The MapScaping Podcast - GIS, Geospatial, Remote Sensing, earth observation and digital geography

This episode examines the Common Space initiative, a non-profit project dedicated to building and launching high-resolution optical satellites designed specifically for humanitarian purposes, such as aiding populations at risk from climate events and conflict. Although there are over a thousand Earth observation satellites currently in orbit, high-resolution imagery remains largely inaccessible to humanitarians, journalists, and civil rights groups due to high costs, restrictive licensing, and the prioritization of defense and intelligence tasking. Common Space aims to bridge the gap between low-resolution public goods (like Landsat and Sentinel) and expensive commercial options by offering 50 to 70-centimeter resolution imagery with open licensing. The project plans to utilize a "club good" funding model, where humanitarian groups can access the data for free, while commercial and government entities pay to participate to fund the system's continued operations. How will a community-driven governance model successfully navigate the ethical risks and potential misuse of releasing high-resolution conflict data in real-time? Learn more about Commonspace here https://www.commonspace.world/ Or connect with the founders here https://www.linkedin.com/in/billfgreer/ https://www.linkedin.com/in/rhiannan-price/

I've been playing around with a lot of large language models lately, and it is absolutely fascinating to watch them work. But what happens when you bring that directly into QGIS? Right now, AI in the geospatial industry is a lot like a fast, enthusiastic new intern, incredibly helpful, and sometimes completely wrong, but improving at a rate that no human can compete with. As we hand more of our geoprocessing tasks over to these algorithms, and computing becomes more pervasive, are our own GIS skills becoming obsolete? Or are we just unlocking radically different opportunities to rethink our careers?

Geospatial Product Swiss Army Knife 1. The "Build It and They Won't Come" Trap We have all seen it: a talented geospatial professional spends months—perhaps years—perfecting a technically sophisticated web map or a niche data service, only to release it to a deafening silence. In our industry, the "build it and they will come" philosophy is a fast track to zero traction. Precision is the enemy of progress when it is applied to the wrong problem. Daniel and Stella Blake Kelly explored a remedy for this pattern. Stella—a New Zealand-born, Sydney-based strategist and founder of the consultancy Cartisan—didn’t start with a master plan. She "fell into" the industry after being inspired by a lecturer with bright blue hair and a passion for GIS that rivaled a Lego builder’s creativity. Today, she helps organizations move from "making things" to "building products that matter" using a framework she calls the Product Swiss Army Knife. -------------------------------------------------------------------------------- 2. The 7-Step Framework: More Than Just a Map Many geospatial experts suffer from a technology-first bias, prioritizing data accuracy over strategic utility. To counter this, Stella advocates for a disciplined, seven-tool toolkit designed to bridge the gap between GIS and Product Design: Vision: Establish a clear statement of what you are building and why it needs to exist. User Needs: Move beyond assumptions to identify real users and their specific friction points. Market & Context: Analyze the existing ecosystem (competitors, data, and workflows) to find your gap. Features: Ruthlessly prioritize "must-haves" to define a lean Minimum Viable Product (MVP). Prototypes & User Flows: Map out the user’s journey through the service before writing a line of code. Proof of Concept: Create a tangible, working version to prove the technical and market logic. Launch & Learn: Release early to gather real-world data and iterate based on evidence. This structure forces builders to treat the "spatial" element as a solution rather than the entire product. To illustrate User Needs (Tool #2), Stella suggests using formal User Stories to step out of the technical mindset: "As a solar panel marketer, I want to find potential customers with enough roof surface area so that I can reach out to them and provide an accurate quote." By grounding the project in a specific human problem, the developer stops building for themselves and starts building for the market. As Stella notes: "The thing about the product Swiss Army knife... is that it can be applied to almost any situation where there is an end consumer, where somebody is going to use the thing, the service that you make." -------------------------------------------------------------------------------- 3. The "200 Tools" Strategy: Programmatic Market Validation Daniel shared an unconventional approach to product discovery that serves as a masterclass in Market Context (Tool #3). Leveraging AI, he has built nearly 200 simple geospatial tools—such as a "Roof Area Calculator"—not as final products, but as a "sandbox" for discovery. This is Programmatic Market Validation. Instead of starting with a complex SaaS model, Daniel uses these micro-tools to find "winners" via organic search traffic. By observing where the internet already has unsolved spatial queries, he lets the market dictate which products deserve a full-scale build. In this new landscape, the barrier to entry has shifted: the competitive advantage is no longer "coding ability"—it is strategic experimentation. -------------------------------------------------------------------------------- 4. Not All Traffic is Equal: The High-Value Keyword Insight One of the most surprising takeaways from this experimentation is the direct link between specific geospatial problems and commercial value. A general GIS data tool might get thousands of views, but a "Roof Area Calculator" generates significantly higher programmatic advertising revenue. The reason? Market Context. The keyword "roofing" implies high-value intent; a user measuring their roof is likely in the market for a new one, making them incredibly valuable to advertisers. Understanding the commercial landscape surrounding a user's problem is the difference between a struggling hobby project and a viable MicroSaaS. -------------------------------------------------------------------------------- 5. The Precision Paradox: Why GIS Experts Struggle with UX There is a fundamental tension between the geospatial technical mindset and the product design mindset. GIS professionals are trained to be exact, precise, and correct. Designers, however, are taught to be wrong, gather feedback, and iterate. Daniel illustrated this with a "Hot Jar" anecdote. He once built a site where users were failing to move through the revenue funnel. Heat maps revealed the issue wasn't the data—it was the layout. Users weren't scrolling down far enough to see the critical action button.

Why Machine-Writing Code is the Best (and Most Dangerous) Thing for Geospatial: The current discourse surrounding AI coding is nothing if not polarized. On one side, the technofuturists urge us to throw away our keyboards; on the other, skeptics dismiss Large Language Models (LLMs) as little more than "fancy autocomplete" that will never replace a "real" engineer. Both sides miss the nuanced reality of the shift we are living through right now. I recently sat down with Matt Hansen, Director of Geospatial Ecosystems at Element 84, to discuss this transition. With a 30-year career spanning the death of photographic film to the birth of Cloud-Native Geospatial, Hansen has a unique vantage point on how technology shifts redefine our roles. He isn’t predicting a distant future; he is describing a present where the barrier between an idea and a functioning tool has effectively collapsed. The "D" Student Who Built the Future Hansen’s journey into the heart of open-source leadership began with what he initially thought was a terminal failure. As a freshman at the Rochester Institute of Technology, he found himself in a C programming class populated almost entirely by seasoned professionals from Kodak. Intimidated and overwhelmed by the "syntax wall," he withdrew from the class the first time and scraped by with a "D" on his second attempt. For years, he believed software simply wasn't his path. Today, however, he is a primary architect of the SpatioTemporal Asset Catalog (STAC) ecosystem and a major open-source contributor. This trajectory is the perfect case study for the democratizing power of AI: it allows the subject matter expert—the person who understands "photographic technology" or "imaging science"—to bypass the mechanical hurdles of brackets and semi-colons. "I took your class twice and thought I was never software... and now here I am like a regular contributor to open source software for geospatial." — Matt Hansen to his former professor. The Rise of "Vibe Coding" and the Fragmentation Trap We are entering the era of "vibe coding," where developers prompt AI based on a general description or "vibe" of what they need. While this is exhilarating for the individual, it creates a systemic risk of "bespoke implementations." When a user asks an AI for a solution without a deep architectural understanding, the machine often generates a narrow, unvetted fragment of code rather than utilizing a secure, scalable library. The danger here is a catastrophic loss of signal. If thousands of users release these AI-generated fragments onto platforms like GitHub, we risk drowning out the vetted, high-quality solutions that the community has spent decades building. We are creating a "sea of noise" that could make it harder for both humans and future AI models to identify the standard, proper way to solve a problem. Why Geospatial is Still "Special" (The Anti-meridian Test) For a long time, the industry mantra has been "geospatial isn’t special," pushing for spatial data to be treated as just another data type, like in GeoParquet. However, Hansen argues that AI actually proves that domain expertise is more critical than ever. Without specific guidance, AI often fails to account for the unique edge cases of a spherical world. Consider the "anti-meridian" problem: polygons crossing the 180th meridian. When asked to handle spatial data, an AI will often "brute force" a custom logic that works for a small, localized dataset but fails the moment it encounters the wrap-around logic of a global scale. A domain expert knows to direct the AI toward Pete Kadomsky’s "anti-meridian" library. AI is not a subject matter expert; it is a powerful engine that requires an expert navigator to avoid the "Valley of Despair." Documentation is Now SEO for the Machines We are seeing a counterintuitive shift in how we value documentation. Traditionally, README files and tutorials were written by humans, for humans. In the age of AI, documentation has become the primary way we "market" our code to the machines. If your open-source project lacks a clean README or a rigorous specification, it is effectively invisible to the AI-driven future of development. By investing in high-quality documentation, developers are engaging in a form of technical SEO. You are ensuring that when an AI looks for the "signal" in the noise, it chooses your vetted library because it is the most readable and reliable option available. From Software Developers to Software Designers The role of the geospatial professional is shifting from writing syntax to what Hansen calls the "Foundry" model. Using tools like GitHub Specit, the human acts as a designer, defining rigorous blueprints, constraints, and requirements in human language. The machine then executes the "how," while the human remains the sole arbiter of the "what" and "why." Hansen’s advice for the next generation—particularly those entering a job market currently hostile to junior engineers—is to abandon generalism.

