Qfield

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