Spatial Stack with Matt Forrest

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

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 It’s an outdated, inefficient process that slows down analysis and limits scalability.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

📩 Get every update from my newsletter ➡️https://forrest.nyc ⬅️🔑 Sign up for Placekey here:https://placekey.ioMerging 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!

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 Will Lyon, a developer relations expert at Wherobots, and Ryan Avery, a machine learning engineer specializing in AI for geospatial imagery, as they dive into the realm of spatial analytics. They explore the breakthroughs in Apache Sedona’s visualization and data engineering capabilities. Discover how AI is revolutionizing electric vehicle charging infrastructure through spatial data analysis. They also tackle the challenges of managing large-scale geospatial data and discuss the latest features of Wherobots, including innovative tools for enhanced imagery analysis.

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.

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!

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 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/