How can you accurately aggregate and compare point-based data from different parts of the world? When analyzing crime rates, population, or environmental factors, how do you divide the entire globe into equal, comparable units for analysis? For data scientists and geospatial analysts, these are fundamental challenges. The solution lies in a powerful class of tools called Discrete Global Grid Systems (DGGS). These systems provide a consistent framework for partitioning the Earth's surface into a hierarchy of cells, each with a unique identifier. The most well-known systems, Google's S2 and Uber's H3, have become industry standards for everything from database optimization to logistics. However, these systems come with inherent trade-offs. Now, a new DGGS called A5 has been developed to solve some of the critical limitations of its predecessors, particularly concerning area distortion and analytical accuracy. Why Gridding the Globe is Harder Than It Looks The core mathematical challenge of any DGGS is simple to state but difficult to solve: it is impossible to perfectly flatten a sphere onto a 2D grid without introducing some form of distortion. Think of trying to apply a perfect chessboard or honeycomb pattern to the surface of a ball; the shapes will inevitably have to stretch or warp to fit together without gaps. All DGGS work by starting with a simple 3D shape, a polyhedron, and projecting its flat faces onto the Earth's surface. The choice of this initial shape and the specific projection method used are what determine the system's final characteristics. As a simple analogy, consider which object you’d rather be hit on the head with: a smooth ball or a spiky cube? The ball is a better approximation of a sphere. When you "inflate" a spiky polyhedron to the size of the Earth, the regions nearest the sharp vertices get stretched out the most, creating the greatest distortion. A Quick Look at the Incumbents: S2 and H3 To understand what makes A5 different, it's essential to have some context on the most popular existing systems. Google's S2: The Cube-Based Grid The S2 system is based on projecting a cube onto the sphere. On each face of this conceptual cube, a grid like a chessboard is applied. This approach is relatively simple but introduces significant distortion at the cube’s vertices, or "spikes." As the grid is projected onto the sphere, the cells near these vertices become stretched into diamond shapes instead of remaining square. S2 is widely used under the hood for optimizing geospatial queries in database systems like Google BigQuery. Uber's H3: The Hexagonal Standard Uber's H3 system starts with an icosahedron—a 20-sided shape made of triangles. Because an icosahedron is a less "spiky" shape than a cube, H3 suffers from far less angular distortion. Its hexagonal cells look more consistent across the globe, making it popular for visualization. H3's immense success is also due to its excellent and user-friendly ecosystem of tools and libraries, making it easy for developers to adopt. However, H3 has one critical limitation for data analysis: it is not an equal-area system. This was a deliberate trade-off, not a flaw; H3 was built by a ride-sharing company trying to match drivers to riders, a use case where exact equal area doesn't particularly matter. To wrap a sphere in hexagons, you must also include exactly 12 pentagons—just like on a soccer ball. If you look closely at a football, you'll see the pentagonal panels are slightly smaller than the hexagonal ones. This same principle causes H3 cells to vary in size. The largest and smallest hexagons at a given resolution can differ in area by a factor of two, meaning that comparing raw counts in different cells is like comparing distances in miles and kilometers without conversion. For example, cells near Buenos Aires are smaller because of their proximity to one of the system's core pentagons, creating a potential source of error if not properly normalized. Introducing A5: A New System Built for Accuracy A5 is a new DGGS designed from the ground up to prioritize analytical accuracy. It is based on a dodecahedron, a 12-sided shape with pentagonal faces that is, in the words of its creator, "even less spiky" than H3's icosahedron. The motivation for A5 came from a moment of discovery. Its creator, Felix Palmer, stumbled upon a unique 2D tiling pattern made of irregular pentagons. This led to a key question: could this pattern be extended to cover the entire globe? The answer was yes, and it felt like uncovering something "very, very fundamental." This sense of intellectual curiosity, rather than a narrow business need, is the foundation upon which A5 is built. A5's single most important feature is that it is a true equal-area system. Using a specific mathematical projection, A5 ensures that every single cell at a given resolution level has the exact same area. This guarantee even accounts for the Earth's true shape as a slightly flattened ellipsoid, no

The Open-Source Conundrum Many successful open-source projects begin with passion, but the path from a community-driven tool to a sustainable business is often a trap. The most common route—relying on high-value consulting contracts—can paradoxically lead to operational chaos. Instead of a "feast or famine" cycle, many companies find themselves with more than enough work, but this success comes at a cost: a fragmented codebase, an exhausted team, and a growing disconnect from the core open-source community. This episode deconstructs a proven playbook for escaping this trap: the strategic transition from a service-based consultancy to a product-led company. Through the story of Jeroen Ticheler and his company, GeoCat, we will analyze how this pivot creates a more stable business, a healthier open-source community, and ultimately, a better product for everyone.

Open-source software is often described as "free," a cornerstone of the modern digital world available for anyone to download, use, and modify. But this perception of "free" masks a growing and invisible cost—not one paid in dollars, but in the finite attention, time, and mounting pressure placed on the volunteer and community maintainers. This hidden tax is most acute when it comes to security. Jody from Geocat, a long-time contributor to the popular GeoServer project, pulled back the curtain on the immense strain that security vulnerabilities place on the open-source ecosystem. His experiences reveal critical lessons for anyone who builds, uses, or relies on open-source software.

What if communities could map their own worlds using low-cost drones and open AI models instead of waiting for expensive satellite imagery? In this episode with Leen from HOT (Humanitarian OpenStreetMap Team), we explore how they're putting open mapping tools directly into communities' hands—from $500 drones that fly in parallel to create high-resolution imagery across massive areas, to predictive models that speed up feature extraction without replacing human judgment. Key topics: Why local knowledge beats perfect accuracy The drone tasking system: how multiple pilots map 80+ square kilometers simultaneously AI-assisted mapping with humans in the loop at every step Localizing AI models so they actually understand what buildings in Chad or Papua New Guinea look like The platform approach: plugging in models for trees, roads, rooftop material, waste detection, whatever communities need The tension between speed and OpenStreetMap's principles Why mapping is ultimately a power game—and who decides what's on the map

This conversation with Jed Sundwall, Executive Director of Radiant Earth, starts with a simple but crucial distinction: the difference between data and data products. And that distinction matters more than you might think. We dig into why so many open data portals feel like someone just threw up a bunch of files and called it a day. Sure, the data's technically "open," but is it actually useful? Jed argues we need to be way more precise with our language and intentional about what we're building. A data product has documentation, clear licensing, consistent formatting, customer support, and most importantly - it'll actually be there tomorrow. From there, we explore Source Cooperative, which Jed describes as "object storage for people who should never log into a cloud console." It's designed to be invisible infrastructure - the kind you take for granted because it just works. We talk about cloud native concepts, why object storage matters, and what it really means to think like a product manager when publishing data. The conversation also touches on sustainability - both the financial kind (how do you keep data products alive for 50 years?) and the cultural kind (why do we need organizations designed for the 21st century, not the 20th?). Jed introduces this idea of "gazelles" - smaller, lighter-weight institutions that can move together and actually get things done. We wrap up talking about why shared understanding matters more than ever, and why making data easier to access and use might be one of the most important things we can do right now.

