What happens when you give an AI the ability to see and understand the physical world? In this episode, Matt Forrest sits down with Yael Maguire, GM and VP of Google Maps Platform and Google Earth, to unpack the massive platform shift happening at the intersection of Artificial Intelligence and geospatial technology.

Yael pulls back the curtain on how Google is transforming its massive corpus of 280 billion Street View, aerial, and satellite images into a searchable, interactive database. Discover how developers, urban planners, and creatives can now ask quantitative questions about physical infrastructure, monitor disaster response in real-time, and even generate hyper-realistic, location-grounded videos using tools like Nano Banana and Veo.

Whether you're building digital twins, tracking climate impact, or revolutionizing the advertising and film industries, this conversation reveals the exact tools Google is rolling out to help you build the future.

In this episode, we cover:

- How "Ask Maps" is changing consumer and enterprise search.
- Using AI to instantly audit city infrastructure like power lines, hydrants, and potholes.
- Grounding generative AI models (Nano Banana and Veo) in actual Street View imagery.
- Google’s partnerships for real-time disaster response using satellite AI.
- The launch of Google Earth AI and what it means for developers.

LEARN MORE

Full Announcement: https://mapsplatform.google.com/resources/blog/three-new-ways-to-build-with-real-world-imagery
More Google Next Announcements: https://mapsplatform.google.com/resources

00:00 - Intro
01:20 - Meet Yael Maguire & Google Maps Platform
03:18 - The AI Platform Shift: Unpacking the New "Ask Maps"
08:48 - Street View Insights: Querying 280 Billion Images with AI
11:12 - Real-World Use Cases: Digital Twins & City Infrastructure
14:04 - The Sky-Down View: Satellite AI & Disaster Response
16:33 - Scaling Solar APIs & The Importance of Temporal Data
18:58 - Unifying Street View, Aerial, and Satellite Data in BigQuery
21:49 - Generative AI Meets Reality: Grounding Models in the Physical World
26:19 - Democratizing Creativity: The Future of Film & Simulation
30:35 - What’s Next: How Developers Can Start Using These Tools Today
33:33 - The Vision for Google Earth AI: Merging Weather, Energy, and Ag Models
36:11 - Outro: The Future is Spatial

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🚀 Join The Spatial Lab:
Stop guessing at your career path. Get direct mentorship, advanced training, and a roadmap to these high-value roles inside The Spatial Lab.
👉 https://forrest.nyc/spatial-lab/

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Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

We dive into the data foundation, exploring the massive complexities of scaling geospatial data, consumer data, and POI data on a global level. Emily breaks down the gap between simply having access to millions of rows of location intelligence data and actually turning it into actionable business strategies.

To solve this, Dataplor launched a new location intelligence platform. By making it easy to visualize location data, non-technical teams can finally execute complex spatial analysis in gis without waiting on a data scientist.

We also explore powerful location intelligence use cases and location intelligence applications. Learn how real estate investors and CPG brands are using this data to fuel their global expansion strategy and retail expansion strategy by identifying market gaps and tracking competitors.

Finally, Emily shares how AI location data integration and AI analysis location data are changing the game, and why establishing a verified "ground truth" is more important than ever.

LEARN MORE ABOUT DATAPLOR

Learn More: https://www.dataplor.com/solutions/global-platform/
Follow Dataplor on LinkedIn: https://www.linkedin.com/company/dataplor/
Book a Demo: https://www.dataplor.com/contact/

Key Takeaways

- The End of the CSV: How the industry is moving from massive, hard-to-process spreadsheets to visual spatial analysis software that answers specific business questions instantly.

- Finding "Ground Truth": Why relying on a single mobility signal isn't enough anymore, and how layering alternative consumer data ensures high-quality insights.

- Winning Global Expansion Strategy: Real-world examples of how CPG brands and real estate investors use location intelligence platforms to spot market gaps and track global competitors.

