through practical examples demonstrating how Geobench can be used to benchmark typical geospatial tasks implemented in Python scripts or QGIS processes. The session will also discuss how benchmarking results can support reproducible research and transparent reporting in scientific publications.
By the end of the workshop, you will be able to design their own benchmarking experiments and apply them to your geospatial workflows.
Serkan Girgin is an Associate Professor at the Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, and leads the Center of Expertise in Big Geodata Science (CRIB), a facility advancing geospatial big data and cloud computing technologies. His research focuses on scalable, cloud-native geospatial data access and processing, with particular attention to performance and energy efficiency. He develops tools and platforms that support Open Science and best practices in research data management and research software development. He has led the design and development of platforms such as Open Data Explorer and OpenSTAC, enabling efficient discovery and access of large-scale research data. His leads collaborative projects, including CLOUD-NES and ECO-SCALE. He had also developed methodologies and systems for industrial risk assessment, including the European Commission's eNatech database and the RAPID-N system. He serves on the ESA DestinE Sounding Board NL, is an eScience Center Fellow, and a SURF Research Support Champion.