Spatial Stack with Matt Forrest

What if satellites became as smart and as accessible as your smartphone?In this episode, I sit down with Michael Bartholomeusz, CEO of NOVI to unpack how Earth observation is being transformed by edge computing in space. We explore the shift from sending terabytes of raw imagery to delivering actionable insights, and why this change could take the cost of analysis from $150,000 down to under $1,000.Michael shares stories from his 25-year career, from working on the space shuttle that launched John Glenn, to leading NOVI’s dual-use strategy for defense and commercial markets. Along the way, we dive into:Why the current Earth observation model is brokenHow edge computing flips the economics of space dataThe “iPhone moment” for satellites and what it means for new applicationsReal-world examples of cost savings and new use casesThe industries poised to benefit most — from defense to agriculture to disaster responseThis is a conversation about more than satellites. It’s about making Earth observation affordable, democratized, and insight-driven.NOVI’s Website: www.novispace.aiNOVI’s LinkedIn: https://www.linkedin.com/company/novispace-incNOVI’s GENIE™ Constellation Press Release: https://www.newswire.com/news/novi-unveils-genie-tm-a-platform-and-satellite-constellation-that-22622479

In this episode, I sit down with Dr. Qiusheng Wu, associate professor, open-source advocate, and creator of some of the most widely used tools in modern geospatial, including Leafmap and GeoAI. If you’ve ever watched his YouTube tutorials or come across his projects on GitHub, you already know the impact he’s had on the community.We dive into:His newly released Python for Geospatial book, one of the most complete guides in the field.The ongoing debate of Python vs. SQL in modern workflows—and how to combine them effectively.The rise of embeddings and geospatial foundation models (including Google’s AlphaEarth) and what they mean for GIS.The future of open-source education and why anyone, with patience and time, can learn and contribute.How Dr. Wu balances teaching, tool-building, publishing, and pushing the boundaries of what’s possible.This is a wide-ranging conversation about technology, education, and the future of spatial data. If you care about where geospatial is headed—and how to keep up—you won’t want to miss it.👉 Links Dr. Wu's Book on Geospatial Python: https://a.co/d/7M08rlYWebsite: https://wetlands.io/Dr. Wu on YouTube:  @giswqs  LinkedIn: https://www.linkedin.com/in/giswqs/

Open data is supposed to unlock innovation, but what happens when “open” isn’t really open?In this episode of The Spatial Stack, I sit down with return guest Mina Nada to explore the world of open data licenses — from Creative Commons to Open Database Licenses and beyond. We break down what these licenses actually allow you to do, where the pitfalls are, and how they impact anyone building geospatial products, tools, or services.Along the way, Mina shares real-world experiences navigating licensing challenges, from NASA’s permissive CC0 approach to the more restrictive “share-alike” models. Whether you’re a developer, analyst, or entrepreneur, this conversation will help you understand how to work with open data without running into legal or business roadblocks.If you’ve ever wondered what “open data” really means for your projects, this is the episode to listen to.The future is spatial — so let’s get into it.

What happens when a hackathon project becomes one of the most influential open-source BI tools in the world?In this episode, we sit down with Max Beauchemin — creator of Apache Superset and Apache Airflow — and Evan Rusackas from Preset, the company behind Superset’s enterprise evolution.We explore:The origin story of Superset at Airbnb — and why Max says it became his “life’s mission”How open-source BI tools are outpacing traditional vendors like Tableau and Power BIWhy geospatial data is more than just maps — it’s a core analytical dimensionThe design philosophy behind Superset: from SQL-first thinking to visual storytellingLessons from building geospatial capabilities into dashboards with Deck.GL and H3The future of BI, and why the open ecosystem is winningWhether you're a data engineer, GIS pro, or just curious how modern BI is evolving, this episode is packed with insights from two people who are building that future.📍 Guest Links:Apache Superset: https://superset.apache.org/Preset: https://preset.io/🎧 Tune in now and hear how Superset is redefining what modern, open, and geospatial-aware BI looks like.

In this episode, I open up about something many of us in GIS rarely talk about — imposter syndrome. That nagging feeling of not being good enough, of questioning whether you really belong in this field, even when the evidence says otherwise.This isn’t a polished success story, it’s an honest conversation about doubt, comparison, and the pressure to measure up in GIS. I share my own experiences, what I’ve learned along the way, and why so many professionals quietly wrestle with the same thoughts.If you’ve ever wondered “Am I really cut out for this?” — you’re not alone. Let’s talk about it.

