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

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 inCtegration 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 QFieldCloud 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 Qfeild:  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 info@mapscpaing.com  

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 conversation 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, from IMMAP, 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/  

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 cloud 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/ https://north-road.com/   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 consolation 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 permanent 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    

** 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.   

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/

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

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/      

 felt.com is a browser-based mapping tool and but 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 a build and open-source tiling engine, 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

Rapid is a free open-source web-based editor for an OpenStreetMap. In the past the focus was on conflating AI-generated datasets with OpenStreetMap data but the future for this editor is conflating authoritative datasets with OpenStreetMap.  Humans are in the loop, people reviewing data authoritative datasets and adding them to OpenStreetMap with a few clicks!  So you might be wondering, what is Authoritative data? And perhaps it doesn't even matter what authoritative means maybe the most important thing is it correct. If you are interested in OpenStreetMap you might enjoy this episode  https://mapscaping.com/podcast/openstreetmap-is-a-community-of-communities/ which is a great introduction to OpenStreetMap as a project but also explains some of the commercial interest in updating the map which adds a lot of context to Rapid and its development and future.   Humanitarian OpenStreetMap Team If you have not heard of Humanitarian OpenStreetMap Team this is well worth checking out!  https://www.hotosm.org/   Segment Anything  (SAM) can segment objects by simply clicking or interactively selecting points to include or exclude from the object. This makes it a user-friendly tool for image segmentation. https://segment-anything.com/   Mapillary Mapillary is a platform that provides street-level imagery and map data from all over the world. The platform is powered by collaboration and computer vision, which helps in generating and maintaining up-to-date, detailed maps. https://mapscaping.com/podcast/scaling-map-data-generation-using-computer-vision/   GeoSpatial Jobs! Drone Deploy's Earthworks team is looking for an experienced Back End Engineer Full time / Remote NV5 is looking for a Senior GIS Analyst https://mapscaping.com/jobs/ Want to help? I could really use some support!    

The promise of digital mapping is to provide a shared and real-time view of the state of the underlying system. pg_eventserv is a free and open-source component that helps fulfill the promise of real-time event modeling and shared views in PostgreSQL. By connecting to PostgreSQL and listening on specified channels, pg_eventserv captures database notifications and forwards them to web clients, enabling real-time updates and synchronization of data displayed on maps or other web interfaces. pg_eventserv does one thing and one thing only: take events generated by the PostgreSQL NOTIFY command and passes the payload along to waiting WebSockets clients. pg_eventserv is free and easy to install and you can find it here: https://github.com/CrunchyData/pg_eventserv What this means is that any client can watch for notifications and update as changes in the database happen. Real-time data! Here is a link to a Youtube video demonstration of pg_eventserv in action! https://youtu.be/UakRtYmoWow  I will let Paul Ramsey the creator of pg_eventserv explain all this in more detail in this episode.  If you want to reach out to Paul the best place to do that is http://blog.cleverelephant.ca/  Or if you want to listen to previous episodes with Paul you might find these interesting Raster in the database? https://mapscaping.com/podcast/rasters-in-a-database/ Dynamic Vector Tiles Straight From The Database https://mapscaping.com/podcast/dynamic-vector-tiles-straight-from-the-database/ Spatial SQL- GIS Without The GIS https://mapscaping.com/podcast/spatial-sql-gis-without-the-gis/   also ...  If you are interested in spatial databases at scale ... you might find this episode interesting https://mapscaping.com/podcast/distributing-geospatial-data/    

You guessed it this episode is all about recruitment! Who is this episode for? Well if you have a career in geospatial … it's for you!   Getting the job you want, changing your career path, or deciding whether to work at a start-up or a more established company. Once you have made these decisions, how do you stand out from the crowd? What is it the recruiters are looking for? And if you do get offered a contract should you negotiate and what is negotiable?    Jett Metcalf has worked as a geospatial recruitment specialist for a few well-known companies like Google, Descartes Labs and is currently the Head of Talent at Regrow Ag. In this episode, he is going to help us understand some of the answers to these questions.   Reach out to Jett on LinkedIn or Twitter  If you enjoy this episode and are interested in more career-focused episodes go to https://mapscaping.com/podcasts/ and filter by “Geospatial Career"   See you next week!  

