Spatial Stack with Matt Forrest

In this episode of the Spatial Stack, Matt sits down with Jake Low, software developer at OpenStreetMap US and maintainer of MapRoulette, OSMCha, Field Papers, and Layercake.

Jake explains how a map that anyone can edit takes in about 40,000 changesets a day, and why his job is building tools for the community rather than gatekeeping it. He walks through every way people contribute, from StreetComplete on your phone to JOSM on the desktop to Field Papers, a pen-and-paper workflow used in disaster response and elementary school classrooms.

The conversation also covers getting data back out of OpenStreetMap, from Layercake's thematic GeoParquet extracts to Sourdough, a new vector tile schema inspired by CSS Zen Garden. Jake breaks down the ODbL license in plain English, how companies like Amazon are paying contractors to map rural driveways, and why 80% of maintaining open source infrastructure is just keeping it alive.

Whether you're making your first edit tonight or pulling the planet file into DuckDB, this conversation shows you how the world's open map actually runs.

Connect with Jake:

Website: https://www.jakelow.com/
GitHub: https://github.com/jake-low
OSM US Slack: https://slack.openstreetmap.us
OpenStreetMap US: https://openstreetmap.us

SUPPORT

Support OSM US: https://openstreetmap.us/get-involved/donate/

LEARN MORE

Field Papers: https://openstreetmap.us/our-work/field-papers/
Layercake: https://openstreetmap.us/our-work/layercake/
OSMCha: https://openstreetmap.us/our-work/osmcha/
MapRoulette: https://openstreetmap.us/our-work/maproulette/

CHAPTERS:

00:00:00 – Intro
00:02:03 – Welcome and Jake's background
00:03:44 – 40,000 changesets a day and OSMCha
00:07:28 – What OpenStreetMap US does
00:11:19 – Contributing at every scale: StreetComplete to JOSM
00:13:11 – Mapping with pen and paper: Field Papers
00:15:56 – Start tonight: MapRoulette and 10,000 duplicate reservoirs
00:19:32 – Gamification, paid mapping, and Amazon driveways
00:22:49 – Sourdough and the CSS Zen Garden idea
00:28:11 – Layercake: OpenStreetMap as GeoParquet
00:31:58 – The ODbL license in plain English
00:34:53 – Keeping open source infrastructure alive
00:41:21 – What people get wrong about OpenStreetMap
00:43:45 – Rapid fire: DuckDB, WebGPU, and Yesterdays

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In this episode of the Spatial Stack, Matt sits down with Dr. Chris Tucker, chairman of the American Geographical Society, in the year the AGS turns 175.

Chris makes the case that spatial thinking is foundational literacy, on the level of language or math, and that the 4.5 million people who have taken AP Human Geography are already practicing it every day in jobs that never mention geography. He shares how a single question from a CIA veteran, about why we cannot draw a bounding box on a map and pull in everything known about that place at that moment, set the direction for almost thirty years of his career.

The conversation also covers what has changed about getting into this work. The advanced degree, the expensive workstation, and the expensive software used to be the price of entry. Free tools and YouTube replaced all three.

Chris then takes on a challenge: name five careers that do not sound geographic but where spatial skills give you a real edge. Surveying, the CAD and built environment world, aviation, marine navigation, and conservation all make the list, along with a case for why geography works as both a horizontal and a vertical.

We also get into the AGS itself, from the Fliers' and Explorers' Globe signed by Amelia Earhart, Sir Edmund Hillary, and Neil Armstrong, to the geographers at 156th and Broadway who worked on the inquiry behind the Treaty of Versailles.

If you have ever struggled to explain what you actually do for a living, this conversation gives you better language for it.

Connect with Chris Tucker and the AGS:
LinkedIn: https://www.linkedin.com/in/christopher-tucker-5595689/
American Geographical Society: https://www.americangeo.org
Email: tucker@americangeo.org

LEARN MORE
AGS 175th anniversary campaign: https://www.americangeo.org
The Fliers' and Explorers' Globe: https://www.americangeo.org/globe
Geography 2050 (Africa: Shaping the Future): https://www.geography2050.org
Teen Maptivist: https://www.teenmaptivists.org/