- The AI Data Revolution: How AI location intelligence and the Model Context Protocol (MCP) are transforming how we consume and personalize geospatial data

00:00 Introduction to the State of Location Intelligence & Its Challenges 03:14 The Foundation: Scaling Geospatial Data & POI Data Globally
06:48 Bridging the Gap: Location Intelligence Solutions & Analytics
09:06 Overcoming Location Intelligence Data Privacy Roadblocks
14:50 Building a Location Intelligence Platform to Visualize Location Data
22:42 Location Intelligence Use Cases: Global Retail Expansion Strategy
28:44 Validation in Spatial Analysis Software: Finding the "Ground Truth"
34:37 The Future: AI Location Intelligence & Model Context Protocol (MCP) 41:42 How to Connect with Dataplor

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A viral LinkedIn post called "Something Big Is Happening" by Matt Schumer has been making the rounds and for good reason.

In this episode, I break down why the pace of AI development should have every GIS professional paying attention, what I'm seeing in the geospatial space right now (from Claude Code in ArcGIS to AI-specific job postings), and the four things you should be doing right now to future-proof your career. Whether you're mid-career or fresh out of school, this one's for you.

Original Article: https://www.linkedin.com/pulse/something-big-happening-matt-shumer-so5he

00:00 — The Article That Went Viral
00:40 — AI Coding Tools Are Changing Everything
01:51 — What I'm Seeing in Geospatial Right Now
02:30 — Step 1: Understand the Landscape
02:49 — Step 2: Start Learning the Tools
03:18 — Step 3: Architect Projects, Don't Just Prompt
04:09 — Step 4: Broaden Your Skill Set
04:43 — Advice for Recent Grads and Early-Career Pros
05:51 — Will AI Actually Wipe Out GIS Jobs?
06:42 — Wrap Up

---

📊 FREE: The Modern GIS Skill Map
The 5 skills that actually matter in modern GIS (and what you can stop learning). Based on a survey of 1,400+ geospatial professionals.
➡ Get the free training + PDF guide: https://forrest.nyc/go/training/

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Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

If you are still trying to run your entire geospatial workflow on a local desktop, you are fighting a losing battle. The "Modern GIS Stack" looks chaotic at first glance with dozens of logos, cloud formats, and new databases. But once you strip away the noise, there are actually only a few key layers you need to master to make it all work.

🚀 Don't navigate this shift alone. Join the Spatial Lab: https://forrest.nyc/spatial-lab/

In this video, I break down the architecture that is replacing the traditional GIS model. We move beyond Shapefiles and Geodatabases into the world of Cloud-Native Geospatial, showing you exactly how Storage, Compute, and Analytics have separated—and how you can use them to scale your career.

📰 Daily modern GIS insights: https://forrest.nyc

00:00 - The Modern GIS Chaos
00:34 - The Shift to Cloud-Native Formats
01:14 - Why Storage Buckets Replaced Hard Drives
02:07 - Essential Formats: GeoParquet, COGs & Zarr
03:57 - Adding Intelligence: STAC & Iceberg Catalogs
06:07 - Transformation & Orchestration (GDAL, dbt, Airflow)
08:30 - The 3 Engines of Modern GIS
08:48 - Engine 1: The Processing Layer (Sedona, Wherobots)
11:19 - Engine 2: The Transactional Layer (PostGIS)
12:38 - Engine 3: The Analytical Layer (BigQuery, Snowflake, DuckDB)
14:54 - Mapping Modern Layers to Traditional GIS
16:29 - The Application Layer: Analytics & BI
17:35 - Connecting QGIS & Python to the Cloud
18:30 - Modern Web Maps (Felt, Mapbox, DeckGL)
20:24 - Conclusion: You Don't Need to Learn Everything

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Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

Have you ever stopped to think about how GPS completely changed the world simply by becoming an invisible infrastructure running in the background of our everyday apps?

According to Pierrick Poulenas, the CEO and co-founder of Picterra, the exact same pattern is playing out right now with Earth Observation and GeoAI.In this episode, we sit down with Pierrick to explore how GeoAI is bridging the gap between raw satellite imagery and accessible business intelligence. We dive into how Picterra is removing the friction of complex remote sensing data, allowing non-technical users to train machine learning models and turn planetary pixels into actionable insights.

We also discuss the massive real-world impact this has on global supply chains and monitoring regenerative agriculture at scale. Plus, Pierrick shares his vision for a collaborative future in the space industry and teases an exciting new free tool for sustainability innovation.

