What does it really take to teach AI to understand our planet?
In this episode, Matt sits down with Isaac Corley, Senior Machine Learning Engineer at Wherobots and maintainer of TorchGeo, and Christopher Ren, Data Scientist and writer behind some of the most thought-provoking essays on Earth observation and AI.
They dive deep into the state of Geospatial AI from embeddings and foundation models to how these tools are reshaping Earth observation and remote sensing. You’ll hear real-world perspectives on what’s working, what’s not, and where the hype outpaces reality.
Key topics covered:
What "embeddings" actually mean for geospatial and remote sensing
How foundation models like AlphaEarth and Tessera are trained
The challenges of applying ML at global scale
The future of Earth observation data pipelines
Why geospatial AI is still the "wild west"
If you’ve ever wondered how AI models are built to map, monitor, and understand the Earth, this conversation breaks it down without the buzzwords.
LINKS:
ISAAC CORLEY
Website: https://isaacc.dev/
LinkedIn: linkedin.com/in/isaaccorley
X: @isaaccorley_
GitHub: @isaaccorley
I’m active in TorchGeo slack: free to join https://join.slack.com/t/torchgeo/shared_invite/zt-22rse667m-eqtCeNW0yI000Tl4B~2PIw
CHRISTOPHER REN
Blog: https://christopherren.substack.com/
Podcast: Spotify | Apple | Youtube
LinkedIn: https://www.linkedin.com/in/christopherren/
Geovibes/Github: https://github.com/cr458/geovibes
🚀 Ready to move beyond desktop GIS?
Step into the Spatial Lab: a global community for ambitious geospatial professionals who want to break out of outdated workflows and join the top 5% of the field.
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AI can answer almost anything, except “where.” That gap is huge, and Kieran McCann from Mapbox is working to close it.
In this episode of The Spatial Stack, Matt sits down with Kieran, who’s spent six years helping Mapbox quietly power some of the most widely used maps and location services in the world. From his early days doing grunt map edits at Apple Maps to shaping Mapbox’s next big bet: AI-native mapping. Kieran shares what it takes to bring true spatial understanding into artificial intelligence.
You’ll hear how Mapbox is experimenting with ways for AI to finally reason about place and direction, what’s still missing for models to “get” geography, and how anyone, even those just starting out, can build powerful AI + geospatial tools today. Kieran also breaks down how showing prototypes can change your career trajectory and why now is the perfect time to jump into this space.
If you’re curious about where mapping and AI collide and what that means for your work, your career, and the apps we all use every day, this conversation is a must-listen.
🚀 Ready to move beyond desktop GIS?
Step into the Spatial Lab: a global community for ambitious geospatial professionals who want to break out of outdated workflows and join the top 5% of the field.
👉 Join Spatial Lab: https://forrest.nyc/spatial-lab/
🎓 Want structured, career-changing learning?
🚀 Modern GIS Accelerator: https://forrest.nyc/accelerator/
— master Python, Spatial SQL & cloud workflows in 2 weeks
🧭 Career Compass: https://forrest.nyc/career-compass/
— fast, practical steps to land the GIS role you want
🪄 AI Copilot for GIS: https://forrest.nyc/ai-copilot-for-gis/
— learn to integrate AI into your geospatial workflows & boost your productivity
📰 Weekly modern GIS insights: https://forrest.nyc
⚡️ Spots for the next live cohort and mentorship cycle are closing soon, join now to lock in your place and momentum.
CONNECT WITH ME
📸 Instagram: https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc
GeoAI isn’t just another buzzword, it’s a turning point for the entire geospatial industry. In this episode of The Spatial Stack, Matt sits down with Tee Barr, Director at Verisk and Councilor for the American Geographical Society, to unpack why GeoAI could reshape the field as profoundly as GPS once did.
From climate risk and supply chain resilience to defense, finance, and human security, Tee explains how AI is already influencing high-stakes decision making and why the upcoming Geography 2050 event is dedicating its theme to this massive shift.
You’ll hear how GeoAI is challenging long-held assumptions about spatial data, the new opportunities and risks it creates, and why ethical responsibility must keep pace with technical innovation. Whether you’re a geospatial professional, business leader, or educator, this conversation will help you understand the stakes and the opportunities in the next era of mapping and analytics.
Register for Geography 2050 here: https://www.geography2050.org/
---
🚀 Ready to move beyond desktop GIS?
Step into the Spatial Lab: a global community for ambitious geospatial professionals who want to break out of outdated workflows and join the top 5% of the field.
👉 Join Spatial Lab: https://forrest.nyc/spatial-lab/
🎓 Want structured, career-changing learning?