How do we get data from a satellite down to Earth? How do we task a satellite?  Today the answer is likely to be via radios and a system of downlink sites or ground stations. As the satellites pass overhead or within “line of sight” data can be sent via radio from the satellite to the receiver on the ground.  If you don't want to wait until the satellite can see the ground station, you can send your data to a geostationary satellite that can always see a ground station and let it send the data back to Earth.  Radios are tried and tested, they have been used for this purpose since the inception of satellite communication and radio waves can pass through Earth's atmosphere without significant loss! But … the frequency spectrum for radio waves is strictly regulated, which can limit available channels for communication, and the bandwidth of radio frequencies is limited, which can reduce the volume of data transmission. What about lasers?  You can send more data faster with a laser, you don't need to worry about interfering with someone else part of the radio spectrum, and ground stations can be much smaller even human-portable!  But … lasers struggle with clouds and the technology is still relatively new So what is the best way to communicate with satellites? Radio or Laser? The answer is … it depends ;)    Jordan Wachs, Director of Business Development for SpaceRake.net does a great job adding context to this discussion but perhaps the bigger question here is what will we do when satellites become internet devices, part of the Internet of Things? What if they were always on always connected in the same way your phone is always on, always connected? What will this enable?    This episode was sponsored by Sponsored by Sinergise, as part of Copernicus Data Space Ecosystem knowledge sharing   People who liked this episode also liked …   How to keep your satellite pointing at earth https://mapscaping.com/podcast/how-to-keep-your-satellite-pointing-at-earth/   Hyperspectral v's Multispectral https://mapscaping.com/podcast/hyperspectral-vs-multispectral/   Sentinel Hub https://mapscaping.com/podcast/sentinel-hub/   Swing by our website sometime https://mapscaping.com/

There is a general understanding that it is becoming increasingly difficult to extract meaning from all the data we are collecting without using AI.   But what is AI, and how did we end up in a situation where it is identifying wolves from dogs based on the presence of snow in the background of images?    What does this mean for spatial analysis using tabular data?    What is explainability?    This is not a "how-to" do spatial analysis using an AI episode, it is an overview of  AI in spatial analysis episode with Vin Sharma, VP of Engineering at FourSquare    https://www.linkedin.com/in/ciphr/ https://foursquare.com/  

PYGEOAPI is a Python server implementation of the OGC API suite of standards ... which might be really useful if you are thinking about upgrading from the first-generation OGC standards to the second-generation OGC standards  ... or if need to implement a custom data source or custom functionality to your web services.    https://pygeoapi.io   If you are using MapServer, Geoserver, Mapproxy, QGIS server, or Deegree you might find this episode interesting!   Relevant previous episodes   Cloud-native Geospatial https://mapscaping.com/podcast/cloud-native-geospatial/   Geoserver https://mapscaping.com/podcast/geoserver/   Geonode https://mapscaping.com/podcast/geonode-open-source-geospatial-content-management-system/      

