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

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.

Researcher Dominik Weckmüller discusses semantic search using embeddings to analyze text with geographic references. Topics include using deep learning models, creating embeddings, challenges in explainability, and the future of embeddings in different media and languages.

Lauren Guy, CEO of Asterra, discusses the innovative applications of L band in detecting water leakages, soil moisture, and minerals from space. Topics include technical insights into SAR, applications of L band, challenges, and limitations in the field of geospatial technology.

Exploring the world of cloud-native geospatial technologies like Geoparquet, range requests, and COPCs. Understanding the impact of these concepts on GIS in the future. Special focus on GeoParquet and its role in cloud-native geospatial. Insightful conversations with experts and recommendations for further exploration in the field.

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 discusses leveraging GIS for rapid decision-making in emergencies. Tools like Kobo Toolbox, ODK Collect, QGIS, Tableau, Power BI, HDX, and HOT are highlighted for data collection, analysis, and visualization. The importance of local knowledge, open-source software, and global connectivity in humanitarian GIS work is emphasized.

Brendan Ashworth, CTO of Bunting Labs, talks about AI Autocomplete for vectorization in QGIS. They discuss challenges in map digitization, the development process, and why QGIS was chosen over other platforms. Brendan's journey from software engineer to geospatial tech player is highlighted, along with the potential of merging geospatial data with machine learning.

Learn how Emlid revolutionized high precision GNSS receivers, making them more affordable and user-friendly. Discover the innovations in GNSS technology that reduced costs and size, paving the way for centimeter accuracy positioning. Explore the challenges of bringing high precision positioning to smartphones and the potential of combining GNSS with visual positioning technologies for better mapping efficiency.