CHAPTERS:
00:00:00 – Welcome and Chris Tucker's Background
00:01:21 – 4.5 Million AP Human Geography Students
00:04:48 – Spatial Thinking as Foundational Literacy
00:09:08 – 175 Years of the American Geographical Society
00:12:03 – The Fliers' and Explorers' Globe
00:15:05 – The AGS and the Treaty of Versailles
00:18:02 – The 175th Anniversary Campaign
00:21:49 – How Matt and Chris Found Geography
00:24:59 – Free Tools and Getting Back Into the Work
00:27:07 – Advocating for Geography in Schools
00:30:55 – Geography as Horizontal and Vertical
00:35:01 – Five Careers Where Spatial Gives You an Edge
00:40:34 – Getting Involved with AGS and Geography 2050

---

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The 5 skills that actually matter in modern GIS (and what you can stop learning). Based on a survey of 1,400+ geospatial professionals.
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What does it take to map something you have never seen?

In this episode, Matt Forrest sits down with Justin Stewart, quantitative ecologist at SPUN, the Society for the Protection of Underground Networks, to unpack how his team built the first global map of arbuscular mycorrhizal fungi, the soil organisms living in symbiosis with most plants on Earth.

Justin walks through the full pipeline, from robotic microscopy of threads two and a half microns wide up to a planetary prediction at one square kilometer resolution. The team assembled 16,000 soil samples from more than 300 studies across 11 languages, trained a random forest on 34 satellite-derived layers in Google Earth Engine, and
summed the result: roughly 110 quadrillion kilometers of fungal thread, about 300 megatons of carbon, six times the biomass of every human alive.

The findings were not what anyone expected. The densest networks are under grasslands, not forests. Croplands run about 50 percent sparser. And 90 percent of the biodiversity hotspots for these fungi sit outside any protected area.

In this episode, we cover:

- Why mapping a symbiosis is harder than mapping an organism
- Robotic imaging in the Amolf biophysics lab, and tracking half a million nodes at once
- Building a global database from literature in 11 languages
- Random forest niche modeling and uncertainty analysis in Google Earth Engine
- The effective radius algorithm that turns network length into biomass
- Why grasslands, flooded wetlands, and the Tibetan plateau light up on the map
- Tilling, fertilizer, and how a 450 million year old symbiosis gets broken
- Rights of nature, eDNA, and why predictive maps are not yet admissible evidence

LINKS:

Justin Stewart
SPUN profile: https://www.spun.earth/team-members/justin-stewart
LinkedIn: https://www.linkedin.com/in/justin-stewart-70512645/
Google Scholar: https://scholar.google.com/citations?hl=en&user=1aqTJIUAAAAJ

SPUN
Website: https://www.spun.earth
Underground Atlas: https://a-hidden-infrastructure.spun.earth/story/a-hidden-infrastructure

LEARN MORE
The paper: Science, June 11, 2026. DOI 10.1126/science.adu4373
Mycorrhizal Biodiversity Map: https://a-hidden-infrastructure.spun.earth/story/a-hidden-infrastructure

CHAPTERS:
00:00:00 – The largest living network on Earth
00:02:15 – Welcome and intro
00:03:38 – What the map shows: a partnership, not an organism
00:05:48 – Robots, petri dishes, and carbon moving at 400 kilometers an hour
00:07:47 – The question that started it: how much is out there?
00:09:25 – 110 quadrillion kilometers and six times human biomass
00:11:15 – What a quantitative ecologist actually does
00:13:53 – Trade, lipids, and 30 percent of a plant's nitrogen
00:16:31 – What people get wrong about soil
00:18:27 – The pipeline: 34 satellite layers, random forest, Earth Engine
00:21:02 – From length to weight: effective radius and the robot named Prince
00:25:41 – Croplands and a broken 450 million year symbiosis
00:31:40 – Rights of nature and getting predictions accepted as evidence
00:37:10 – Where to find SPUN and Justin

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What does it actually take for an AI to navigate you through the real world, without a screen and without a camera running the whole time?

In this episode of the Spatial Stack, Matt Forrest sits down with Sean Gorman and Pramukta Rao, the co-founders of Zephr, to unpack how they ground large language models in location. Sean and Pramukta spent twenty years building startups together, from GeoIQ to Snap's visual positioning work, and at Zephr they walked away from the camera and went back to the sensors already in your phone.

They get into why the blue dot on a map breaks down the moment you stop looking at a screen, why language models are bad at spatial reasoning like left, right, and across the street, and how they fix it by doing the geometry first and handing the model clean language. Instead of training ever-bigger foundation models, they push a small model and well-structured data down to the edge so the conversation stays fast.

