Charting the Future of Earth Observation 2024

Charting the Future of Earth Observation: Technology Innovation for Climate Intelligence20Endnotes 1. NOAA National Centers for Environmental Information (NCEI). (2024). U.S. Billion-Dollar Weather and Climate Disasters. https://www.ncei.noaa.gov/access/billions/. 2. World Meteorological Organization (WMO). (2020). The value of Surface-Based Meteorological Observation Data: Costs and benefits of the Global Basic Observing Network. https://library.wmo.int/records/item/57167-the-value-of-surface-based- meteorological-observation-data-costs-and-benefits-of-the-global-basic-observing-network?offset=681. 3. Analysys Mason. (2023). The implications of increasing Earth observation data. https://www.analysysmason.com/research/ content/articles/the-implications-of-increasing-earth-observation-data/. 4. World Economic Forum. (2023). Amplifying the Global Value of Earth Observation. https://www3.weforum.org/docs/WEF_ Amplifying_the_Global_Value_of_Earth_Observation_2024.pdf. 5. Albedo. (2022). A First Look at 10cm Satellite Imagery. https://albedo.com/post/albedo-simulated-imagery. 6. Simera Sense. (2024). Simera Sense, world leader in Earth observation optical imaging solutions, raises €13.5m in growth investment round. https://www.simera-sense.com/capital-raise/. 7. NASA Landsat Science. (2024). Landsat Next: Mission Objectives. https://landsat.gsfc.nasa.gov/satellites/landsat-next/mission-details/. 8. NASA Earthdata. (2024). FIRMS Frequently Asked Questions. https://www.earthdata.nasa.gov/faq/firms-faq. 9. Robinson, C., R. Gupta, S. F. Nsutezo, E. Pound, et al. (2023). Turkey Building Damage Assessment. Microsoft Research. https://www.microsoft.com/en-us/research/uploads/prod/2023/02/Turkey-Earthquake-Report-2_MS.pdf. 10. NEMAforyou. (May 4, 2023). Emerging Earth Observation Technologies and Their Impacts on the Future of Disaster Response. YouTube. https://www.youtube.com/watch?v=YfkyMU6x9qk&t=583s&ab_channel=NEMAforyou. 11. Canadian Space Agency. (2024). WildFireSat: Enhancing Canada’s ability to manage wildfires. Government of Canada. https://www.asc-csa.gc.ca/eng/satellites/wildfiresat/. 12. World Economic Forum. (2024). Innovation and Adaptation in the Climate Crisis: Technology for the New Normal. https://www.weforum.org/publications/innovation-and-adaptation-in-the-climate-crisis-technology-for-the-new-normal/. 13. Davenport, T. H. and R. Bean. (2024). Five Key Trends in AI and Data Science for 2024. MIT Sloan Management Review. https://sloanreview.mit.edu/article/five-key-trends-in-ai-and-data-science-for-2024/. 14. Heikkilä, M. and W. D. Heaven. (2024). What’s next for AI in 2024. MIT Technology Review. https://www.technologyreview. com/2024/01/04/1086046/whats-next-for-ai-in-2024/. 15. Lütjens, B., B. Leshchinskiy, C. Requena-Mesa, F. Chishtie, et al. (2020). Physics-informed GANs for Coastal Flood Visualization. https://www.researchgate.net/publication/344734339_Physics-informed_GANs_for_Coastal_Flood_Visualization. 16. Bodnar, C., W. P . Bruinsma, A. Lucic, M. Stanley, et al. (2024). Aurora: A Foundation Model of the Atmosphere. https://arxiv.org/abs/2405.13063. 17. Koehl, D. (2024). Prithvi-weather-climate: Advancing Our Understanding of the Atmosphere. NASA Earthdata. https://www.earthdata.nasa.gov/learn/blog/prithvi-weather-climate-foundation-model-background-benefits. 18. Nemni, E., J. Bullock, S. Belabbes and L. Bromley. (2020). Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery. Remote Sensing, vol. 12, issue 16, no. 2532. https://www.mdpi.com/2072-4292/12/16/2532. 19. Yu, H., Z. Luo, L. Wang, X. Ding and S. Wang. (2023). Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data. Remote Sensing, vol.15, issue 14, no. 3601. https://www.mdpi.com/2072-4292/15/14/3601. 20. Maddy, E. S. and S. A. Boukabara. (2021). MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 8606-8616. https://doi.org/10.1109/JSTARS.2021.3104389. 21. CRUE ERA-Net. (2008). Effectiveness and Efficiency of Early Warning Systems for Flash-Floods (EWASE). https://www.academia. edu/31684129/Effectiveness_and_Efficiency_of_Early_Warning_Systems_for_Flash_Floods_EWASE. 22. Rogers, D. and V. Tsirkunov. (2024). Global assessment report on disaster risk reduction: costs and benefits of early warning systems. World Bank Group. https://documents.worldbank.org/en/publication/documents-reports/ documentdetail/609951468330279598/global-assessment-report-on-disaster-risk-reduction-costs-and-benefits-of-early- warning-systems. 23. Hallegatte, S., C. Greeb, R. J. Nicholls and J. Corfee-Morlot. (2013). Future flood losses in major coastal cities. Nature Climate Change, vol. 3, pp. 802-806. http://dx.doi.org/10.1038/NCLIMATE1979. 24. Hinkel, J., D. Lincke, A. T. Vafeidis, M. Perrette, et al. (2014). Coastal flood damage and adaptation costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences of the United States of America, vol. 111, issue 9, pp. 3292-3297. http://dx.doi.org/10.1073/pnas.1222469111. 25. European Space Agency (ESA). (2024). A milestone in digital Earth modelling. https://www.esa.int/Applications/Observing_the_ Earth/A_milestone_in_digital_Earth_modelling. 26. Güneralp, B., İ. Güneralp and Y. Liu. (2015). Changing global patterns of urban exposure to flood and drought hazards. Global Environmental Change, vol. 31, pp. 217-225. https://doi.org/10.1016/j.gloenvcha.2015.01.002. 27. Nearing, G., D. Cohen, V. Dube, M. Gauch, et al. (2024). Global prediction of extreme floods in ungauged watersheds. Nature, vol. 627, pp. 559–563. https://doi.org/10.1038/s41586-024-07145-1.