Spatial Stack with Matt Forrest

Matt Forrest
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Welcome to The Spatial Stack, where modern geospatial technology takes center stage. Our episodes feature interviews with leading experts, insightful discussions on the integration of AI and big data in spatial tech, and case studies on groundbreaking projects worldwide. Tune in to stay ahead in the rapidly evolving world of geospatial technology!

  1. Desktop GIS is Dying. Here’s What Replaced It.

    6D AGO ·  BONUS

    Desktop GIS is Dying. Here’s What Replaced It.

    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 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!

    22 min
  2. The Next GPS? Why GeoAI is the New Invisible Infrastructure with Pierrick Poulenas (Picterra)

    MAR 18

    The Next GPS? Why GeoAI is the New Invisible Infrastructure with Pierrick Poulenas (Picterra)

    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/ 📰 Daily modern GIS insights: https://forrest.nyc CONNECT WITH ME📸 Instagram:  https://www.instagram.com/matt_forrest/🎵 TikTok: https://www.tiktok.com/@mbforrgis💼 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!

    34 min
  3. Mastering Spatial Data in R: TidyCensus, PMTiles, & AI with Kyle Walker

    FEB 17

    Mastering Spatial Data in R: TidyCensus, PMTiles, & AI with Kyle Walker

    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_walkerLinkedIn: 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/ 📰 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!

    52 min
  4. #40: The "GPT Moment" for Earth: Moving from Computer Vision to Large Earth Models

    FEB 11

    #40: The "GPT Moment" for Earth: Moving from Computer Vision to Large Earth Models

    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 GeoAI00:01:51 – Introductions & Icebreakers (The controversial ice cream opinions)00:03:08 – The Challenge: Monitoring a changing Earth at scale00:10:30 – Data Engineering: The hidden complexity of NAIP, clouds, and tiling artifacts00:14:19 – Modeling Reality: Why Computer Vision models fail on geospatial data00:21:51 – The Google Earth Engine Debate: Walled gardens vs. bringing compute to the data00:27:53 – Introducing Rasterflow: A new architecture for scalable inference00:36:51 – The Engineering Story: Why we switched from Dask to Ray00:43:40 – File Formats: Why Zarr is superior to COGs for multidimensional inference00:47:40 – Workflow Walkthrough: Running the "Fields of the World" model00:51:40 – Embeddings, Foundation Models, and Large Earth Models00:57:40 – How to get started with Rasterflow 📰 Modern GIS insights: https://forrest.nyc CONNECT WITH ME📸 Instagram: https://www.instagram.com/matt_forrest/💼 LinkedIn: https://www.linkedin.com/in/mbforr/ 🌐 Website: https://forrest.nyc Join me at ELIS 2026 - elisevent.com - Use the code Matt15 for 15% off! See you in Canada!

    1h 1m
  5. #39: Why Geospatial Needs the Lakehouse with Damian Wylie

    FEB 4

    #39: Why Geospatial Needs the Lakehouse with Damian Wylie

    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📰 Daily modern GIS insights: https://forrest.nycCONNECT 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!

    39 min
  6. #38: How Apache Sedona Solved Big Data’s Hardest Problem with Jia Yu

    JAN 29

    #38: How Apache Sedona Solved Big Data’s Hardest Problem with Jia Yu

    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 Community00:18:11 - 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 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!

    56 min
  7. The Hidden History (and Flaws) of the Zip Code

    JAN 23 ·  BONUS

    The Hidden History (and Flaws) of the Zip Code

    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!

    12 min
  8. #37: From Static Maps to Living Systems: How AI Is Changing Global Mapping with Cliff Allison from TomTom

    JAN 21

    #37: From Static Maps to Living Systems: How AI Is Changing Global Mapping with Cliff Allison from TomTom

    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!

    1 hr
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Welcome to The Spatial Stack, where modern geospatial technology takes center stage. Our episodes feature interviews with leading experts, insightful discussions on the integration of AI and big data in spatial tech, and case studies on groundbreaking projects worldwide. Tune in to stay ahead in the rapidly evolving world of geospatial technology!

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