Connect with Pierrick and Picterra:

https://picterra.ai/
https://www.linkedin.com/company/picterra/

Key Takeaways:

- Why Earth Observation is following the "GPS Playbook" to reach mass adoption.
- The shift from just collecting raw satellite data to creating usable applications at scale.
- How human-in-the-loop design builds trust and accuracy in AI models.
- Real-world use cases in spatial finance, fast-moving consumer goods (FMCG), and regenerative agriculture.

---

🚀 Join The Spatial Lab:
Stop guessing at your career path. Get direct mentorship, advanced training, and a roadmap to these high-value roles inside The Spatial Lab.
👉 https://forrest.nyc/spatial-lab/

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🌐 Website: https://forrest.nyc

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

In this episode of the Spatial Stack, Matt sits down with Kyle Walker, Professor of Geography at TCU and the creator of popular R packages like tigris and tidycensus.

Kyle dives into why he views US Census data as critical infrastructure and how open data is fundamentally transforming decision-making across industries like real estate and energy. He shares the origin story of his open-source work, explaining why he champions the R programming language for full-stack geospatial analysis. The conversation also covers the evolution of web mapping, from the laborious process of rendering dot-density maps to the blazing-fast performance of modern tools like PMTiles.

Finally, Kyle reveals how generative AI specifically Claude Code and the Zed editor is serving as his ultimate coding assistant, allowing him to rapidly build complex projects like the mapgl package and turn his ideas into reality faster than ever.

Connect with Kyle:

X/Twitter: https://x.com/kyle_e_walker
LinkedIn: https://www.linkedin.com/in/walkerke/
Bluesky: https://bsky.app/profile/kylewalker.bsky.social

00:01:00 – Welcome and Kyle Walker’s Background at TCU
00:06:18 – Why US Open Data is Critical Infrastructure
00:09:20 – Demystifying Census Data with tigris and tidycensus
00:15:48 – Applied Spatial Data: Real Estate and Forecasting Models
00:18:28 – The Evolution of High-Resolution Dot Density Maps
00:23:48 – The Human Element: How People React to Seeing Data Maps
00:29:14 – R vs. Python: Why R is a Geospatial Powerhouse
00:37:44 – Accelerating Development: Using Claude and AI for Coding
00:43:40 – The Future of Mapping: PMTiles, Segment Anything, and LLMs
00:48:18 – Where to Find Kyle’s Book, Tools, and Workshops

---

🚀 Join The Spatial Lab:
Stop guessing at your career path. Get direct mentorship, advanced training, and a roadmap to these high-value roles inside The Spatial Lab.
👉 https://forrest.nyc/spatial-lab/

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Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

We have never had more data about our planet: petabytes of satellite imagery, aerial photos, and sensor readings collected daily. Yet, turning that massive volume of "noise" into a clear signal remains the fundamental challenge of the geospatial industry.

In this episode of the Spatial Stack, I sit down with the engineering and product minds from Wherobots: Ryan, Phil, and Len - to tear down the architecture required to handle Earth Observation data at a planetary scale. We move beyond the buzzwords to discuss the engineering "war stories" of building resilient inference pipelines.

We dive deep into why the industry is moving away from simple computer vision toward "Large Earth Models" that function like LLMs for the physical world. We also get into the weeds of the tech stack: the battle between Dask and Ray for distributed compute, why Cloud-Optimized GeoTIFFs (COGs) aren't always the answer for inference, and how formats like Zarr are unlocking multidimensional analysis.

In this episode, we cover:

The Data Bottleneck: Why "garbage in, garbage out" is still the biggest hurdle in monitoring a changing planet.

Infrastructure Realities: The specific limitations of Google Earth Engine and why we needed a cloud-agnostic approach.

Engineering Pivot: Why Wherobots migrated from Dask to Ray to solve "crashing cluster" syndromes and memory management issues.

The Future of GeoAI: How embeddings and foundation models are compressing petabytes of data into searchable, semantic insights.