So why would anyone want to put alot of data into a browser? Well, for a lot of the same reasons that edge computing and distributed computing have become so popular. You get the data a lot closer to the user and you don't have to pay for the compute ;)  … this sounds great but as I found out during this conversation it's not as easy as it might seem!  There are a lot of trade-offs that need to be evaluated when moving data and analytics to the client.   Nick Rabinowitz  Senior Staff Software Engineer at Foursquare has a ton of experience with this so he volunteered his time to help us understand more about it. https://location.foursquare.com/ https://studio.foursquare.com/home If you are not familiar with the Arrow data format it might be worth checking out   Apache Arrow defines a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead   Related podcast episodes that you might find interesting include H3 grid system https://mapscaping.com/podcast/h3-geospatial-indexing-system/ The H3 geospatial indexing system is a discrete global grid system consisting of a multi-precision hexagonal tiling of the sphere with hierarchical indexes. H3 is a really interesting approach to tiling data that was developed by UBER and has been open-sourced.  Hex Tiles https://mapscaping.com/podcast/hex-tiles/ If you have not heard of the H3 grid system before listen to that episode first before listening to this one it will add a lot of useful context! Spatial Knowledge Graphs https://mapscaping.com/podcast/spatial-knowledge-graphs/ Foursquare is moving away from spatial joins and focusing on building a knowledge graph. If you are not familiar with graphs this might be a good place to start, also its interesting to hear the reasons for the move from spatial joins to another data structure.   Distribution Geospatial Data https://mapscaping.com/podcast/distributing-geospatial-data/ This is interesting if you want to understand more about distributed databases and some of the strategies for doing this. It sounds complicated but this episode is a really good introduction!    Cloud Native Geospatial https://mapscaping.com/podcast/cloud-native-geospatial/ This episode give a solid overview of what cloud-native means and some of the current geospatial cloud native formats out there today   I am constantly thinking about how I can make this podcast better for you so if you have any ideas or suggestions please let me know!  Also, I am thinking of recording a behind-the-scenes episode, is that something you might be interested in? if so what questions do you have?   

Sounds like a great idea right?   In this episode, Paul Ramsey explains why you shouldn't ... unless you want to ... and how you can ... if you have to.   You can find Paul's blog here: http://blog.cleverelephant.ca/about   Previous episodes with Paul  Spatial SQL https://mapscaping.com/podcast/spatial-sql-gis-without-the-gis/   GDAL https://mapscaping.com/podcast/gdal-geospatial-data-abstraction-library/   Dynamic Vector Tiles https://mapscaping.com/podcast/dynamic-vector-tiles-straight-from-the-database/   Blog posts by Paul about Rasters in the Database https://www.crunchydata.com/blog/postgres-raster-query-basics https://www.crunchydata.com/blog/waiting-for-postgis-3.2-secure-cloud-raster-access   Check Out Our Geospatial Job Board! https://mapscaping.com/jobs/      

A knowledge graph is a network of relationships between real work entities and in this episode, you will learn how and why knowledge graphs might be a better choice than spatial joins!    Further listening! The H3 Indexing System https://mapscaping.com/podcast/h3-geospatial-indexing-system/   Hex Tiles https://mapscaping.com/podcast/hex-tiles/   Points of Interest data https://mapscaping.com/podcast/all-of-the-places-in-the-world/   Dark Data https://mapscaping.com/podcast/unstructured-data-is-dark-data/

I am sure you have heard of ChatGPT by now so the hope of this episode is to give you some more context about what is it built on and how it works.   To do that I invited Daniel Whitneck back on the podcast  You can connect with Daniel here https://datadan.io/   and listen to his previous episode here: https://mapscaping.com/podcast/an-introduction-to-artificial-intelligence/   This is perhaps the quote for the episode that I have spent the most time thinking about "We always thought AI would be logical and lack creativity - but it is almost the exact opposite" This reframes the idea of being wrong to being creative which I think you could argue really depends on the context!    If you have not already played around with ChatGPT it's well worth spending the time to experiment with it ... while its still free ;)  https://chat.openai.com/auth/login   Further listening    If you have not already listened to this episode about computer vision and GeoAI you might find it interesting. Listen out for the discussion around plausible / realistic data and real measurements - I think this gives more context to the use cases for generative AI  https://mapscaping.com/podcast/computer-vision-and-geoai/   You might also enjoy this episode about fake satellite imagery  https://mapscaping.com/podcast/fake-satellite-imagery/   BTW  I have started a job board for geospatial people feel free to check it out!        