If you’re working with spatial data, AI workflows, or massive batch jobs, you’ve probably hacked together more than a few pipelines. But what if there’s a better way?In this episode, I sit down with Kenten Danas, Senior Manager of Developer Relations at ‪@Astronomer‬ to explore how Apache Airflow powers the modern data stack including real-world geospatial and climate risk modeling pipelines.We cover:What Airflow actually is (and why it’s everywhere)How it’s used in geospatial pipelines, AI, and LLM workflowsNew features in Airflow 3.0 like assets, remote execution, and backfillsWhy orchestration is the key to scalable spatial data processingTools like the Airflow AI SDK that make LLM pipelines easier to manageLinks from the show:Astronomer Academy (with courses + certifications): https://academy.astronomer.io/Astronomer Webinars: https://www.astronomer.io/events/webi...Astro CLI (for running Airflow locally): https://www.astronomer.io/docs/astro/...Free trial of Astro: https://www.astronomer.io/lp/signup/Airflow AI SDK (open source Python SDK for working with LLMs from Airflow): https://github.com/astronomer/airflow...Vibrant Planet Geospatial + ML Airflow use case: https://www.astronomer.io/blog/airflo...Whether you're building spatial features for machine learning or just want a more reliable way to manage your data workflows this is the episode for you.

In this discussion, Kyle Satterfield, a GISP with a wealth of experience in launching GIS programs, shares insights from a lively LinkedIn debate on Esri versus open-source solutions. He highlights the complexities of choosing the right tools, balancing IT needs with GIS user preferences. Listener-friendly designs versus sophisticated features emerge as key themes. Kyle recounts challenges faced in local government GIS implementation and emphasizes the benefits of established platforms. This conversation transcends the usual arguments, offering real-world perspectives on navigating the GIS landscape.

📩 Get every update from my newsletter ➡️ https://forrest.nyc ⬅️💨 Sign up for the Spatial Lab Community ➡️ https://forrest.nyc/spatial-lab ⬅️Why is geospatial data still stuck in its own world — and what will it take to finally integrate it with the rest of the data ecosystem?In this episode, I talk with Javier de la Torre, founder of Carto and one of the original architects of modern spatial analytics. We explore the history of GIS as a siloed discipline, the cost that isolation has had on the industry (and professionals), and how open formats like GeoParquet are finally changing the game.Javier shares his journey from biodiversity research to building Carto, insights from working with major cloud platforms, and the behind-the-scenes story of how geospatial data became a first-class citizen in the analytics world.If you've ever wrestled with incompatible file formats, siloed teams, or the slow pace of innovation in GIS — this conversation will feel like a breath of fresh air.We cover:Why GIS has been isolated for decadesThe hidden costs of geospatial silosHow GeoParquet was born — and what it changesWhat it means to make spatial data “just a join away”The future of cloud-native geospatialSubscribe for more conversations at the intersection of spatial, data, and innovation.

Dr. Nadine Alameh, a leading voice in geospatial and AI with experience at NASA and the UN, dives into the complexities of mapping equity. She discusses how AI models can fail without local insights, revealing the tension between global institutions and grassroots efforts. Alameh emphasizes the significance of open tools and inclusive standards, challenging traditional notions of 'ground truth'. This enlightening conversation reshapes our understanding of data's role in power dynamics and the urgent need for equitable Earth intelligence.

📩 Get every update from my newsletter ➡️ https://forrest.nyc ⬅️💨 Sign up for the Spatial Lab Community ➡️ https://forrest.nyc/spatial-lab ⬅️What if the future of AI depends on understanding where, not just what? In this episode, I’m joined by Justus Killian, partner at Space Capital, one of the only VC firms focused entirely on geospatial and space-based tech. We dive into why geospatial data is no longer niche, how satellites became billion-dollar businesses, and why the next wave of AI breakthroughs could come from those who understand location. We also unpack the evolving venture landscape, the rise of geo-embeddings, and how spatial data is finally moving beyond the map into real-time decision-making.Whether you're building LLMs, funding the next Planet Labs, or just curious about how Earth is being mapped in ways you’ve never seen, this one's for you.