In this episode, we cover:

- Why they left visual positioning and AR cameras behind after Snap

- Cooperative positioning: getting survey-grade accuracy out of commodity phones

- GNSS and the urban canyon problem (3 meters to 50 meters and back)

- Beyond the blue dot: building a first-person, egocentric experience

- Why LLMs struggle with geometry, and what context engineering solves

- Small models at the edge vs giant foundation models

- Overture Maps, GERS IDs, and conflating POIs with imagery to "agree on reality"

- The grounding service: MCP, REST, and Opus or Gemma on device

- Conversations about place as a new geospatial primitive

Whether you build with spatial data, work on AI navigation, or just want to see where location and LLMs are heading, this conversation is your field guide.

Connect with Zephr:

Website: https://zephr.xyz

Sean Gorman (LinkedIn): https://www.linkedin.com/in/sean-gorman-93a79

Pramukta Rao (LinkedIn): https://www.linkedin.com/in/pramukta/

Zephr (LinkedIn): https://www.linkedin.com/company/zephr-xyz

00:00:00 – Intro and twenty years of startups together
00:04:05 – Why they walked away from cameras and AR at Snap
00:04:54 – Commodity sensors and cooperative positioning
00:07:15 – GNSS 101 and the urban canyon problem
00:10:56 – Beyond the blue dot: an egocentric experience
00:13:37 – Why LLMs are bad at left, right, and across the street
00:18:19 – Context engineering and the retrieval problem
00:22:03 – Small models at the edge vs giant foundation models
00:30:09 – Collective memory: OpenStreetMap, Mapillary, Overture
00:32:08 – Conflating POIs with imagery to agree on reality
00:35:36 – The grounding service: MCP, REST, Opus or Gemma on device
00:38:37 – What's next: conversations as a new geospatial primitive
00:42:22 – Where to find Zephr

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In this episode of the Spatial Stack, Matt sits down solo to work through a fight that broke out across Reddit and LinkedIn last week: is GIS dying, or is it just changing its name?

It started with a GIS manager who argued the field is alive, pointing to a friend who landed a $200,000 job building autonomous systems, a role where GIS never appeared in the title or the description. The comments pushed back. If the keyword for our whole profession barely returns jobs anymore, is the field really growing?

Matt's answer is that both sides are right. The work of spatial is expanding into data engineering, software, and product roles. The GIS title is contracting at the same time. It's one trend seen from two directions, the same way geology departments quietly became geoscience without the work ever changing.

He makes the case that the title was never the skill. Spatial intuition is, and it's the thing that transfers into higher-paying roles that don't carry the GIS label. Then he closes with a challenge: describe who you are and what you do without using the words GIS, spatial, or geospatial.

Whether you're job hunting, stuck in the technician trap, or an employer who wants this skill set but doesn't know what to call it, this conversation is about how we position the work going forward.

---

📊 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/

CHAPTERS:

00:00:00 – The Reddit post that started it
00:01:00 – Two views: the work is growing vs. the title is shrinking
00:02:34 – Why "stay positive" doesn't pay the rent
00:03:33 – Spatial is special: what actually transfers
00:04:53 – The geology-to-geoscience analogy
00:05:54 – The title of GIS was never the skill
00:07:09 – The technician trap and where the salaries moved
00:08:18 – Listener comments: spatial judgment, the map as interface
00:10:32 – What to do now: SQL, Python, and cloud-native beyond the toolbox
00:11:51 – Building a portfolio on LinkedIn
00:13:22 – The Spatial Intuition Challenge
00:15:32 – Put your face on it: make it a video

CONNECT WITH ME
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📧 Newsletter: https://forrest.nyc
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What does it actually take to make AI useful on location data?

In this episode, Matt Forrest sits down with Ryan Urabe, co-founder and CTO of Dataplor, to unpack how AI, embeddings, and agents are changing the way we work with points of interest and places data.

Ryan explains why general-purpose models already understand spatial concepts but still struggle to execute them, and why the real unlock is the harness around the model, not a geospatial-specific model. He walks through Dataplor's data-quality philosophy, the category problem (why "supermarket" and "grocery store" have zero string similarity but near-zero conceptual distance), and how embeddings let them measure conceptual distance across 10^9 places and even across languages.

Whether you build with spatial data, lead a data team adopting AI, or you are trying to figure out what embeddings actually do, this conversation maps out what is working today and what is still forming.