🚀 Modern GIS Accelerator: https://forrest.nyc/accelerator/
— master Python, Spatial SQL & cloud workflows in 2 weeks
🧭 Career Compass: https://forrest.nyc/career-compass/
— fast, practical steps to land the GIS role you want
🪄 AI Copilot for GIS: https://forrest.nyc/ai-copilot-for-gis/
— learn to integrate AI into your geospatial workflows & boost your productivity
📰 Weekly modern GIS insights: https://forrest.nyc
⚡️ Spots for the next live cohort and mentorship cycle are closing soon, join now to lock in your place and momentum.
CONNECT WITH ME
📸 Instagram: https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc
What happens when a geospatial professional decides to map the entire business of our industry?
In this episode of Spatial Stack, Matt talks with Wilfred Waters, the creator of the first-ever Geospatial Index, a hand-built list tracking hundreds of public companies shaping maps, data, satellites, sensors, and spatial software. Will shares how he spent countless late nights pulling together every geospatial stock he could find, from Vietnam to Finland to the U.S., and what the process revealed about where the industry is heading.
Along the way, we unpack how indexes work, why understanding the business side of geospatial matters (even if you’re not an investor), and how seeing the market clearly can help you think differently about your own career and the technologies you rely on.
Geospatial FM on Spotify: https://open.spotify.com/show/5gpQUsaWxEBpYCnypEdHFC
On Apple Podcasts: https://podcasts.apple.com/us/podcast/geospatial-fm/id1674368928
Substack: www.geospatial.fm
Index introduction and construction rules: https://www.geospatial.fm/p/compounding
2024 Year In Review: https://www.geospatial.fm/p/2024-geospatial-stocks-performance
⚠️ Nothing in this episode is investment advice. It’s for educational and informational purposes only.
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 broken
How edge computing flips the economics of space data
The “iPhone moment” for satellites and what it means for new applications
Real-world examples of cost savings and new use cases
The industries poised to benefit most — from defense to agriculture to disaster response
This is a conversation about more than satellites. It’s about making Earth observation affordable, democratized, and insight-driven.
NOVI’s Website: www.novispace.ai
NOVI’s LinkedIn: https://www.linkedin.com/company/novispace-inc
NOVI’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/7M08rlY
Website: 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 BI
Why geospatial data is more than just maps — it’s a core analytical dimension
The design philosophy behind Superset: from SQL-first thinking to visual storytelling
Lessons from building geospatial capabilities into dashboards with Deck.GL and H3
The future of BI, and why the open ecosystem is winning
Whether 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 workflows
New features in Airflow 3.0 like assets, remote execution, and backfills
Why orchestration is the key to scalable spatial data processing
Tools like the Airflow AI SDK that make LLM pipelines easier to manage
Links 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.
What happens when a heated LinkedIn debate turns into a real conversation?
In this episode, I sit down with Kyle Satterfield, GISP, after a spirited online exchange about geospatial tools — specifically, the classic open-source versus Esri debate.
What started as a back-and-forth in the comments became a deeper conversation about the choices we make in GIS: buy versus build, custom versus out-of-the-box, IT-driven versus GIS-led approaches, and how the right answer isn’t always as simple as we make it seem online.
Kyle shares candid stories from his work launching GIS programs at the county level, navigating the tug-of-war between IT departments, administrators, and GIS teams. We talk about the value of fast wins, the realities of budgets and buy-in, and why sometimes being "boxed in" by a platform can be a good thing.
This episode isn’t about who’s right — it’s about getting past the hot takes and hearing real perspectives from both sides of the geospatial world.
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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 decades
The hidden costs of geospatial silos
How GeoParquet was born — and what it changes
What it means to make spatial data “just a join away”
The future of cloud-native geospatial
Subscribe for more conversations at the intersection of spatial, data, and innovation.
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💨 Sign up for the Spatial Lab Community ➡️ https://forrest.nyc/spatial-lab ⬅️
What if the way we map the Earth is reinforcing the very inequalities we aim to solve?
In this episode of The Spatial Stack, I sit down with Dr. Nadine Alameh, one of the most influential voices in geospatial and AI for Earth. With a background spanning NASA, the United Nations, and the Open Geospatial Consortium, Nadine brings a unique systems-level perspective to the urgent question: Who gets to define the map?
We dive into:
How AI models can fail without local context and community input
The tensions between global institutions and grassroots data efforts
The importance of open tools, inclusive standards, and rethinking what “ground truth” really means
What it will take to make Earth intelligence equitable, actionable, and trustworthy
Whether you’re building geospatial systems, training machine learning models, or working on climate and land use policy, this conversation will reshape how you think about data, power, and place.
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💨 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.
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In this episode, we dive into one of the biggest challenges holding back GIS professionals today: the technician trap. Sparked by a real-world job posting from one of the world’s richest tech companies—offering just $20–$22 an hour for a GIS contract role—we explore what this means for the broader industry.
Why are highly skilled spatial professionals being underpaid, and what separates a $45K contractor from a $300K geospatial data engineer? The answer lies in how you position yourself—and whether your work is seen as project-based execution or strategic value creation.
