8-7-2026 –, HoC Patina Taal: English
Background: OpenRiverCam is a free and open-source ecosystem for measuring water levels, velocities and discharge using image-based methods. The main input is a short video of the water surface. Videos are geographically registered, preprocessed, orthorectified, analyzed for velocities, and combined with cross-section information to yield water levels and flows. It converts pixel distances into meters, extracts moving details, uses cross correlation to estimate velocities, and combines pre-measured bathymetric surveys to yield water levels and discharge. For a very clear example, check out the front page of https://openrivercam.org. The first two software products created in this ecosystem provide fully operational, autonomous and automated measurements for applications such as irrigation, real-time hydropower control, flood early warning and infrastructure design:
OpenRiverCam Operating System (ORC-OS): runs on a SBC in the field and operationally collects and processes videos. Analyses can be sent to the cloud if the user wants this. ORC-OS is equipped with a user-friendly web interface for configuration, live monitoring, and direct access to data and videos stored on the device.
LiveOpenRiverCam (LiveORC): a REST API receiving videos from ORC-OS devices, centrally storing these and serving these out as raw downloads or to applications for e.g. decision support.
The cool thing for this workshop: all ORC applications are built on a Python API with command-line interface called pyOpenRiverCam (pyORC). PyORC makes available all methods through API including image stabilization, pre-processing, orthorectification, velocimetry analysis, discharge estimation and intuitive plotting. It also offers workflows for command-line processing of videos into water levels, velocities, and discharge. Moreover, if image registration control points are geographical, all outputs are geographical as well! Data can be exported to QGIS compatible meshes, and visualized, and trace simulations and combinations with other geographical data can be made.
In this 90-minute workshop we will install pyORC and process one or two drone videos into velocity products that can be ingested in QGIS. We will go through the entire process up to the point where you see data in QGIS. Bring your own laptop and make sure QGIS and Python are pre-installed! Linux or Windows? Both is fine!
Hessel is trained as a hydrologist. He has gained about 20 years of experience with flood and drought risk modelling and understanding, as well as flood and drought monitoring and forecasting. Much of his work at Deltares focused on global to local flood risk management for national institutes, clients in International Finance such as the World Bank, and the insurance sector. He has authored and co-authored about 80 papers. His approach to development of technology and projects is strongly anchored in co-design. Over the past 5 years, his focus has been on research and development of locally sustainable technology and methods for monitoring related to natural hazard risks. He creates a bridge between local users and entrepreneurs with skills to operationally collect, use and operationalize data, and local and international audiences that require such data, such as National Hydrometeorological Service Providers, water-related consultancies, International Financing Institutes and Earth Observation centres. Within Rainbow Sensing, as a self-trained developer, he conceived and developed the idea for OpenRiverCam, the first entirely free and open-source software ecosystem for operational management of river monitoring sites using camera observations. Within Delft University and IHE Delft, he supervises students that work on affordable or repurposed technology for environmental monitoring. He works for and/or collaborates with internationally recognized entities such as Deltares, Rijkswaterstaat, The Netherlands Enterprise Agency, the World Bank, Humanitarian OpenStreetMap Team and GroundTruth SA.