In this episode, we cover:

- Why AI is an accelerant for data quality, not a replacement for it
- Treating AI like a capable employee on their first day
- Where general models fall short on spatial problems
- DuckDB as the Swiss Army knife for orchestrating spatial data
- The category problem and conceptual vs. semantic distance
- How embeddings map 7-Eleven in Tokyo and Tennessee to the same concept
- A vision for agentic AI built natively for geospatial
- The "end of the scarcity of intelligence" framing for where this is all heading

Connect with Ryan:

LinkedIn: https://www.linkedin.com/in/rurabe/
Website: https://www.dataplor.com
Email: ryan@dataplor.com

LEARN MORE

Dataplor's agentic SaaS product is launching this summer. To start your complimentary trial, contact: freetrial@dataplor.com

📊 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.

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00:00:00 – Cold open
00:01:01 – Welcome and Ryan's background
00:03:44 – Why AI still struggles with location
00:05:13 – Bringing Dataplor's data into AI, product and team
00:08:36 – How technical teams are adopting AI
00:10:10 – Treating AI like a capable new employee
00:12:20 – Where general models fall short on spatial
00:16:44 – DuckDB and opinionated workflows
00:18:14 – Data quality as the whole game
00:20:58 – The category problem: supermarket vs. grocery store
00:27:53 – Embeddings and conceptual space, with a 3D walkthrough
00:38:22 – A vision for agentic AI in geospatial
00:43:37 – The end of the scarcity of intelligence
00:47:25 – Where to find Ryan and Dataplor

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What does it actually take to map every agricultural field on Earth?

In this episode, Matt sits down with Jen Marcus, Vice President of Strategic Innovation Programs at Taylor Geospatial, and Isaac Corley, Director of AI/ML Research at Taylor Geospatial and a torchgeo maintainer, the team behind Fields of The World (FTW).

In late April they released the first globally consistent dataset of agricultural field boundaries, at 10m resolution, fully open on Source Cooperative. They dive deep into how it came together, from building the fiboa format to standardize ground truth across 24 countries, to running model inference across the entire planet, to shipping it with a confidence layer instead of pretending it was perfect. You'll hear honest perspective on what GeoAI can really do today and where the hype outpaces reality.

In this episode, we cover:

- Why a global field boundary map had never been done, and why no single organization was positioned to do it
- The labeled-data problem and why models have to generalize to places like South America and Africa with little ground truth
- The fiboa format and Chris Holmes's "architectures of participation"
- How the Technical Fellows program turned open-source contributors into the core team
- Running global inference efficiently with Sentinel-2 planting and harvest mosaics
- Cloud-native outputs (GeoParquet, PMTiles, Zarr) you can stream with no backend
- What's real vs. what's marketing in geospatial AI, and the ImageNet lesson
- What's next: stakeholder feedback loops, higher-resolution imagery, and mapping new features beyond fields

Whether you build ML pipelines, work with satellite data, or you've ever wondered how much of the planet is still genuinely unmapped, this conversation breaks it down without the buzzwords.

LINKS:

Fields of The World: https://fieldsofthe.world
Dataset on Source Cooperative: https://source.coop/wherobots/fields-of-the-world
Taylor Geospatial: https://taylorgeospatial.org

Jen Marcus

LinkedIn: https://www.linkedin.com/in/jennifer-marcus-b559091/

Isaac Corley

Website: https://isaac.earth
LinkedIn: https://www.linkedin.com/in/isaaccorley/
GitHub: https://github.com/isaaccorley

📊 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/

CHAPTERS:
00:00:00 – Cold Open
00:01:01 – Welcome and Guest Intros (Jen Marcus and Isaac Corley)
00:02:32 – Why Map Field Boundaries, and Why It Had Never Been Done
00:05:53 – Going from Local to Global Scale
00:07:19 – Architectures of Participation and the fiboa Format
00:12:44 – The First St. Louis Meeting and the Technical Fellows Program
00:18:04 – Running Global Inference at Scale
00:22:54 – Cloud-Native Outputs on Source Cooperative
00:25:05 – Why This Matters and What's Real vs. Hype
00:28:39 – The ImageNet Lesson and Holding a North Star
00:32:20 – What's Next for Fields of The World
00:36:12 – Impact, an OpenStreetMap for Fields, and How to Get Involved
00:40:33 – Postdoc, Tech Fellows, and Looking Out the Airplane Window

🚀 Join The Spatial Lab:
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👉 https://forrest.nyc/spatial-lab/

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In this episode of the Spatial Stack, Matt sits down with Troy Schmidt, a 20-year GIS developer and the creator of SPHERE, an open-source Python package that runs FEMA's HAZUS flood risk methodology on GeoParquet and DuckDB.