Reflections from the FOSS4G 2025 conference Processing, Analysis, and Infrastructure (FOSS4G is Critical Infrastructure) The high volume of talks on extracting meaning from geospatial data—including Python workflows, data pipelines, and automation at scale—reinforced the idea that FOSS4G represents critical infrastructure. AI Dominance: AI took up a lot of space at the conference. I was particularly interested in practical, near-term impact talks like AI assisted coding and how AI large language models can enhance geospatial workflows in QGIS. Typically, AI discussions focus on big data and earth observation, but these topics touch a larger audience. I sometimes wonder if adding "AI" to a title is now like adding a health warning: "Caution, a machine did this". Python Still Rules (But Rust is Chatting): Python remains the pervasive, default geospatial language. However, there was chatter about Rust. One person suggested rewriting QGIS in Rust might make it easier to attract new developers. Data Infrastructure, Formats, and Visualization When geospatial people meet, data infrastructure—the "plumbing" of how data is stored, organized, and accessed—always dominates. Cloud Native Won: Cloud native architecture captured all the attention. When thinking about formats, we are moving away from files on disk toward objects in storage and streaming subsets of data. Key cloud-native formats covered included COGs (Cloud Optimized GeoTIFFs), Zarr, GeoParquet, and PMTiles. A key takeaway was the need to choose a format that best suits the use case, defined by who will read the file and what they will use the data for, rather than focusing solely on writing it. The Spatial Temporal Asset Catalog (STAC) "stole the show" as data infrastructure, and DuckDB was frequently mentioned. Visualization is moving beyond interactive maps and toward "interactive experiences". There were also several presentations on Discrete Global Grid Systems (DGGS). Standards and Community Action Standards Matter: Standards are often "really boring," but they are incredibly important for interoperability and reaping the benefits of network effects. The focus was largely on OGC APIs replacing legacy APIs like WMS and WFS (making it hard not to mention PyGeoAPI). Community Empowerment: Many stories focused on community-led projects solving real-world problems. This represents a shift away from expert-driven projects toward community action supported by experts. Many used OSM (OpenStreetMap) as critical data infrastructure, highlighting the need for locals to fill in large empty chunks of the map. High-Level Takeaways for the Future If I had to offer quick guidance based on the conference, it would be: Learn Python. AI coding is constantly improving and worth thinking about. Start thinking about maps as experiences. Embrace the Cloud and understand cloud-native formats. Standards matter. AI is production-ready and will be an increasingly useful interface to analysis. Reflections: What Was Missing? The conference was brilliant, but a few areas felt underrepresented: Sustainable Funding Models: I missed a focus on how organizations can rethink their business models to maintain FOSS4G as critical infrastructure without maintainers feeling their time is an arbitrage opportunity. Niche Products: I would have liked more stories about side hustles and niche SAS products people were building, although I was glad to see the "Build the Thing" product workshop on the schedule. Natural Language Interface: Given the impact natural language is having on how we interact with maps and geo-data, I was surprised there wasn't more dedicated discussion around it. I believe it will be a dominant way we interact with the digital world. Art and Creativity: Beyond cartography and design talks, I was surprised how few talks focused on creative passion projects built purely for the joy of creation, not necessarily tied to making a part of something bigger.

Karl returns to the Mapscaping podcast to discuss his latest venture, Tyche Insights - a platform aimed at building a global community of geospatial storytellers working with open data. In this conversation, we explore the evolution from his previous company, Building Footprint USA (acquired by Lightbox), to this new mission of democratizing public data storytelling. Karl walks us through the challenges and opportunities of open data, the importance of unbiased storytelling, and how geospatial professionals can apply their skills to analyze and share insights about their own communities. Karl shares his vision for creating something akin to Wikipedia, but for civic data stories - complete with style guides, editorial processes, and community collaboration. Featured Links Tyche Insights: Main website: https://tycheinsights.com Wiki platform: https://wiki.tycheinsights.com Example project: https://albanydatastories.com Mentioned in Episode: USAFacts: https://usafacts.org QField Partner Program: https://qfield.org/partner Open Data Watch: (monitoring global open data policies)

AI Slop: An Experiment in Discovery Solo Episode Reflection: I'm back behind the mic after about a year-long break. Producing this podcast takes more time than you might imagine, and I was pretty burnt out. The last year brought some major life events, including moving my family back to New Zealand from Denmark, dealing with depression, burying my father, starting a new business with my wife, and having a teenage daughter in the house. These events took up a lot of space. The Catalyst for Return: Eventually, you figure out how to deal with grief, stop mourning the way things were, and focus on the way things could be. When this space opened up in my life, AI came into the picture. AI got me excited about ideas again because for the first time, I could just build things myself without needing to pitch ideas or spend limited financial resources. On "AI Slop": I understand why some content is called "slop," but for those of us who see AI as a tool, I don't think the term is helpful. We don't refer to our first clumsy experiments with other technologies—like our first map or first lines of code—as slop. I believe that if we want to encourage curiosity and experimentation, calling the results of people trying to discover what's possible "slop" isn't going to help. My AI Experimentation Journey My goal in sharing these experiments is to encourage you to go out and try AI yourself. Phase 1: SEO and Content Generation My experimentation began with generating SEO-style articles as a marketing tool. As a dyslexic person, I previously paid freelancers thousands of dollars over the years to help create content for my website because it was too difficult or time-consuming for me to create myself. Early Challenges & Learning: My initial SEO content wasn't great, and Google recognized this, which is why those early experiments don't rank in organic search. However, this phase taught me about context windows, the importance of prompting (prompt engineering), and which models and tools to use for specific tasks. Automation and Agents: I played around with automation platforms like Zapier, make.com, and n8n. I built custom agents, starting with Claude projects and custom GPTs. I even experimented with voice agents using platforms like Vappy and 11 Labs. Unexpected GIS Capabilities: During this process, I realized you can ask platforms like ChatGPT to perform GIS-related data conversions (e.g., geojson to KML or shapefile using geopandas), repro data, create buffers around geometries, and even upload a screenshot of a table from a PDF and convert it to a CSV file. While I wouldn't blindly trust an LLM for critical work, it's been interesting to learn where they make mistakes and what I can trust them for. AI as a Sparring Partner: I now use AI regularly to create QGIS plugins and automations. Since I often work remotely as the only GIS person on certain projects, I use AI—specifically talking to ChatGPT via voice on my phone—as a sparring partner to bounce ideas off of and help me solve problems when I get stuck. Multimodal Capabilities: The multimodal nature of Gemini is particularly interesting; if you share your screen while working in QGIS, Gemini can talk you through solving a problem (though you should consider privacy concerns). The Shift to Single-Serve Map Applications I noticed that the digital landscape was changing rapidly. LLMs were becoming "answer engines," replacing traditional search on Google, which introduced AI Overviews. Since these models no longer distribute traffic to websites like mine the way they used to, I needed a new strategy. The Problem with Informational Content: Informational content on the internet is going to be completely dominated by AI. The Opportunity: Real Data: AI is great at generating content, but if you need actual data—like contours for your specific plot of land in New Zealand—you need real data, not generated data. New Strategy: My new marketing strategy is to create targeted, single-serve map applications and embed them in my website. These applications do one thing and one thing only, using open and valuable data to solve very specific problems. This allows me to rank in organic search because these are problems that LLMs have not yet mastered. Coding with AI: I started by using ChatGPT to code small client-side map applications, then moved to Claude, which is significantly better than OpenAI's models and is still my coding model of choice. Currently, I use Cursor AI as a development environment, swapping between Claude code, OpenAI's Codex, and other models. A Caveat: Using AI for coding can be incredibly frustrating. The quality of the code drops dramatically once it reaches a certain scale. However, even with flaws, it’s a thousand times better and faster than what I could do myself, making my ideas possible. Crucially, I believe that for the vast majority of use cases, mediocre code is good enough. Success Story: GeoHound After practicing and refining my methods, I dec

Jonathan Wagner, CEO of Scribble Maps, is back on the podcast, and this time we're talking about Scribble—an AI agent he's built into his platform. Not a chatbot, an agent. There's a difference, and we get into that. https://mapscaping.com/podcast/the-business-of-web-maps/ So far, Scribble has access to 140 tools. It can view your map, select tools, build plugins, fetch data, and handle onboarding and customer education. But here's the thing—should you care? I think you should, because we're going to see more and more of these things. And whether you like it or not, for a lot of people, this is going to be the way they interact with geospatial data. I don't think we can put the genie back in the bottle. I personally, I'm not entirely sure I would if I could. Yeah, sure, there's a lot of uncertainty around what these things can do and how they're going to impact us. I get that. I feel it too. But we can't afford to stick our heads in the sand and pretend like it's not happening. In this conversation, Jonathan walks through why he built Scribble (spoiler: his wife was expecting and he needed to solve an onboarding problem), the real risks of adding AI to your product, and the technical decisions behind using Gemini over OpenAI. We also talk about privacy concerns, the Model Context Protocol (MCP), and what this all means for the future of GIS. We touch on the QGIS MCP server, the democratization of mapping tools, and when maps aren't actually the answer. It's an honest look at where we are with AI agents in geospatial, from someone who's actually building one. https://en.wikipedia.org/wiki/Lojban https://github.com/jjsantos01/qgis_mcp How's that?