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images.    You might think that this is exactly what we are doing in earth observation but there are a few important differences between computer vision and what some people refer to as GeoAI.   This week Jordi inglada is going to help you understand what those differences are and why it's not always possible to use Computer vision techniques in the field of Remote Sensing.   Listen out for these key points during the conversation! Why plausible or realistic data is not always a substitute for actual measurements, except when it is ;)  In computer vision we can learn from the data, in earth observation we know the physics To do interesting work in data science you need to - Computer science, applied math, and domain expertise. You don't need to be an expert in all three but you need to be interested in all three Vectors in the machine learning world don't necessarily have anything to do with points lines and polygons ;)   Sponsored by Sinergise, as part of Copernicus Data Space Ecosystem knowledge sharing. dataspace.copernicus.eu/ http://dataspace.copernicus.eu/   Related Podcast Episodes   Super Resolution https://mapscaping.com/podcast/super-resolution-smarter-upsampling/ Fake Satellite Imagery https://mapscaping.com/podcast/fake-satellite-imagery/   Sentinal Hub https://mapscaping.com/podcast/sentinel-hub/ Google Earth Engine  https://mapscaping.com/podcast/introducing-google-earth-engine/   Microsofts Planetary Computer  https://mapscaping.com/podcast/the-planetary-computer/   BTW MapScaping has started a Job Board!  it's in the early stages but it's live Jobs - Mapscaping.com        

Data is what data does  - more about that later on ;)  This episode focuses on designing for privacy, how do we create value from location data without sacrificing personal privacy?  Well, you might start by adhering to the Enhanced Standards For Precise Location Information which means that information about sensitive places like churches, hospitals, military bases, and LGBTQ+ spaces isn't misused or sold. Plus, they protect our exact location from being shared with law enforcement or bounty hunters!  Yes, that's right bounty hunters!  You might also think about adding noise to the data, maybe you want to blur the time stamp and look at everything in aggregate? It turns out the location data is not just classified as personal data but is actually classified as sensitive personal data in privacy law so if you are going to work with it you really need to understand the risks involved. One of the really interesting ideas mentioned by Elizabeth Hein VP of Compliance & Data Protection was the idea of regulating Use, Harm, and Risk instead of sensitive data  Data Is What Data Does: Regulating Use, Harm, and Risk Instead of Sensitive Data If you want to learn more about POI data and why points of interest data are so hard  check out the episode called All of the Places in the World: https://mapscaping.com/podcast/all-of-the-places-in-the-world/ On a side note, I am working on a side project, it's a job board for geospatial people, and you can find it here https://mapscaping.com/jobs/ it's still in the development phase but feel free to check it out!

When comparing multispectral and hyperspectral data it is not simply a case of “more data more better”!  With hyperspectral you have “The curse of Dimensionality” but you also get more flexibility to pick exactly what bands you want to use! With multispectral you have less noise but you also have less data! This episode is designed to be a beginner's guide to the differences between hyperspectral and multispectral satellite data.   You can reach out to Gordon Logie here: https://sparkgeo.com/blog/team/gordon/   Podcast episode with the CEO of Sentinel-Hub https://mapscaping.com/podcast/sentinel-hub/   Here are some courses that focused on hyperspectral and offer further training   https://eo-college.org/courses/beyond-the-visible/ https://eo-college.org/courses/beyond-the-visible-imaging-spectroscopy-for-agricultural-applications/ https://www.enmap.org/events_education/hyperedu/

This week we are going to learn how Foursquare is trying to identify and map all of the places in the world!  Foursquare uses a mixture of crowd source and data conflation to maintain a database of 205 million places ... and it's not easy! Each phone might see the world slightly differently in terms of location accuracies and crowdsourcing data means that people "check-in" at different locations.    Kyle Fowler - Senior Director, Engineering at Foursquare Is going to give a behind-the-scenes look at how the "Orginal location-based social network" is trying to map all of the places in the world.  This episode is the first in a series of episodes I am going to publish in partnership with Foursquare and the idea is to use it as a reference for later episodes about Privacy and location data, Knowledge Graphs, AI, Location Based Marketing and Big geospatial Data in the Browser.       

