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Semantic Segmentation of Aerial Images

Published: September 12, 2024

A comparison of UNET versus SegNET for Land Cover Segmentation
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Published in , 2024

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MOOC Cubes and Clouds - Cloud Native Open Data Sciences for Earth Observation

Published in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2024

The “Cubes and Clouds” Massive Open Online Course (MOOC) addresses the growing challenges and opportunities in Earth Observation (EO) data analysis posed by the exponential growth of satellite missions and data volumes. This course introduces cloud-native EO concepts and open science principles, facilitating collaboration and data sharing within the EO community. Key topics covered in the MOOC include data discovery, processing workflows, and data sharing using real-world examples like snow cover mapping in the Alps. Participants engage with interactive lessons, videos, and hands-on coding exercises, leveraging freely available geospatial data and emphasizing open data principles and interoperability. The course infrastructure seamlessly integrates with cloud platforms like the Copernicus Data Space Ecosystem, enabling learners to apply concepts in a practical, cloud-based environment. Initial user statistics indicate strong interest, particularly among early-career professionals and researchers, with participant surveys suggesting increased confidence in using EO cloud platforms and embracing open science practices upon course completion. Overall, “Cubes and Clouds” serves as a valuable resource for the EO community, fostering transparency, collaboration, and innovation in geospatial science. Future developments aim to expand course content and attract diverse user groups, ensuring broader accessibility and long-term impact in advancing cloud-native EO and open science initiatives. Read more

Recommended citation: Zellner, P. J., Claus, M., Dolezalova, T., Balogun, R. O., Eberle, J., Hodam, H., Eckardt, R., Meißl, S., Jacob, A., and Anghelea, A.: MOOC Cubes and Clouds - Cloud Native Open Data Sciences for Earth Observation, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W12-2024, 157–162, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-157-2024, 2024. https://isprs-archives.copernicus.org/articles/XLVIII-4-W12-2024/157/2024/isprs-archives-XLVIII-4-W12-2024-157-2024.pdf