✅ Sign Up for Wherobots: https://wherobots.com/
✅ Learn more about Apache Sedona: https://wherobots.com/apache-sedona/
✅ Learn more about RasterFlow: https://wherobots.com/blog/rasterflow-earth-observation-inference-engine/
✅ Sign Up for the RasterFlow Private Preview: https://wherobots.com/rasterflow-preview/

00:00 – Teaser: The "Garbage In, Garbage Out" problem in GeoAI
00:01:51 – Introductions & Icebreakers (The controversial ice cream opinions)
00:03:08 – The Challenge: Monitoring a changing Earth at scale
00:10:30 – Data Engineering: The hidden complexity of NAIP, clouds, and tiling artifacts
00:14:19 – Modeling Reality: Why Computer Vision models fail on geospatial data
00:21:51 – The Google Earth Engine Debate: Walled gardens vs. bringing compute to the data
00:27:53 – Introducing Rasterflow: A new architecture for scalable inference
00:36:51 – The Engineering Story: Why we switched from Dask to Ray
00:43:40 – File Formats: Why Zarr is superior to COGs for multidimensional inference
00:47:40 – Workflow Walkthrough: Running the "Fields of the World" model
00:51:40 – Embeddings, Foundation Models, and Large Earth Models
00:57:40 – How to get started with Rasterflow

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Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

There are trillions of dollars invested in the physical world every da: infrastructure, supply chains, and our planet.

Yet many of these massive decisions are made without the data to back them up. For too long, geospatial analytics has been gated behind specialized teams and siloed technology, treated as "spatial is special" rather than just another data type.

In this episode, we sit down with Damian Wiley from Wherobots to break down how cloud architecture is finally closing this gap. With a heavy-hitting background from AWS EC2 and Databricks, Damian explains the shift from transactional databases to the Lakehouse architecture and why "Zero ETL" is the holy grail for data engineering.

We dive deep into why spatial data shouldn't be gated, how open table formats like Iceberg are changing the game, and why the future involves AI agents that can directly query the physical world.

If you are a data engineer, developer, or leader looking to unlock location intelligence without the headache of complex infrastructure, this conversation is for you.

✅ Sign Up for Wherobots: https://wherobots.com/
✅ Learn more about Apache Sedona: https://wherobots.com/apache-sedona/
✅ What is Apache Sedona: https://wherobots.com/blog/what-is-apache-sedona/
✅ Test out SedonaDB: https://sedona.apache.org/sedonadb/latest/
✅ Connect with Jia on LinkedIn: https://www.linkedin.com/in/wyliedamian/

00:00 - The Trillion Dollar Data Gap: Investing in the physical world without intelligence
02:15 - From AWS EC2 to Geospatial: Damian’s journey from cloud infrastructure to spatial data
06:40 - "Spatial is Special" No More: Breaking down silos and making spatial data "just data"
09:00 - The Lakehouse Advantage: Decoupling storage and compute for economic agility
12:15 - Fragmented History: Why geospatial tech became so compartmentalized
17:30 - Real-World Impact: Optimizing supply chains and climate response with frequent data
22:45 - The Economics of Analytics: Lowering the Total Cost of Ownership (TCO) for pipelines
28:30 - AI Agents & The Physical World: Connecting LLMs to ground-truth reality
37:00 - Compute Strategy: When to use OLAP vs. OLTP for spatial workloads
46:00 - Zero ETL & The Future: How Iceberg and open standards enable interoperability
51:20 - Getting Started with SedonaDB: Vibe coding and the future of spatial queries

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Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

Large Language Models can write poetry and debug code, but they still don't understand the fundamental physics of the real world. Ask an AI to find the "nearest restaurant" to a specific coordinate, and it struggles because it lacks Spatial Intelligence.

In this episode, we sit down with Jia Yu, the co-creator of Apache Sedona and co-founder of Wherobots, to discuss why geospatial data breaks standard big data engines and how he built the solution that now powers over 2 million downloads a month.

We trace the 10-year journey from a PhD research paper to a top-level Apache project, diving into the deep technical challenges of distributed computing. Jia explains why spatial data requires a completely different architecture than standard text or numbers and how the industry is finally moving toward a "Spatial Lakehouse" to break down data silos.

In this episode, we explore:

- The "Multimodality" Trap: Why mixing vector, raster, and LiDAR data crashes traditional systems.

- How SedonaDB is bringing massive scale to single-node machines (so you don't always need a cluster).

- The hardest problem in distributed computing - How to split a map across 1,000 servers without breaking the data.

- The multi-year fight to get native geometry support into Apache Iceberg.

