Cubes, Clouds and Hazards#
This is course material for the lecturs on Cubes, Clouds and Hazards which is based on the online course Cubes and Clouds (Zellener et al., 2024) but with main focus on Natural Hazards (Landslides) and Mapping those Hazards.
Description#
The physical lectures on ‘Cubes, Clouds & Hazards ‘ teaches the concepts of data cubes, cloud platforms and open science in the context of earth observation for monitoring Natural Hazards. Here is a great introduction about the concepts of cubes and clouds for earth observation:
Landslides are one of the most destructive natural hazards, causing significant loss of life, property damage, and disruption to infrastructure. Effective monitoring and mapping of landslides are critical to mitigate their impacts, support disaster response, and enable long-term planning in vulnerable areas. Cloud computing has emerged as a transformative tool in this domain, offering unparalleled capabilities for data storage, analysis, and collaboration.
One of the primary advantages of cloud computing is its ability to handle large volumes of data efficiently. Landslide mapping often requires the integration of diverse datasets, including satellite imagery, LiDAR scans, geological surveys, and real-time sensor data. These datasets are vast, often exceeding the storage and processing capabilities of local systems. Cloud platforms provide scalable storage solutions and powerful computing resources, enabling researchers to process high-resolution imagery and analyze terrain models with precision. This capability is crucial for detecting landslide-prone areas and generating detailed risk maps.
Cloud computing also facilitates real-time data analysis, a critical feature for monitoring active landslides or regions prone to rapid changes. Using the cloud, data from remote sensors or drones can be uploaded, processed, and analyzed almost instantaneously. This capability supports early warning systems, allowing authorities to issue timely alerts and take preventive measures to reduce casualties and damage. Additionally, cloud platforms can integrate weather data, such as rainfall intensity and duration, which are key triggers for landslides, further enhancing prediction accuracy.
Collaboration and accessibility are other significant benefits of cloud computing. Landslide mapping requires coordination among geologists, engineers, local governments, and international organizations. Cloud platforms enable seamless sharing of data and models, fostering interdisciplinary collaboration and ensuring that all stakeholders have access to the latest information. Moreover, cloud-based tools are accessible from anywhere, empowering disaster management teams to make informed decisions even in remote or affected areas with limited physical infrastructure.
Another critical aspect is cost-effectiveness. Traditional methods of storing and processing geospatial data require significant investment in hardware, software, and maintenance. Cloud computing eliminates many of these costs by providing on-demand resources, allowing organizations to scale their operations based on current needs. This democratizes access to advanced mapping technologies, enabling even resource-constrained regions to adopt state-of-the-art tools for landslide risk assessment.
Finally, the flexibility of cloud computing supports the integration of machine learning and artificial intelligence (AI) algorithms for landslide detection and prediction. These technologies thrive on large datasets and require substantial computational power—both of which the cloud can provide. AI models trained on historical landslide data can identify patterns and predict future events with remarkable accuracy, offering a proactive approach to disaster management.
Lecturer#
This course will be delevered physically by Ashok Dahal.
Content#
The course explains the concepts of data cubes, EO cloud platforms and their use for natural hazard monitoring and mapping.
Learning Objectives#
After finishing the participant will understand the theoretical concepts of cloud native EO processing and have gained practical experience by conducting an end-to-end EO workflow. The participant will be capable of independently using cloud platforms to approach EO related research questions and be confident in how to share research by adhering to the concepts of open science.
License#
Where not stated explicitly otherwise this work is licensed under a Creative Commons Attribution 4.0 International License.
Acknowledgement#
This course material is based on the work of Zellener et al (2024).
Zellner, P. J., Dolezalova, T., Claus, M., Eberle, J., Balogun, R. O., Meißl, S., Eckardt, R., Hodam, H., Jacob, A., & Anghelea, A. (2024). Cubes & Clouds - Cloud Native Open Data Sciences for Earth Observation (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10869466