In this episode of Scene from Above, Julia Wagemann speaks with Matthias Mohr, independent software developer and one of the key contributors to the STAC (SpatioTemporal Asset Catalog) and STAC API specifications.

STAC has become foundational to how Earth observation data is discovered and accessed across cloud platforms. But its origins lie in a fragmented landscape of portals, inconsistent metadata, and incompatible APIs.

Matthias shares how STAC emerged from practical needs within the community and how it evolved into a widely adopted standard for geospatial data discovery.

Together, Julia and Matthias unpack:

• Why STAC was created and what problem it solved
• The difference between static STAC catalogues and STAC APIs
• How organisations struggle when adopting STAC internally
• The role of extensions and interoperability
• Where cloud-native geospatial infrastructure may head next

A thoughtful conversation for anyone working with large-scale Earth observation data, from analysts querying data, to engineers publishing catalogues, to decision-makers shaping data infrastructure.

Host: Julia Wagemann

Guest: Matthias Mohr

In this episode of Scene from Above, Julia Wagemann speaks with Aimee Barciauskas, Data Engineer at Development Seed, about what it actually takes to make Earth observation data usable at scale.

For the past seven years, Aimee has worked closely with NASA’s IMPACT team, supporting the agency’s transition to cloud-native workflows. From initiatives like the Multi-Mission Algorithm and Analysis Platform (MAAP) to community-driven tooling and documentation, her work sits at the intersection of data engineering, open science, and real-world impact.

Together, Julia and Aimee unpack:

• What “cloud-friendly” really means (beyond storing data in the cloud)

• The technical and cultural barriers teams still face

• Why reproducibility is as much about habits as it is about tools

• How frictionless workflows could shape the future of Earth science

A practical conversation for anyone working with large-scale geospatial data and thinking about how to move from experimentation to repeatable, shareable workflows.

Host: Julia Wagemann

Guest: Aimee Barciauskas (Development Seed)

In this episode, we speak with Swetha Kolluri, who works on AI and Digital Innovations at the World Bank. Previously serving as Head of Experimentation at UNDP in India, Swetha has dedicated her career to applying frontier technologies to social and environmental challenges. With her background spanning rural development, data science, and sustainability initiatives across India and the US, she brings unique perspectives on transformative solutions using AI and cloud computing.

In this conversation, Swetha shares insights from her work designing innovative development experiments, and her vision for how earth observation, citizen science and AI can address global challenges such as environmental degradation and climate change. She also shares some tips managing cloud costs for organisations who may have low resource bases.

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Sign-up for thriveGEO's Cloud-Native Geospatial 101 training and use the exclusive discount code CNGPODCAST - https://bit.ly/geospatial_cloud

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More about Swetha:

• LinkedIn - https://www.linkedin.com/in/swetha-kolluri/

Projects that Swetha mentioned in the episode:

GeoAI for brick kilns in India. This digital innovation applied AI to detect 48,287 brick kilns from satellite data with an accuracy of 96%, empowering environmental regulators in India to expedite climate action.

https://geo-ai.undp.org.in/

DiCRA (Data in Climate Resilient Agriculture) is an accredited digital public good (DPG) that has democratized satellite data for informing climate finance in agriculture. This innovation provided data and intelligence on climate resilience across 50 million hectares of agriculture land across 7 states in India.

https://github.com/undpindia/dicra

‘VAYU – OpenAir’ with open software, open algorithms and open data collected on air pollution produced through hyperlocal mapping of air pollution in two cities - Patna and Gurgaon. This dataset is used to map point sources of air pollution and enable cities take action on specific sources.

https://github.com/undpindia/VAYU_OpenAir

More about thriveGEO - https://thrivegeo.com/