We’ll unpack:
Whether you're just entering the GIS field or feeling stuck in your current role, this episode offers a candid look at how to grow your career by aligning technical skills with business impact—and why it's time to expect more from GIS roles.
🎧 Plus: Learn about the Modern GIS Accelerator, a program designed to help you scale your skills and reframe your career path.
📩 Get every update from my newsletter ➡️ https://forrest.nyc ⬅️
💨 Sign up for the Modern GIS Accelerator ➡️ https://forrest.nyc/accelerator ⬅️
Feeling stuck in your GIS career? You're not alone. In this episode, we dive deep into the common challenges GIS professionals face and how to break free from the "technician trap." Discover actionable strategies to transition from a support role to a strategic contributor, adding greater value to your organization.
We'll explore how to shift your mindset from focusing solely on deliverables to emphasizing outcomes. Learn how to identify your technical passions, specialize effectively, and position yourself as a leader in your field. Whether you're aiming to build a standout portfolio, land a promotion, or pivot into a new GIS role, this episode offers practical insights to help you achieve your goals.
Plus, hear real-world examples of how GIS professionals have transformed their careers by applying these principles. Tune in and start unlocking the next level in your GIS journey!
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What if you could stop wrangling geospatial data, and just get to the good part?
In this episode, I sit down with Rob Fletcher, Chief Science Officer at Seer AI, to talk about one of the biggest blockers in geospatial today: accessing and integrating the right data, at the right time, in the right format.
Rob shares his journey from doing particle physics at CERN to pioneering spatial data infrastructure, and how Seer AI is building what he calls a “CDN for geospatial data.”
We dive into:
🌍 Why the biggest challenge in GIS isn’t analysis—it's finding and formatting data
💡 The danger of agreeing to analytics projects without knowing what's possible
🛠️ Why Seer AI chose graph technology to organize spatial data across sources
⚡ How a "side project" dashboard became essential to real-time disaster response
📊 Bridging the gap between GIS teams and business analysts
Whether you're working in local government, disaster response, sustainability, or just tired of managing messy spatial data, this episode will change how you think about building modern GIS workflows.
🔗 Learn more about Seer AI: https://seer.ai
🎧 Subscribe for more episodes on the future of geospatial
In this episode, we sit down with David Price, GIS Project Manager for Wood County, Ohio, to explore the evolving landscape of modern GIS. David’s journey into geospatial wasn’t a traditional one—he started in data analytics before discovering GIS, a shift that completely changed his career trajectory.
We dive into:
🔹 Why GIS education is falling behind and how universities are missing the mark
🔹 The critical skills GIS professionals need today (Hint: It’s more than just mapping)
🔹 The debate: Do you really need to learn to code to stay competitive?
🔹 Open-source vs. Esri: Why a hybrid approach is the future of GIS
🔹 The power of real-time data, automation, and APIs in GIS workflows
David shares his experiences managing an enterprise GIS system, adapting to new geospatial technologies, and why he believes learning SQL and Python can future-proof your GIS career.
If you’re a GIS professional, student, or just someone interested in the future of geospatial technology, this is an episode you don’t want to miss!
Is there a future in GIS? How can you break past the salary ceilings and position yourself for long-term success? In this episode, we dive into the realities of GIS careers, salaries, and strategies to help you grow.
🔹 Why GIS salaries often lag behind similar data roles
🔹 The Technician Trap—how GIS is often seen as a support role rather than a strategic function
🔹 Skills that matter—Python, SQL, and cloud-native GIS for career flexibility
🔹 How to position yourself as a problem-solver, not just an executor
🔹 Tangible steps to accelerate your career without switching jobs
GIS is an exciting field, but to truly thrive, you need to think beyond maps and spatial analysis—you need to be strategic. Tune in to learn how to showcase your skills, position yourself for growth, and maximize your career growth.
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Wildfires are getting more intense, yet many people in Los Angeles had no idea the fire was near them—until they saw the smoke. Despite all our advancements in geospatial technology, disaster response still struggles with timely alerts, real-time data access, and predictive modeling.
In this episode, I sit down with Mina Nada, a remote sensing specialist, to explore what went wrong during the recent LA wildfires and how geospatial tools like satellite imagery, fire modeling, and risk mapping can improve future disaster response. We break down why current wildfire alerts are failing, what GIS professionals can do to fix it, and how earth observation data plays a critical role in mitigating fire disasters.
If you’re in GIS, remote sensing, emergency management, or just curious about the future of fire detection, this conversation is packed with insights you don’t want to miss.
🔥 Topics Covered:
✔️ Why wildfire alerts failed during the LA fires
✔️ How satellite imagery tracks wildfires in real-time
✔️ The role of GIS in predicting and responding to fires
✔️ Challenges in making wildfire data more accessible
✔️ What we need to do to prevent future wildfire disasters