Exploring the Evolution and Impact of Mapillary with Ed from Meta. Topics include Ed's journey with Mapillary, the process of uploading and utilizing street-level imagery, and the integration with OpenStreetMap. Ed talks about the challenges of mapping with various devices, the role of community contributions, and future potentials in mapping technology, such as using neural radiance fields (NeRFs) for creating immersive 3D scenes. The episode provides insights into how Mapillary is advancing geospatial data collection and usage. 00:00 Introduction to the Map Scaping Podcast 00:57 Meet Ed: Product Manager at Meta 02:09 Ed's Journey with Mapillary 03:59 What is Mapillary? 07:00 The Evolution of 360 Cameras 09:20 Uploading Imagery to Mapillary 14:10 Building a 3D Model of the World 19:10 Meta's Use of Map Data 21:24 The Importance of Community in Mapping 24:15 The Importance of Authoritative Data 24:49 Meta's Contributions to Open Source Geo World 25:27 Real-World Applications: Vietnam's B Group 28:16 Innovative Mapping in Detroit 31:38 Future of Mapping: Lidar and Beyond 32:20 Exploring Neural Radiance Fields (NeRFs) 35:40 Challenges and Innovations in Mapping Technology 45:25 Community Contributions and Future Directions 46:37 Closing Remarks and Contact Information Previous episodes that you might find interesting https://mapscaping.com/podcast/scaling-map-data-generation-using-computer-vision/ https://mapscaping.com/podcast/the-rapid-editor/ https://mapscaping.com/podcast/overture-maps-and-the-daylight-distribution/

Telematics Data is Reshaping Our Understanding of Road Networks In this episode MIT Professor Hari Balakrishnan explains how Cambridge Mobile Telematics (CMT) is transforming traditional road network analysis by layering dynamic behavioural data onto static map geometries. Telematics data creates "living maps" that go beyond traditional road geometry and attributes. By collecting movement data from 45 million users through phones and IoT devices, CMT has developed sophisticated models that can: - Generate dynamic risk maps showing crash probability for every road segment globally - Detect infrastructure issues that aren't visible in traditional mapping (like poorly placed bus stops) - Validate and correct map attributes like speed limits and lane connectivity - Differentiate between overpasses and intersections using movement patterns - Create contextual understanding of road segments based on actual usage patterns Particularly interesting for GIS professionals is CMT's approach to data fusion, combining traditional map geometry with temporal movement data to create predictive models. This has practical applications from infrastructure planning to autonomous vehicle navigation, where understanding the cultural context of road usage proves as important as precise geometry. The episode challenges traditional static approaches to road network mapping, suggesting that the future lies in dynamic, behavior-informed spatial data models that can adapt to changing conditions and usage patterns. For anyone working with transportation networks or smart city initiatives, this episode provides valuable insights into how movement data is changing our understanding of road infrastructure and spatial behaviour. Connect with Hari on LinkedIn! https://www.linkedin.com/in/hari-balakrishnan-0702263/ Cambridge Mobile Telematics https://www.cmtelematics.com/ BTW, I keep busy creating free mapping tools and publishing them there https://mapscaping.com/map-tools/ swing by and take a look!

In this week’s episode, I’m thrilled to welcome back Ariel Seidman, founder of HiveMapper. Ariel was my very first podcast guest back in 2019, and HiveMapper has come a long way since then! We explore how HiveMapper has evolved from a drone-based mapping system to a cutting-edge platform collecting street-level data at a global scale. Ariel shares the challenges of scaling large-scale mapping efforts, the pivot to building their own hardware, and the role of blockchain-based incentives in driving adoption. Here are just a few topics we cover: Why HiveMapper shifted focus from drones to street-level mapping. The power of combining hardware and software to solve mapping challenges. How HiveMapper has already mapped 28% of the global road network. The revolutionary edge computing and data filtering techniques driving efficiency. What it takes to compete with industry giants like Google Maps. Whether you're fascinated by the intersection of geospatial technology and innovation or looking for insights into scaling impactful startups, this episode is packed with value. Let me know your thoughts or hit reply if you’d like to discuss the episode! https://beemaps.com/ Connect with Ariel here https://www.linkedin.com/in/aseidman/ PS I have just finished creating a web-based tool that lets you explore and download OpenStreetMap data, It is a bit different from other tools and I would appreciate some feedback. https://mapscaping.com/openstreetmap-category-viewer/

Tracking elephants in Southern Africa’s Kavango-Zambezi (KAZA) region, the largest transfrontier conservation area in the world. Lead scientist Robin Naidoo from the World Wildlife Fund-US explains the complex, cross-border collaboration required to understand elephant movements across vast landscapes and the role of GNSS. Connected with Robin https://www.worldwildlife.org/experts/robin-naidoo Read more information about this study here https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2664.14746 https://news.mongabay.com/2024/09/jumbo-collaring-effort-reveals-key-elephant-movement-corridors/ Check out https://www.movebank.org/

Today's episode touches on some pretty big topics like Imposter Syndrome, Mentorship, Career Progression, Adaptability and Diversity Today you are going to hear two stories from two very different voices. Two brilliant people who happen to be women in geospatial. Ta Taneka https://www.linkedin.com/in/ta-taneka/ Mary Murphy https://www.linkedin.com/in/mary-murphy-12319433/ You can check out the GIS Directions Podcast here: https://esriaustralia.com.au/gis-directions-podcast or search for GIS Directions where every you listen to podcasts Recommended Podcast Episodes Getting where you want to go in your geospatial career Mentorship leadership and career advice Mentorship leadership and career advice

In this episode, Marco Bernasconi, co-founder and CEO of OPENGIS.ch, introduces us to QField, an open-source mobile application designed for field data collection in conjunction with QGIS. Marco shares his journey in developing QField and discusses its seamless integration with QGIS, allowing users to capture, survey, and manage geospatial data on various mobile devices. We also discuss the technical aspects of QField, including its user-friendly interface, the ability to connect with external sensors, and the recent introduction of QField Cloud for enhanced data synchronization and management. Marco highlights the application’s diverse use cases, from citizen science initiatives to archaeological documentation and utility inspections, demonstrating its potential to transform data collection processes across various industries. More information on Qfield: https://qfield.org/ https://qfield.cloud/ Or https://www.opengis.ch/#contact On a personal note, I have been working as a freelance Geospatial consultant for some time now and one of my projects is slowly winding down, which is why I am looking for new projects to get involved in! If you need expertise in Geospatial consultancy, GIS management or the marketing of geospatial products and services Please reach out! https://www.linkedin.com/in/danielodonohue/ or contact me here [email protected]

This is the story of Priscilla Cole, and what she did when she discovered that her ambitions were bigger than the tools she was using! Connect with Priscilla here! https://www.linkedin.com/in/priscilla-cole-5892549/ Recommended Listening The Way You Talk About Your Skills Is Costing You Money Geospatial Consulting As A Business And Career Mid-Life Career Change Getting Where You Want To Go In Your Career Applying For A Job, Getting Picked and Negotiating Mentorship Leadership And Career Advice

In this episode, I'm joined by Konstantine Klemmer, a researcher at Microsoft, to dive deep into the fascinating world of GeoAI. Konstantine introduces us to Satclip, a cutting-edge model that encodes geographic locations based on satellite images. We discuss how Satclip works, the data it uses, and its potential applications, particularly in low-resource settings and predictive modeling. Whether you're into AI, geography, or just curious about the intersection of these fields, this episode is packed with insights. Key Takeaways: What is Satclip?: Learn about Satclip's location encoding, a neural network that converts geographic coordinates into numerical representations based on satellite images. Data and Training: Understand how Satclip is trained using Sentinel-2 satellite images and how it captures unique geographic features. Applications: Discover how Satclip can be used in low-resource environments, such as on edge devices, and how it enhances other models by providing geographic context. The Future of GeoAI: Explore the potential future directions for Satclip, including more detailed regional models and the integration of multiple data modalities. Connect with Konstantine https://www.linkedin.com/in/konstantinklemmer/ Try Satclip https://github.com/microsoft/satclip Recommended Listening https://mapscaping.com/podcast/computer-vision-and-geoai/ https://mapscaping.com/podcast/planet-imaging-everything-every-day-almost/

In this episode, I welcome Jason Gilman, a Principal Software Engineer at Element 84, to explore the exciting world of natural language geocoding. Key Topics Discussed: Introduction to Natural Language Geocoding: Jason explains the concept of natural language geocoding and its significance in converting textual descriptions of locations into precise geographical data. This involves using large language models to interpret a user's natural language input, such as "the coast of Florida south of Miami," and transform it into an accurate polygon that represents that specific area on a map. This process automates and simplifies how users interact with geospatial data, making it more accessible and user-friendly. The Evolution of AI and ML in Geospatial Work: Over the last six months, Jason has shifted focus to AI and machine learning, leveraging large language models to enhance geospatial data processing. Challenges and Solutions: Jason discusses the challenges of interpreting natural language descriptions and the solutions they've implemented, such as using JSON schemas and OpenStreetMap data. Applications and Use Cases: From finding specific datasets to processing geographical queries, the applications of natural language geocoding are vast. Jason shares some real-world examples and potential future uses. Future of Geospatial AIML: Jason touches on the broader implications of geospatial AI and ML, including the potential for natural language geoprocessing and its impact on scientific research and everyday applications. Interesting Insights: The use of large language models can simplify complex geospatial queries, making advanced geospatial analysis accessible to non-experts. Integration of AI and machine learning with traditional geospatial tools opens new avenues for research and application, from environmental monitoring to urban planning. Quotes: "Natural language geocoding is about turning a user's textual description of a place on Earth into a precise polygon." "The combination of vision models and large language models allows us to automate complex tasks that previously required manual effort." Additional Resources: Element 84 Website State of the Map US Conference Talk on YouTube Blog Posts on Natural Language Geocoding Connect with Jason: Visit Element 84's website for more information and contact details. Google "Element 84 Natural Language Geocoding" for additional resources and talks.