- Why the next generation of models must evolve from text-based to spatially intelligent.

✅ Sign Up for Wherobots: https://wherobots.com/
✅ Learn more about Apache Sedona: https://wherobots.com/apache-sedona/
✅ What is Apache Sedona: https://wherobots.com/blog/what-is-apache-sedona/
✅ Test out SedonaDB: https://sedona.apache.org/sedonadb/latest/
✅ Connect with Jia on LinkedIn: https://www.linkedin.com/in/dr-jia-yu/ 

00:00:00 - Intro & Welcome 
00:00:51 - The Origin Story: From GeoSpark to Apache Sedona 
00:06:03 - Why Geospatial Data is "Special" (The Multimodality Problem) 
00:09:47 - When to Move to Distributed Computing? 
00:13:21 - The Secret to Maintaining a Vibrant Open Source Community
00:18:11 - The Features That Drove Adoption: Spatial SQL & Python 
00:22:35 - Deep Dive: How Spatial Partitioning Works 
00:28:57 - Why Build a Cloud-Native Platform? 
00:33:05 - The Rise of the Spatial Lakehouse & Apache Iceberg 
00:40:17 - Introducing SedonaDB: A Single-Node Engine 
00:45:10 - The Future: Why AI Needs Spatial Intelligence 
00:48:44 - Advice for Getting Started with Spatial Engineering

📰 Daily modern GIS insights: https://forrest.nyc

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Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

In 1963, the US Postal Service introduced "Mr. Zip" to make mail delivery faster. They never intended for those five digits to determine your insurance premiums, your home value, or your health outcomes.

In this short deep-dive, we explore how an arbitrary logistical tool became a shorthand for community and why that’s dangerous. From the misleading boundaries of Dallas, Texas, to the tragic data failures during the Flint water crisis, we uncover the real story behind the map.

Listen in to learn why it's time to move beyond the zip code and start looking at the details that actually matter.

---

Whenever you’re ready, here are 3 ways I can help you:

🎓 Modern GIS Accelerator: The step-by-step roadmap to master Python, Spatial SQL & Cloud workflows. Stop just "making maps" and start building spatial solutions. 👉 https://forrest.nyc/accelerator/

🧪 The Spatial Lab: Join the top 5% of geospatial professionals in our private community. Get access to exclusive courses, mentorship, and the network you need to level up. 👉  https://forrest.nyc/spatial-lab/

📰 Daily modern GIS insights: https://forrest.nyc

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

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

Maps have been around for thousands of years, but what they represent and how they work is changing faster than ever.

In this episode, I’m joined by Cliff Allison, who has spent more than 30 years building enterprise-scale mapping systems for governments and global organizations. Today, he leads government global sales at TomTom, helping bring modern, AI-powered mapping infrastructure to some of the most demanding use cases in the world.

We talk about how maps have evolved from static snapshots into living systems that update continuously, how open standards and collaboration made global mapping possible at scale, and why machines are now increasingly interacting with maps and with each other.

We also explore what this shift means for defense, intelligence, humanitarian response, and decision-making, and why mapping is no longer just a visualization layer, but a foundational system for understanding and predicting the world.

If you work in geospatial, data, AI, or infrastructure, this conversation will change how you think about maps.

---

Whenever you’re ready, here are 3 ways I can help you:

🎓 Modern GIS Accelerator: The step-by-step roadmap to master Python, Spatial SQL & Cloud workflows. Stop just "making maps" and start building spatial solutions. 👉 https://forrest.nyc/accelerator/

🧪 The Spatial Lab: Join the top 5% of geospatial professionals in our private community. Get access to exclusive courses, mentorship, and the network you need to level up. 👉  https://forrest.nyc/spatial-lab/

📰 Daily modern GIS insights: https://forrest.nyc

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

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

We have an incredible amount of public geospatial data—high-resolution elevation, weather forecasts, floodplain maps, real-time sensors—yet most people still can’t easily answer a simple question:

“What’s my flood risk right here, right now?”

In this episode, I’m joined by Kevin Bullock, an aerospace engineer and remote sensing expert at Development Seed, to talk about how he turned years of geospatial expertise into Hydra Atlas, a mobile app designed to make flood risk understandable and accessible for everyday users.