Troy dives into why depth damage functions are simpler than the engineering language suggests, and how the gap between the HAZUS methodology and the HAZUS software pushed him to build a Python-first alternative. He shares the moment he realized the data and the science were already public, and that the only thing missing was a modern stack to run it on.

The conversation also covers the three types of flooding most people lump together (coastal, riverine, and pluvial), why pluvial risk is the gap that nobody insures, and what an open core model means for geospatial science.

Finally, Troy walks through his cloud-native discovery process, from Wherobots and Earthmover webinars to DuckDB and vectorized math, and explains why turning legacy methodology into a Python package unlocks deployment patterns that were never possible before.

📊 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/

Connect with Troy Schmidt:
LinkedIn: https://www.linkedin.com/in/mr-troy-schmidt/
SPHERE on GitHub: https://github.com/Niyam-Projects/sphere

CHAPTERS:
00:00:00 – Intro
00:01:35 – Welcome and Troy's 20-Year GIS Journey
00:04:17 – The Risk Modeling Landscape
00:07:25 – Coastal, Riverine, and Pluvial Flooding
00:11:05 – HAZUS Methodology vs. HAZUS Software
00:16:05 – Site-Specific vs. Census Block Analysis
00:18:05 – Building SPHERE: A Python Package for Risk
00:21:25 – Cloud-Native, GeoParquet, and DuckDB
00:25:05 – Why the Methodology Was Simple All Along
00:27:05 – Discovery: Webinars, Wherobots, and the Modern Stack
00:29:35 – Spatial as a Boundary Data Type
00:32:35 – The Open Core Model and the Bridge to Modern GIS
00:38:05 – Ensemble Models and What's Next for Climate Risk
00:39:35 – Where to Find Troy and SPHERE

🚀 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
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💼 LinkedIn: https://www.linkedin.com/in/mbforr/
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In this episode of the Spatial Stack, Matt sits down with Maggie Ma, tech content creator at @maggieindata and former geospatial data scientist.

Maggie left her corporate data science role last year to become a full-time content creator across Instagram, YouTube, LinkedIn, and TikTok. She's a 3x LinkedIn Learning instructor and an AI educator helping people break into data science, learn coding, and stay current with AI.

We dig into the GIS title trap (and why the same job pays less under a different title), the seven internships that got Maggie into geospatial data science, cold-emailing professors and police departments, and how she positioned herself as the spatial person on non-spatial teams. We also cover the push and pull factors that led to her quitting corporate, what day in the life looks like as a full-time creator, and how she actually uses AI in her workflow today.

Whether you're a GIS analyst wondering if you're underpaid, a geography student trying to land your first role, or a working data scientist thinking about going full-time creator, this conversation is full of specific tactics and honest reflections.

Connect with Maggie:

Instagram: https://www.instagram.com/maggieindata
YouTube: https://www.youtube.com/@maggieindata
LinkedIn: https://www.linkedin.com/in/maggieindata
TikTok: https://www.tiktok.com/@maggieindata

CHAPTERS:

00:00:00 – Intro
00:01:08 – Welcome and Maggie's Background
00:03:26 – Statistics, Psychology, and Discovering Human Geography
00:06:39 – First Job: Geospatial Data Scientist in Logistics
00:08:08 – The GIS Title Trap and Salary Bands
00:11:33 – Cold Emailing Into Crime Analytics and Hospital Research
00:14:16 – Starting to Create Content as a Working Data Scientist
00:18:50 – Push and Pull Factors for Leaving Corporate
00:21:28 – Adjusting to Life Without a Job as Input
00:27:33 – Vibe Coding: Lovable, Warp, and Claude Code
00:29:08 – The Hidden Risks of Vibe Coding (Security, Data Leaks)
00:32:08 – Using AI in Content Workflows
00:36:19 – Final Advice and Where to Find Maggie

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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

---

🚀 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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The future is spatial, but how do we actually make sense of the data? In this episode, we sit down with Emily Lisle from Dataplor to discuss the current state of location intelligence and how to overcome the biggest location intelligence challenges facing businesses today.

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

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📊 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/

📰 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

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

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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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

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/

📰 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

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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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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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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🌐 Website: https://forrest.nyc

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.

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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/

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