In this episode, I talk with Steve Brumby, co-founder, CEO and CTO of Impact Observatory, about sustainability and environmental risk analysis. Impact Observatory uses satellite imagery and machine learning to empower decision-makers with planetary insights.

Highlights:

• Using machine learning to generate thematic maps to represent land cover and land use.
• Geospatial data from the European Space Agency’s Copernicus program that is available on a variety of platforms.
• The importance of identifying the relevant output for end users and others in the value chain.
• How machine learning engineers sometimes discover things used by remote sensing scientists that are no longer necessary.
• Keeping models simple.
• Mitigating bias in models by using large and globally diverse datasets.
• Get to know your customer and their pain points, then craft a machine learning solution that works for them – if you’re lucky, it’ll also work for others.
• Finding the things you’re passionate about – both the technology and helping the customers in that space.

Links:

• Impact Observatory's website
• Steve Brumby on LinkedIn
• K. Karra, C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. Mathis and S. P. Brumby, "Global land use / land cover with Sentinel 2 and deep learning," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 4704-4707, doi: 10.1109/IGARSS47720.2021.9553499
• Brown, C.F., Brumby, S.P., Guzder-Williams, B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data 9, 251 (2022). https://doi.org/10.1038/s41597-022-01307-4
• Impact Observatory’s data
  • https://www.arcgis.com/home/item.html?id=d6642f8a4f6d4685a24ae2dc0c73d4ac
  • https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class
• UN Biodiversity Lab: country dashboard for Australia

Resources for Computer Vision Teams:
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