This podcast episode is all about semantic search and using embeddings to analyse text and social media data. Dominik Weckmüller, a researcher at the Technical University of Dresden, talks about his PhD research, where he looks at how to analyze text with geographic references. He explains hyperloglog and embeddings, showing how these methods capture the meaning of text and can be used to search big databases without knowing the topics beforehand. Here are the main points discussed: Intro to Semantic Search and Hyperloglog: Looking at social media data by counting different users talking about specific topics in parks, while keeping privacy in mind. Embeddings and Deep Learning Models: Turning text into numerical vectors (embeddings) to understand its meaning, allowing for advanced searches. Application Examples: Using embeddings to search for things like emotions or activities in parks without needing predefined keywords. Creating and Using Embeddings: Tools like transformers.js let you make embeddings on your computer, making it easy to analyze text. Challenges and Innovations: Talking about how to explain the models, deal with long texts, and keep data private when using embeddings. Future Directions: The potential for using embeddings with different media (like images and videos) and languages, plus the ongoing research in this fast-moving field. Connect with Dominik Weckmüller here https://geo.rocks/ Stay up to date with AI here https://huggingface.co/ Try searching for “map” here https://huggingface.co/spaces Check out this project I am working on https://quickmaptools.com/

In this episode, we welcome back Lauren Guy, CEO and founder of ASTERRA, a groundbreaking company using L band and synthetic aperture radar (SAR) for commercial purposes. Lauren shares his journey as a geophysicist and discusses the innovative applications of L band in detecting water leakages, soil moisture, and even minerals from space. Dive deep into the technical, commercial, and environmental aspects of SAR technology and learn about the future potential of this exciting field. **Key Topics Covered:** **Introduction to Astera**: - Overview of Asterra's mission and Lauren Guy's background as a geophysicist. - The unique use of L band and SAR for commercial applications. **Understanding L Band and Synthetic Aperture Radar (SAR)**: - Explanation of the electromagnetic spectrum and how L band fits in. - Advantages of L band, including its ability to penetrate the ground. **Technical Insights into SAR**: - Detailed discussion on polarizations, signal processing, and the electrical properties of materials detected by SAR. - Comparison between L band and other bands like X and C band. **Applications and Benefits of L Band**: - Real-world examples of how Astera uses L band for water leak detection and soil moisture mapping. - Discussion on the environmental and commercial impact of these applications. **Challenges and Limitations**: - Addressing issues such as noise interference from cell phones and radars. - Limitations in resolution and the complexities of SAR technology. **Case Studies and Success Stories**: - Success stories including the detection of 118,000 water leakages worldwide and the discovery of significant lithium deposits. **Business Strategies and Market Penetration**: - Insights into ASTERRA's business model, customer education, and market challenges. - Strategies for overcoming barriers and building trust with clients. **Future Aspirations and Technological Developments**: - Plans for launching their own satellites to ensure reliable data sources. - The role of AI in enhancing SAR capabilities and improving detection accuracy. **Entrepreneurial Advice for Remote Sensing Practitioners**: - Lauren’s advice for remote sensing scientists and entrepreneurs in the industry. - The importance of data feedback loops and the journey from a 20% to an 86% success rate in detections. **Guest Information:** - **Lauren Guy**: CTO and founder of ASTERRA. Connect with Lauren on https://www.linkedin.com/in/lauren-guy-asterra/ **Company Information:** - **ASTERRA**: Learn more about ASTERRA’s innovative solutions at https://asterra.io/ **Additional Resources:** - Check out Lauren’s previous appearance on the podcast for more insights into SAR technology. - Explore ASTERRA’s groundbreaking work in remote sensing and their various applications across different industries. **Episode Highlights:** - "We can find water leakages from space and distinguish treated water from other types of water based on their dielectric properties." - "ASTERRA has verified, dug, and fixed 118,000 leakages across 65 countries using L band SAR technology." - "Our success rate has increased from 20% to around 86% thanks to the integration of AI and continuous data feedback." **Support the Show:** - If you enjoyed this episode, please leave a review on your favourite podcast platform and share it with your network. Thank you for tuning in to the MapScaping Podcast! Recommended Listening Finding Water Leaks From Space Introduction To Synthetic Aperture Radar-SAR Flood Monitoring From Space ( using SAR)

Cloud-native geospatial, range requests, chucks, COGs and COPCs ... [ insert confusing acronym here ] Sometimes It feels like we need to learn a whole new vocabulary and if you have been doing #geo for a while you might be wondering how much of this is actually going to impact me. What bits of this are the ones that I need to know about? I don’t think that anyone is going to be talking about cloud native in 10 years, in the same way, no one talks about digital cartography or computer analysis because where else would you do your cartography? And how else would you do your analysis? Maybe the names won’t be as important but the concepts will and while this episode is focused on Geoparquet it does so within the context of cloud-native geospatial - and this concept is not going away! You can connect with Kyle Barron here https://x.com/kylebarron2 or here https://kylebarron.dev/ If you want to learn more about cloud-native geospatial I can highly recommend these episodes https://mapscaping.com/podcast/cloud-optimized-point-clouds/ https://mapscaping.com/podcast/introduction-to-cloud-native-geospatial/ https://mapscaping.com/podcast/planet-scale-tiled-maps-without-a-server/ https://mapscaping.com/podcast/what-is-modern-gis/ https://mapscaping.com/podcast/the-planetary-computer/ I am working on a new project called https://quickmaptools.com/ like the name suggests it is a bunch of browser-based map tools. So far we have created several different conversion tools and will continue to add more to the list. Check it and let me know what you think!

Mappedin started as a school project and evolved into a leading indoor mapping company, working with malls, airports, hospitals, and Fortune 500 companies. You guessed, today's podcast is all about indoor mapping, why it's hard, what are the use cases driving it, what the state of the art looks like today and what we can expect in the future. Key points discussed include: 1. **The Challenge of Indoor Mapping**: Unlike outdoor mapping, indoor environments are complex due to the density of objects and frequent changes. Moreover, indoor spaces are mostly private property, making it difficult to collect data comprehensively. 2. **Importance of Indoor Maps**: Despite the challenges, the need for indoor maps is growing. Applications range from wayfinding in malls and airports to optimizing logistics in warehouses and creating better guest experiences in various venues. 3. **Mappedins's Approach**: enabling non-experts to create and maintain indoor maps. Their tools are designed for everyday users, allowing them to update maps as easily as they would a document in Google Docs. 4. **Technological Advances**: While technologies like LiDAR and digital twins offer detailed 3D models, are they really practical? . 5. **Indoor Positioning**: Accurate indoor positioning is crucial for the success of indoor maps, similar to how GPS revolutionized outdoor mapping. However, this remains a challenging area due to signal interference and the complexity of indoor spaces. 6. **Future Outlook**: Digital indoor maps become as ubiquitous as Wi-Fi, providing essential data for various applications and improving overall user experience ... but we are not there yet! Try Mappedin for yourself https://www.mappedin.com/ or connect with Hongwei here https://www.linkedin.com/in/hongweil/ Recommended Listening Where does the blue dot come from ( how Google knows your location ) Hyper Accurate Indoor Location Using the Geomagnetic fields of buildings to navigate indoors I am working on a new project https://quickmaptools.com/ and would really appreciate some feedback!

Hugo Powell, from immap.org shares his expert insights on how GIS technologies are leveraged to analyze data, visualize scenarios, and facilitate rapid decision-making during emergencies. Here are the key tools mentioned: 1. **Kobo Toolbox**: An open-source tool used for data collection in humanitarian contexts. Kobo Toolbox allows for both quantitative and qualitative data collection and is operational offline, which is crucial in areas with limited internet connectivity. It supports geospatial data collection and can be used for needs assessments in settings like refugee camps. 2. **ODK Collect**: Similar to Kobo Toolbox, ODK Collect is an open-source mobile application used for field data collection. It is widely used in humanitarian efforts for its ease of use and the capability to work offline. 3. **QGIS**: A free and open-source geographic information system used for viewing, editing, and analyzing geospatial data. Hugo notes that QGIS is core for mapping and data analysis in humanitarian operations. 4. **Tableau and Power BI**: Business intelligence tools mentioned for their use in analyzing and visualizing data. These tools help in making data-driven decisions during humanitarian operations. 5. **Humanitarian Data Exchange (HDX)**: An open platform for sharing data across crises and organizations, which helps in avoiding duplication of efforts and enhances coordination among humanitarian actors. 6. **Humanitarian OpenStreetMap Team (HOT)**: Provides crowdsourced geospatial data which is extremely valuable in humanitarian settings for its accuracy and timeliness. 7. **Esri’s Living Atlas and other Esri tools**: While not open-source, Esri’s tools are sometimes used for their comprehensive geospatial data, particularly in natural disaster contexts like earthquakes. 8. **Humanitarian Spatial Data Center**: Managed by IMAP, this tool aggregates and processes data, providing access to data, analytics, and visualization tools all in one place. It has been particularly successful in deployments like Afghanistan. This episode was sponsored by scribblemaps.com https://youtu.be/CDkG9eS6H2M Recommended Listening Geospatial Support For Humanitarian Emergencies A Self-Contained Environment For Open-Source Geospatial Tools GGIS Offline And In The Field The Business Of Web Maps Peer to Peer Mapping And Digital Democracy I am working on a new project over at QuickMapTools.com and any feedback is really appreciated!