We explore why so much critical data remains difficult to use, how Kevin pulled together datasets from FEMA, NOAA, and USGS, and why mobile—not web—was the right platform for this problem. Kevin also shares what it was like building a geospatial app with Swift, testing real-world use cases, and designing an interface that prioritizes clarity over complexity.

This conversation goes beyond flooding. It’s about modern GIS, product thinking, open data, and what happens when geospatial professionals stop building tools for other experts and start building tools for people.

If you’re interested in geospatial product development, public data, mobile mapping, or turning complex systems into usable software, this episode is for you.

Download HydraAtlas: https://apps.apple.com/us/app/hydraatlas/id6749492232
Follow Kevin on LinkedIn: https://www.linkedin.com/in/kevbullock/

---

Whenever you’re ready, here are 3 ways I can help you:

🎓 Modern GIS Accelerator: The step-by-step roadmap to master Python, Spatial SQL & Cloud workflows. Stop just "making maps" and start building spatial solutions. 👉 https://forrest.nyc/accelerator/

🧪 The Spatial Lab: Join the top 5% of geospatial professionals in our private community. Get access to exclusive courses, mentorship, and the network you need to level up. 👉  https://forrest.nyc/spatial-lab/

🧭 Career Compass: Not sure where to start? Get the fast, practical steps to land the GIS role you actually want. 👉 https://forrest.nyc/career-compass/

📰 Daily modern GIS insights: https://forrest.nyc

CONNECT WITH ME
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💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

If there’s one word to describe the past year in geospatial, it’s change.

In this solo episode, I take you behind the scenes of what I’ve been seeing, hearing, and working on across geospatial, cloud, and AI over the past year, and how those shifts are shaping what actually matters heading into 2026 .

I talk about:

- Where AI is real vs overhyped in geospatial workflows
- Why cloud-native geospatial has quietly crossed into real production systems
- How formats like GeoParquet, Iceberg, and modern compute engines are changing where spatial data lives
- Why architecture and systems thinking are becoming the most valuable skills in the industry
- The rise of power skills (not “soft skills”) across roles like data engineering, product, architecture, and leadership
- What roles are emerging, and how they actually work together in modern spatial teams

This isn’t a predictions episode built on hype. It’s a grounded look at what changed, what didn’t, and what skills and mindsets will matter most as geospatial continues to integrate with the broader data and AI ecosystem.

If you’re a GIS professional, data engineer, architect, product manager, or leader trying to understand how spatial fits into modern systems, this episode will help you frame what’s next, and how to prepare for it.

---

🚀 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

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⚡️ Spots for the next live cohort and mentorship cycle are closing soon,  join now to lock in your place and momentum.

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GeoPandas is one of the most important tools in modern GIS, but many people still aren’t sure when to use it, why it matters, or where it fits alongside tools like PostGIS, DuckDB, Apache Sedona, and cloud-native data formats.

In this video, I break down GeoPandas from the ground up: what it is, how it works under the hood, its strengths and limitations, and when to choose something else. If you’ve ever worked in ArcGIS or QGIS and wondered how to bring those same workflows into Python, this is the perfect place to start.

What we cover in this video:

- What GeoPandas actually does (How it extends Pandas, adds geometry types, reads vector formats, and integrates tools like Shapely, Fiona, PyProj, GeoArrow, and GeoParquet)
- Why GeoPandas matters in modern GIS
- When GeoPandas is the right tool
- When NOT to use GeoPandas
- How GeoPandas fits into the modern stack (How it pairs with DuckDB, SedonaDB, PostGIS, Apache Sedona (Spark), data lakes, Iceberg, and cloud-native geospatial)
- How to actually get started

This video is for you if you are a:

• GIS professionals moving into Python
• Data scientists adding spatial capabilities
• Engineers exploring geospatial data stacks
• Anyone who wants a modern alternative to desktop GIS workflows

Resources from the video

- My GeoPandas Course: https://www.youtube.com/watch?v=0mWgVVH_dos
- GeoPandas Documentation: https://geopandas.org/en/stable/getting_started/introduction.html
- Dr. Qiusheng Wu's New Book on Geospatial Python: https://www.amazon.com/dp/B0FFW34LL3

---

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— 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

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0:00 Intro to GeoPandas
0:35 What is GeoPandas
2:54 Why should you care about GeoPandas?
5:12 Do you need to use GeoPandas?
8:22 How do you use GeoPandas?
10:59 Pitfalls of GeoPandas
13:06 When NOT to use GeoPandas?
14:50 Where to learn about GeoPandas?