Protomaps is a serverless system for planet-scale maps, it's an umbrella project consisting of a few different components one of which is PMtiles. PMtiles is “Cloud Optimise Geotiff” for web mapping, what this means is that you can build a base map and host it without the need for a server!  PMtiles is a single file that you can access via HTTP range requests in the same way that you can access data within a Cloud Optimised Geotiff with the important difference that PMtiles can also contain vector data! What this means is that you can create your own base map, and host it on something like Amazon S3 object storage at a fraction of the cost of other base map solutions!    During this episode, you will hear Brandon, the founder, and creator of Protomaps, talk about scarcity, and well I have never really thought about base maps as being a scarce resource I can definitely see how a product like PMtiles could remove some of the barriers to entry for a lot of creativity in terms of base maps.    More information on Protomaps is here:  https://protomaps.com/   Tippecanoe https://github.com/felt/tippecanoe.git https://bertt.wordpress.com/2023/01/06/creating-vector-pmtiles-with-tippecanoe/ Relevant podcast episodes  Cloud Optimized Point Clouds https://mapscaping.com/podcast/cloud-optimized-point-clouds/ Cloud Native Geospatial https://mapscaping.com/podcast/cloud-native-geospatial/ Microsoft's Planetary computer https://mapscaping.com/podcast/the-planetary-computer/ Stamen Design - Full Stack Cartography https://mapscaping.com/podcast/full-stack-cartography/   If you have any questions or comments, let me know, I would love to hear from you!

Storytelling with point clouds   This is not your typical point clouds episode! Today we are talking about how to use point clouds to tell a story. During this episode, you will hear Benjamin Muller talk about using a point cloud to make a film about the city of St Gallen in Switzerland and you might be tempted to think … what a waste of time! Why not use the data to make better measurements that lead to better decisions?  How many IT projects have failed, not because they were based on bad decisions but because they failed to get people to adopt the changes?  The best decisions are meaningless unless they are adopted.  So, how do we get people to change or adopt the change we are trying to make?  I think the first thing to understand is that packaging matters!  This episode is a case study into wrapping our ideas in a story and visualizing them using geospatial data.   Here is a link to the visualizations that were created using the point cloud data https://www.gruenesgallustal.ch/resume   You can take a look HxDR platform here https://hxdr.com/   If you are interested in more technical episodes about point clouds you might enjoy these!   The Point Data Abstraction Library https://mapscaping.com/podcast/pdal-point-data-abstraction-library/   Cloud Optimized Point Clouds https://mapscaping.com/podcast/cloud-optimized-point-clouds/   Bathymetric Lidar https://mapscaping.com/podcast/bathymetric-lidar-and-blue-carbon/   Lidar from Drones https://mapscaping.com/podcast/lidar-from-drones/   Lidar from Space https://mapscaping.com/podcast/gedi-space-lasers/

You are about to meet Peter Spencer, a Freelance Archaeologist, Surveyor, and Geomatics Specialist You are also about to learn how geospatial tech and techniques are being applied in the field of archaeology at an object scale with laser scanning that enables fragments of skeletons from all over the world to be 3D printed and pieced together locally. At a site scale LiDAR, ground penetrating radar, and photogrammetry have completely changed the game in terms of modeling archaeological sites in a non-destructive manner. And at the landscape scale, Earth observation data and AI are being used to prospect for new sites!   You can connect with Pete here https://www.linkedin.com/in/peter-spencer-mcifa-162292136/   If you find this episode interesting you might like these episodes ;)  Using Synthetic Aperture Radar ( from space ) to find where drinking water is leaking out of pipes https://mapscaping.com/podcast/finding-water-leaks-from-space/    This episode is all about using LiDAR from drones https://mapscaping.com/podcast/lidar-from-drones/   The earth archive project is one archaeologist's goal to scan the entire world using LiDAR to create an archive for the future.  https://mapscaping.com/podcast/the-earth-archive/