AI Autocomplete for QGIS Brendan Ashworth the CTO and co-founder of https://buntinglabs.com/ focuses on integrating AI with QGIS, and today on the podcast we are talking about Autocomplete for vectorization. Along the way Brendan will share with us why Bunting Labs chose to build this on top of QGIS, the Challenges in Map Digitization, what the development process was like and how this is different from tools like Segment Anything ( from meta ) Here's what we discussed: Introduction to Bunting Labs: Get to know more about Brendan and Bunting Labs, whose mission revolves around enhancing QGIS with AI, especially focusing on automating vectorization processes. AI Autocomplete for Vectorization: We explored the AI autocomplete feature developed by Bunting Labs that simplifies the vectorization of maps in QGIS, streamlining the digitization process for better efficiency. Brendan’s Background and Motivation: Brendan shared his journey from a software engineer to a pivotal player in the geospatial sector, spurred by a project that showcased the potential of merging geospatial data with machine learning. Why Choose QGIS?: Discover why Bunting Labs opted for QGIS over other GIS platforms, with an emphasis on its open-source nature and vibrant community ecosystem. Challenges in Map Digitization: Our conversation covered the technical challenges involved in developing AI capable of accurately understanding and digitizing maps. Iterative Development and Learning: Brendan highlighted the evolutionary process of their AI model, which has significantly improved from its early versions. AI vs. Segment Anything: Brendan explained how their AI autocomplete tool differs from existing solutions like Segment Anything, particularly in handling specific digitizing challenges. The Future of AI in Geospatial Data Analysis: We discussed potential future applications of AI in geospatial data, including automatic georeferencing and metadata extraction. Privacy Considerations: We also touched on the importance of privacy in the development and deployment of AI technologies in geospatial data analysis. Changing the Geospatial Landscape: Brendan shared his vision for using geospatial data not just to map the current world but to plan and improve future landscapes. Sponsored by https://www.scribblemaps.com/ Recommended Listening https://mapscaping.com/podcast/the-business-of-web-maps/ https://mapscaping.com/podcast/the-business-of-qgis-development/ https://mapscaping.com/podcast/qgis-offline-and-in-the-field/ https://mapscaping.com/podcast/computer-vision-and-geoai/ https://quickmaptools.com/ - MapTools to save your time processing GIS data

GNSS receivers - why precise positioning will not be coming to your phone any time soon Igor is the CEO and cofounder of Emlid.com a company that started out making high-precision GNSS receivers in his kitchen and crowd-funded the first batch on Kickstarter. But that was over ten years ago so today on the podcast Igor is going to tell us about the innovations that made this possible, give a great explanation of RTK and PPP and explain why we should expect to see high precision positioning on your phone any time soon. Connect with Igor here: https://www.linkedin.com/in/igor-vereninov-52a73ab0/ Or visit https://emlid.com/ In this episode, we cover: **Introduction to Emlid and its Focus**: Introduction to the company Emlid, its CEO and co-founder Igor, and their focus on high precision GNSS receivers and software designed for centimeter accuracy positioning. **Startup Story and Crowdfunding**: The origin story of Emlid, starting from working with drones in university, the need for accurate maps, the initial challenges with high precision GPS technology, the development of their own GNSS receiver, and their successful crowdfunding campaign on Kickstarter. **Innovations in GNSS Technology**: How Emlid managed to make high precision GNSS technology more affordable and accessible, the role of open-source software, and the technical innovations that allowed them to reduce the cost and size of GNSS receivers. **Market and Technology Evolution**: The discussion on how the market for GNSS technology has evolved, including the impact of autonomous cars on the development and availability of multi-frequency GNSS chips, and how these advancements benefited broader applications beyond surveying and construction. **Precision vs. Accuracy in GNSS**: An explanation of the difference between precision and accuracy in the context of GNSS technology, and the significance of each in various applications like drone mapping and volume measurements. **RTK and PPP Explained**: A detailed explanation of Real-Time Kinematic (RTK) and Precise Point Positioning (PPP), including how they work, their applications, and their advantages and limitations. **The Future of GNSS Technology**: Insights into the future directions of GNSS technology, including the challenges and potentials for achieving sub-centimeter accuracy with smartphones, the complementarity of GNSS and visual positioning systems, and the potential for GNSS technology to replace traditional surveying methods. **Global Utility of GNSS**: A discussion on the importance of GNSS as a global utility, its indispensable role in modern technology and everyday life, and the potential consequences of GNSS failures. More GNSS-related podcast episodes! From GNSS to VPS Reimaging GPS How Google Knows Your Location Past, Present and Future of GNSS SBAS - A base station in the sky

Refactoring the Way you Talk About your geospatial skills: It is Costing you Money Some of the key topics in this episode 1.Our Geospatial Skills and Marketability: There's a realization that while our traditional geospatial skills are valuable, they might not always be marketed effectively to match the broader IT and data analysis job markets. We discuss the benefit of framing our skills in terms that are more widely recognized outside the niche of geospatial technology, such as data science or IT. 2.The Spatial Discount: We explore the concept of the spatial discount, which refers to the observation that geospatial professionals might face a disparity in compensation compared to their counterparts in more generalized IT roles, despite having highly transferable and valuable data manipulation skills. 3. Skill Development and Adaptation: The importance of continually developing skills that are not only advanced within the geospatial domain but also marketable across various sectors is emphasized. Learning and mastering technologies that have broad applications, such as SQL for spatial data manipulation, can enhance our versatility and marketability. 4. Communication and Marketing Skills: Our ability to effectively communicate and market our skills is highlighted as crucial for career advancement. We are encouraged to adopt the language and terminology that resonate with broader industries and potential employers, moving beyond the jargon of the geospatial field. 5. Finding Value in Our Geospatial Work: The discussion also touches on the importance of identifying and articulating the real-world value of our geospatial work. We should focus on how our skills can solve practical problems and address the needs of businesses and organizations, rather than solely on the technical complexity of our tasks. 6. Professional Development: Lastly, the conversation advocates for a proactive approach to our professional development, suggesting that we should seek out opportunities to learn new skills and technologies that align with market demands and personal interests. These points collectively suggest a strategy for us, as geospatial professionals, to enhance our career prospects: by broadening our skill sets, effectively marketing our capabilities, and aligning our work with the needs and language of the wider IT and data analysis fields. Connect with Brain Timoney on LinkedIn Thank you to our sponsors https://www.scribblemaps.com/ https://merginmaps.com/ Recommended Listening Modern Geospatial Rebranding Gis and Geospatial Getting Where You Want To Go In Your Geospatial Career Mid-Career Change

Modern geospatial - not the bleeding edge of geospatial but modern geospatial - what is it? Well my guest Will Cadell, the CEO of SparkGeo describes modern geospatial as the intersection of the cloud, smart space, open source data/standards, AI and smart devices - that's modern geospatial And as you will hear during the discussion it's important to understand the difference between modernisation and innovation when we think about moving people from where they are now to where they want to be with regards to their geospatial capabilities. You might be wondering - what does any of this have to do with me? I just want to make better things, I just want to help people use all this awesome geospatial stuff … but you don’t get to do that without first understanding what “better” looks like for them - what is their version of awesome geo stuff … and that is why you should listen to this episode! Connect with Will Cadell Twitter https://twitter.com/geo_will LinkedIn https://www.linkedin.com/in/willcadell/ SparkGeo https://sparkgeo.com/ https://www.strategicgeospatial.com/ This episode is sponsored by https://www.scribblemaps.com/ Recommended Listening The Business of Web Maps https://mapscaping.com/podcast/the-business-of-web-maps/ Modern GIS https://mapscaping.com/podcast/what-is-modern-gis/