CONNECT WITH ME
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Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

Meta has more than 3 billion users across Instagram, WhatsApp, and even its new AR glasses. Behind the scenes, all of them are powered by one thing: maps. But instead of relying on closed systems, Meta is betting big on open data—and building its own global map.

In this episode, I talk with Said Turksever from Meta, who leads their open mapping strategy. We dive into:

🌍 Why Meta cares so much about maps
🛠 The tools they’re building with AI and open source
🏙 How cities from Phoenix to Naples are being transformed by open data
🚶 The future of pedestrian mapping and accessibility
🤝 The role of communities in shaping the next generation of maps

From disaster response to daily navigation, the impact of open mapping stretches far beyond social media. This is a conversation about technology, community, and the future of how we navigate the world.

🚀 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 6 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

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ArcGIS Pro has been the center of GIS workflows for decades but how does it hold up in a world moving toward open, cloud-native, and AI-powered geospatial tools? In this video, I break down what ArcGIS Pro actually is, where it shines, where it struggles, and how it fits into the modern GIS ecosystem.

Whether you’re doing personal GIS projects, running a small team, or architecting enterprise-scale systems, this deep dive will help you understand when ArcGIS Pro is the right choice and when alternatives like QGIS, GeoPandas, DuckDB, PostGIS, Sedona, or cloud-native stacks might serve you better.

What You’ll Learn

- What ArcGIS Pro is and how it fits into Esri’s ecosystem
- Its strengths in cartography, desktop analysis, 3D tools, enterprise integration, and data management
- Newer support for modern formats like GeoParquet, COGs, STAC, and DuckDB
- Where ArcGIS Pro begins to struggle (big data, cloud workflows, Python lock-in, cost/licensing)
- How it compares to open tools like QGIS, GeoPandas, and modern geospatial data platforms
- My honest assessment of whether YOU should be using ArcGIS Pro across personal, team, and enterprise use cases

---

🚀 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.

0:00 Intro to ArcGIS Pro
0:31 My background with Esri
1:16 What is ArcGIS
4:02 Why does ArcGIS Pro matter?
7:11 Do you need to use ArcGIS Pro?
12:54 How do you use ArcGIS Pro?
14:50 Pitfalls of ArcGIS Pro
17:47 When to use ArcGIS Pro?
20:15 Where to learn about ArcGIS Pro?

CONNECT WITH ME
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In this episode, I sit down with Felix Palmer, creator of A5, a new global grid system that’s redefining how we represent and analyze the Earth.
Felix shares how a 1980s math paper led him to build a pentagon-based global grid, one that fixes the distortions found in systems like H3 and S2, achieving true equal-area cells across the entire planet.

We discuss the geometry behind A5, the trade-offs of global gridding, and why visual design and spatial computation are deeply connected.
If you’ve ever worked with spatial indexes or wondered how we might rebuild the geometry of the globe itself, this episode will change the way you see maps.

We cover:

The origins of A5 and how it compares to H3
Why equal-area grids matter for accurate analysis
The visual and geometric challenges of mapping a sphere

A5 Website: https://a5geo.org/
A5 on GitHub: https://github.com/felixpalmer/a5

🚀 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

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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.
👉 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/
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📧 Newsletter: https://forrest.nyc
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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
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📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc

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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

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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/

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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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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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.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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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.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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💨 Sign up for the Modern GIS Accelerator ➡️ https://forrest.nyc/accelerator ⬅️

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:

• The pay disparity across GIS roles and what it reveals
• How entry-level positions can set the wrong trajectory
• Why spatial skills alone aren’t enough without systems thinking
• The mindset shift needed to break free from being "just a mapmaker"
• How to move from being a button-pusher to a strategic stakeholder

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.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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💨 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!

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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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

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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!