The main topics discussed during this episode include: Basics of LIDAR data and its applications. Differences between LIDAR and photogrammetry. Processing chain of LIDAR data. Challenges in classifying point clouds. Applications of LIDAR technology in vegetation mapping, terrain modelling, and infrastructure inspection. The future of LIDAR technology includes the potential for more affordable and widespread use. Importance of automated processing tools for handling large volumes of data. Connect with Nejc Dougan here: https://www.linkedin.com/in/nejcdougan/ or at https://www.flai.ai/ Recommended listening Cloud Optimized Point Clouds https://mapscaping.com/podcast/cloud-optimized-point-clouds/ PDAL - the point data abstraction library https://mapscaping.com/podcast/pdal-point-data-abstraction-library/ Lidar from drones https://mapscaping.com/podcast/lidar-from-drones/ Bathymetric Lidar https://mapscaping.com/podcast/bathymetric-lidar-and-blue-carbon/

Alex Leith is a Digital Earth Architect and in this episode, you will learn what Infrastructure as code is - hint it is the opposite of the "clicky-clicky" and so much more! Connect with Alex here https://auspatious.com/ Recommended Listening Cloud-Optimized Point CLounds https://mapscaping.com/podcast/cloud-optimized-point-clouds/ Cloud Native Geospatial https://mapscaping.com/podcast/cloud-native-geospatial/ Planet Scale Tiled Maps without a Server https://mapscaping.com/podcast/planet-scale-tiled-maps-without-a-server/ What is Modern GIS https://mapscaping.com/podcast/what-is-modern-gis/

GeeMap is an open-source Python library that provides tools for interactive mapping with Google Earth Engine (GEE), which is a platform for earth science data and analysis ... and today you are going to hear from the creator of GeeMap! Connect with Qiusheng Wu here: https://wetlands.io/ This episode is sponsored by Planet learn more at https://www.planet.com/gis/ Recommended Listening Introduction to Google Earth Engine https://mapscaping.com/podcast/introducing-google-earth-engine/ Introduction to Sentinel Hub https://mapscaping.com/podcast/sentinel-hub/ Planet - Imaging everything every day ( almost ) https://mapscaping.com/podcast/planet-imaging-everything-every-day-almost/ Introduction to Microsoft's Planetary Computer https://mapscaping.com/podcast/the-planetary-computer/

GPS reimagined? Why do we need to reimagine GPS? ... Is it broken? Recommended Podcast Episodes How Google Calculates Your Location https://mapscaping.com/podcast/how-google-calculates-your-location/ From GNSS To VPS https://mapscaping.com/podcast/from-gnss-to-vps/ Navigating The Past Present and Future of GNSS https://mapscaping.com/podcast/navigating-the-past-present-and-future-of-gnss/ SatelliteBased Augmentation System - A Base Station In The Sky https://mapscaping.com/podcast/satellite-based-augmentation-system-a-base-station-in-the-sky/

Nyall Dawson is a QGIS developer, cartographer, and owner and founder of North Road, a company specializing in open-source geospatial software. His journey into geospatial began with personal interests in mapping and cartography, which later evolved into a business called North Road. But that's not why I wanted to make this episode for you, I wanted to share this story with you because it could be your story too. You could decide to have a story that starts with contributing to something you care about, which leads to you becoming a known expert within a community that cares about the same thing and evolves into paid opportunities. That could be your story too! You can connect with Nyall here: https://twitter.com/nyalldawson https://www.linkedin.com/in/nyall-dawson-18b6016a/ Sponsored by Planet Learn more at www.planet.com/gis Recommended Podcast Episodes Planet https://mapscaping.com/podcast/planet-imaging-everything-every-day-almost/ Monetizing an open-source geospatial project https://mapscaping.com/podcast/monetizing-an-open-source-geospatial-project/ Being self-employed in Earth Observation https://mapscaping.com/podcast/being-self-employed-in-the-earth-observation-sector/ Geospatial Side Hustles https://mapscaping.com/podcast/geospatial-side-hustles/ Self Employment in the GIS Industry https://mapscaping.com/podcast/self-employment-in-the-gis-geospatial-industry/ A Business built on Open Source GIS https://mapscaping.com/podcast/a-business-built-on-open-source-gis/

This episode is all about making beautiful maps ... I am not a cartographer but my guest Mamata Akella is a professional cartographer at Felt! So today on the podcast we are talking about Essential Elements of Map Design: Which of course starts with questions like - who is it for, what is it for and how do we get it to them? And then moves on to Visual Hierarchy, Zoom-Based Styling, Color Palettes, and Interpretation We discuss a few practical examples during the conversation and you can find links to those in the show notes Recommended Listening https://mapscaping.com/podcast/felt-upload-anything/ https://mapscaping.com/podcast/communicating-with-maps-the-art-of-cartography/ https://mapscaping.com/podcast/full-stack-cartography/

Planet manufactures and manages the world’s largest constellation of earth observation satellites! Imaging “Just about everywhere on earth just about every day – Making change visible, accessible, and actionable” … and the hope of this episode is to help you understand how they do that – along the way you will hear about their two constellations and how they work together Learn the difference between ghost ships and dark ships and find out that there are very few ground control points in the ocean and why that matters Find out what this means for GIS and permit enforcement. For more information go to https://www.planet.com/gis/ Recommended Podcast Episodes Hyperspectral vs. Multispectral https://mapscaping.com/podcast/hyperspectral-vs-multispectral/ NICFI Program https://mapscaping.com/podcast/reduce-and-reverse-tropical-forest-loss-with-nicfi/ Synthetic Data https://mapscaping.com/podcast/synthetic-data-for-real-problems/ Labels Matter https://mapscaping.com/podcast/labels-matter/

This episode is a story about wide-angle imaging for fire mapping and maritime search but it's also a story about changing the culture and getting people to trust a new way of doing things. My guest today is Alison Harrod - mission success manager at a start-up called Overwatch imaging Whenever I work with a company like Overwatch Imaging it is hard to know which story to tell, we could just as easily have made an episode about AI and object detection or about smart sensors because they do those things too. The decision depends on the guest and their background so after meeting Alison we decided to make this episode for you and try to give you a broad overview of what wide-angle imaging is and how it's used in the context of fire mapping and maritime search. ...but It's one thing to have a technology and it is another thing entirely to get people to use it … as you will hear fire mapping is not “a move fast and break things kind of situation” Connect with Alison here! https://www.linkedin.com/in/alisonharrod/ https://www.overwatchimaging.com/ Other relevant podcast episodes that you might enjoy Thermal Imagery From Space https://mapscaping.com/podcast/thermal-imagery-from-space/ Finding Water Leaks From Space https://mapscaping.com/podcast/finding-water-leaks-from-space/ Cube Satellites Of The Stratosphere https://mapscaping.com/podcast/cube-satellites-of-the-stratosphere/

It's not about becoming an influencer it's about creating opportunities for yourself In this episode, we tackle the common misconception that personal branding is solely for influencers, revealing how it's actually about creating the right visibility and opportunities in your professional sphere. Helena Merschdorf shares her unique insights, drawing from her rich background in GIS and marketing, and discusses: Solving the Obscurity Problem: Discover how personal branding can help you get noticed by the right people, not just everyone. Effective Communication in Technical Fields: Learn the art of conveying complex GIS concepts to non-experts. Defining and Building Your Personal Brand: Uncover the essence of personal branding and how to strategically develop it. Overcoming Challenges: Helena offers guidance on tackling imposter syndrome and finding your niche in the vast world of GIS. Choosing the Right Platforms: Get tips on selecting the best channels for your personal branding efforts based on your target audience. Success Stories and Strategies: While specific examples aren't named, learn about the different levels of personal branding success and what might work for you. This episode is not just about building a personal brand; it's about leveraging that brand to carve a unique path in the geospatial industry. Connect with Helena here https://www.linkedin.com/in/helenamerschdorf/ https://www.tales.co.nz/ Other relevant podcast episodes Rebranding GIS and Geospatial https://mapscaping.com/podcast/rebranding-gis-geospatial/ Python Maps https://mapscaping.com/podcast/python-maps/ Getting Your Dream Job In Earth Observation https://mapscaping.com/podcast/getting-your-dream-job-in-earth-observation/

Entity resolution is the process of matching and merging records from different sources that refer to the same entity. today's episode is about entity resolution for place data, why you might want to do that, and what any of this has to do with the dollar, Unix time and the idea that If data is really driving innovation, join keys are going to become more valuable. Today's guest is Auren Hoffman https://www.linkedin.com/in/auren/ https://twitter.com/auren https://www.youtube.com/@worldofdaas If you want to try Placekey for yourself go to https://www.placekey.io/ If you want to learn more about SafeGraph listen to this podcast episode https://mapscaping.com/podcast/building-geospatial-truth-sets/