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

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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

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

For years, geospatial professionals have been forced to download massive datasets—only to discard most of the data. It’s an outdated, inefficient process that slows down analysis and limits scalability. But a major shift is happening. Cloud-native geospatial is transforming how we store, access, and process spatial data, making it faster, more efficient, and accessible to everyone.

In this episode, I sit down with Jed Sundewald, Executive Director of Radiant Earth, to explore how the Cloud-Native Geospatial Forum is shaping the future of GIS, the evolution of cloud-optimized formats like COGs and STAC, and what this means for GIS professionals, developers, and decision-makers.

Whether you're new to cloud-native geospatial or looking to optimize your data workflows, this conversation will give you a front-row seat to the future of geospatial technology.

📩 Get every update from my newsletter ➡️ https://forrest.nyc ⬅️
🏔️ Attend the Cloud Native Geospatial Forum in April ➡️ https://2025-ut.cloudnativegeo.org/ ⬅️

🔑 Topics Covered:

✔️ Why traditional geospatial workflows are inefficient
✔️ The rise of cloud-native geospatial formats (COGs, STAC, and more)
✔️ How cloud-native GIS benefits both small and large-scale users
✔️ The role of the Cloud-Native Geospatial Forum and upcoming events
✔️ How to integrate cloud-native principles into your geospatial stack

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

📩 Get every update from my newsletter ➡️https://forrest.nyc ⬅️

🔑 Sign up for Placekey here:https://placekey.io

Merging and cleaning location data is one of the biggest headaches in GIS, real estate, and business intelligence. Duplicate addresses, inconsistent formats, and messy datasets can derail even the most sophisticated analysis. But what if there was a universal identifier that made all of those problems disappear?In this episode, I sit down with Hayden Mortimer, President of PlaceKey, to uncover why traditional addresses aren’t enough and how PlaceKey is revolutionizing the way we connect place-based data. We dive into the challenges of entity resolution, the pitfalls of fuzzy matching, and how a simple, open-source identifier is making spatial joins seamless.Whether you work in GIS, data science, or any field that relies on location data, this conversation will change how you think about addressing and spatial relationships. Don’t miss it!✔️ Why addresses alone aren’t reliable for data integration✔️ The hidden challenges of matching places across datasets✔️ How PlaceKey works and why it’s different from other solutions✔️ Real-world use cases for small businesses and enterprise-level applications✔️ The future of location data and entity resolution🎧 Listen now and learn how to fix your messy location data for good!

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

Matt returns to discuss the future of Spatial Stack—what it is, where it’s headed, and how it will evolve into a go-to platform for exploring the intersection of geospatial technology and real-world impact. From its origins as a live-stream experiment to a full-fledged video-first podcast, this episode lays out the vision for breaking the geospatial echo chamber, identifying new opportunities, and working together to expand the field.

Expect a mix of solo deep dives, expert conversations, and practical discussions on geospatial trends, Earth observation, and the growing role of spatial data across industries. Whether you're a seasoned GIS professional or just curious about geospatial’s future, this episode sets the stage for what’s to come.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

Apache Sedona has been a core element of the geospatial analytics space for several years now, and in this live stream we will explore how Wherobots is taking this to the next level with different advancements in visualization, powerful geospatial data engineering, and AI.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

Standardizing spatial data for specific verticals or use cases has been tried many times throughout the history of geospatial. But now we're at a point where things might actually be taking a turn and having a standard format for different use cases be it agriculture, socio-demographics, or any other number of problems might be within sight. We'll talk with Chris Holmes to hear his opinions about why this is changing and why now is the right time to take this on and taking a look at a first use case of this for agricultural field data with fiboa.

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

How do you get access to just the data you need? And what if that data isn't in large datasets or traditional mapping services? And can AI make this data accessible to those without a background in geospatial? Get answers to these questions and more in this live stream with Hantz Févry, CEO of Stoovo!

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

In this conversation with Kyle Barron from Development Seed, we will learn lonboard, a new Python package that solves the problem of visualizing massive geospatial datasets in Python. Learn about the motivations behind the project, some of the tech under the hood, and of course a demo!

Sign up for TLDR for free here!

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

Join Isaac Brodsky, CTO and Co-Founder at Fused where we will dive deep into scaling geospatial workflows and shipping them using their new product Fused.

https://www.fused.io/

Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!