Embracing Open-Source Geospatial Technology is easy as an individual but what if you want your organization to use FOSS4G How do you get strategic buy-in? It turns out that the software does not sell itself and that even in the age of AI we still have to convince a human if we want organizational change to to happen. I think the temptation is to say hey look at this long list of specifications and notice how FOSS4G is often better or equal to the close source equivalent. Or hey look at the price tag … it costs nothing which is way cheaper than this other thing which costs more than nothing. While this might be all the argumentation you need in some cases … in general, making change happen is hard, and it's going to require more than that. That's why I have invited Todd Barr back on the podcast to walk us through what it takes to get an organization to Embrace Open-Source Geospatial Technology. Here are a few of the key points Getting buy-in for open-source software and addressing concerns about security and IP protection Perspectives of External and internal stakeholders on open-source Software Importance of collaboration, empathy, and understanding in decision-making and stakeholder management Challenges of implementing open source technology in a corporate environment Benefits of using open source solutions, such as faster analysis, increased stability, and flexibility for innovation Accessibility and support in open source communities, including direct interaction with developers and availability of external consultants Customization and development work required for creating vertical solutions with open-source components Finding skilled developers and training them in geospatial technology Cost-saving advantages of open source technology in cloud computing Leading arguments for implementing open source software: cost savings and freedom to modify and customize Advocacy for supporting and integrating with the open source community. The last time Todd was on the podcast we talked about Leadership and Mentorship in the Geospatial community https://mapscaping.com/podcast/skills-leadership-mentorship-and-the-geospatial-community/ If you are interested in FOSS4G you might enjoy these previous episodes A Business built on Open Source GIS https://mapscaping.com/podcast/a-business-built-on-open-source-gis/ Monetizing an open-source geospatial project https://mapscaping.com/podcast/monetizing-an-open-source-geospatial-project/ Or just scroll through the archive to find episodes about QGIS, PostGIS, Geoserver, Geonode, Python, and a bunch of other open-source projects I could use some support! please consider supporting this podcast on Patreon https://www.patreon.com/MapScaping Some more episodes you might enjoy ESRI, GIS careers, Geospatial Data Science QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

** Warning** Consuming this content may lead to educated opinions and or a better understanding of the future of location technology! ** Proceed with caution!! ** If are curious about any of the following topics this episode is for you! Evolution of Positioning Systems Terrestrial-based Positioning: The role of Wi-Fi positioning and the potential of 5G in positioning. Visual Positioning Systems (VPS) GNSS Low Earth Orbit (LEO) Satellites: The potential of LEO satellites in enhancing positioning and navigation. Future of Positioning: Predictions and expectations for the future of navigation and positioning technologies. Connect with Sandy on LinkedIn https://www.linkedin.com/in/sandy-kennedy-569a6a4/ Recommended listening SBAS - Satellite-based augmentation system https://mapscaping.com/podcast/satellite-based-augmentation-system-a-base-station-in-the-sky/ GNSS - past present and future https://mapscaping.com/podcast/navigating-the-past-present-and-future-of-gnss/ Where does Goolge's blue dot come from https://mapscaping.com/podcast/how-google-calculates-your-location/ On the personal front, I have just moved back to New Zealand after 13 years in Denmark. It has been pretty busy the last couple of weeks, hence the lack of published podcast episodes. Some more episodes you might enjoy ESRI, GIS careers, Geospatial Data Science QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

In this podcast episode, Jennings Anderson, a research scientist at Meta, discusses the Overture Maps Foundation, a downstream product of OpenStreetMap. He explains his background in open map data and his interest in studying collaboration within the OpenStreetMap community. Jennings then dives into the Daylight Distribution, an open data product produced by Meta, and how it combines building data sets from various sources into one unified theme. Jennings emphasizes the importance of a stable ID system within the Overture Maps Foundation and the potential for easy conflation and integration of third-party data. Jennings also explains the relationship between OpenStreetMap and Overture Maps, highlighting how they complement each other. Relevant podcast episodes OpenStreetMap Is A Community Of Communities Cloud Native Geospatial Cloud Optimized Point Clouds The Rapid Editor With regards to accessing Overture Map data, you might find this YouTube video helpful https://youtu.be/fZj6kTwXN1U?feature=shared Just in case you are interested in the Google building footprints here is a link to that :) https://sites.research.google/open-buildings/ Some more episodes you might enjoy ESRI, GIS careers, Geospatial Data Science QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

100 billion Points Every Day 100 billion is a very large number, let's say that I gave you a spreadsheet with 100 billion rows in it, each row consisted of five columns Latitude, Longitude, Device ID, A Timestamp, and a column telling the name of the data provider What would you do with that? How would you clean it? Make sense of it? Extract value from it? What would people use it for? And how would you do this in a way that could be systematized? FourSquare does this every day with the help of something they call a movement engine. To help understand more about how they do this I have invited Gabriel Durkin the director of data science on the podcast. This is the last in a series of episodes I have worked on together with FourSquare and I have to say it's been really enjoyable working with them. If you are interested in hearing some of the previous episodes just check out the links below! From Pixels To Patterns AI In Spatial Analysis https://mapscaping.com/podcast/from-pixels-to-patterns-ai-in-spatial-analysis/ Big Data In The Browser https://mapscaping.com/podcast/big-data-in-the-browser/ Spatial Knowledge Graphs https://mapscaping.com/podcast/spatial-knowledge-graphs/ Designing For Location Privacy https://mapscaping.com/podcast/designing-for-location-privacy/ All Of The Places In The World https://mapscaping.com/podcast/all-of-the-places-in-the-world/ Geospatial Jobs There are a few new jobs on our Job Board! The most interesting one is the role of Social Media Manager at Felt - United States (Remote) ( If you want to apply for this one, it might be a good idea to listen to this episode first ;) https://mapscaping.com/podcast/felt-upload-anything/ ) See more at https://mapscaping.com/jobs/ As a bonus for reading all the way to the end :) If you are looking for free terrain data for anywhere in the world you might find this useful https://github.com/openterrain/openterrain/wiki/Terrain-Data Some more episodes you might enjoy ESRI, GIS careers, Geospatial Data Science QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

Computer vision is everywhere! But teaching an algorithm to identify objects requires a lot of data and this is definitely the case when we think about GeoAI But it is not enough to have a lot of data we also need data that is labeled If we are looking for cars in images we need a lot of images of cars and we need to know which pixels are the car! Of course, I am oversimplifying but I hope you get the idea, Now imagine that you can automatically generate a large labeled data set of realistic images of cars based on the specifications of a specific sensor. These data sets are often referred to as synthetic data or fake data and to help us understand more about this I have invited Chris Andrews from Rendered AI on the podcast. Here are a few previous episodes you might find interesting Computer Vision And GeoAI https://mapscaping.com/podcast/computer-vision-and-geoai/ In this episode, the discussion is aimed at an increased understanding of the differences between computer vision and the AI that is used in the Earth Observation world. Labels Matter https://mapscaping.com/podcast/labels-matter/ What it takes to create labeled training data manually. If you are new to the idea of labeled data sets this is a good place to start. Fake Satellite Imagery https://mapscaping.com/podcast/fake-satellite-imagery/ This is a good episode if you want to know more about Generative AI and Generative Adversarial Networks. Also, check out this website https://thisxdoesnotexist.com/ to get an idea of where and how these Generative Adversarial Networks can be used. Look for a website called This City Does Not Exist http://thiscitydoesnotexist.com/ On a silently similar note try uploading an image to https://bard.google.com/ … it's pretty interesting!

This is an interview with a senior data scientist from Hub Ocean, a platform that aims to unlock and unite ocean data. Hub Ocean - as the name suggests is a hub for ocean data Now we have talked about these kinds of data hubs before on the podcast - Sentinal Hub - a data hub for earth observation data, Microsoft Planetary Computer, Google Earth Engine, Open Topography is data but for topography data …. The concept is not new but also not easy to implement and if they work, these types of data hubs have a gravity to them that becomes more powerful over time. One of the guiding concepts behind these data hubs seems to be the idea of FAIR data - Findable, Accessible, Interoperable, and Reuseable data …. But its not enough to ensure that the data is fair I think we should also consider how we can make the results of our research Findable, Accessible, Interoperable, and Reuseable data If you are not already familiar with Cloud Optimised Geospatial formats it is worth checking out these two episodes. https://mapscaping.com/podcast/cloud-optimized-point-clouds/ https://mapscaping.com/podcast/cloud-native-geospatial/ Some more episodes you might enjoy ESRI, GIS careers, Geospatial Data Science QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

felt.com is a browser-based mapping tool and its also a reminder that just because we have always done web mapping one way it doesn’t mean it always has to be done that way. For example, Felt lets you upload anything! That's a bold promise, you can upload anything you want and we will figure it out on the back end. Felt is also the first and only flagship sustaining member of the QGIS project, they are supporting the development of an open-source tiling engine, called Tippecanoe. They also support protomaps and the development of PMtiles as well as contributing code to Maplibre and Gdal ... But that is not why you should listen to this episode … you should listen to this episode because if we are going to grow the geo pie we need more upload anything buttons Easily bring data into Felt with our QGIS plug-in and Felt API How We Make Your Data Look Great at Every Scale with Tippecanoe Some more episodes you might enjoy ESRI, GIS careers, Geospatial Data Science QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping