We're a company agnostic, ideas conference, pushing the boundaries of the traditional geospatial industry. We have big ideas about the future of geospatial and know you do too. Attending is more than just that, it’s about sharing, challenging and pushing the status quo. The sixth annual NORTH51 Conference takes place at the Malcolm Hotel in Canmore, Alberta, on April 24-26, 2024 and features keynotes, presentations, panels, and invaluable networking opportunities. The speakers and presenters at NORTH51 bring a unique and wide-ranging perspective as their share emerging ideas, technology breakthroughs, and their predictions about where the future will take us. Attracting over 100 leaders from across Canada, the USA, and internationally NORTH51 attendees sit at the intersection of high technology knowledge and high strategic knowledge. This mix of attendees leads to engaging conversations and networking opportunities.
External link for NORTH51 Conference
Canmore, Alberta, CA
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Find out how to use TorchGeo for training deep learning models on remote sensing data. Geospatial data offers unique challenges to typical deep learning workflows and TorchGeo offers some great tools to simplify them. Click the link to learn how to speed up your ability to iterate on new ideas: bit.ly/3SrtLuB #RemoteSensing #TorchGeo #PyTorch #ComputerVision
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We're hiring! Apply now to be a key player in transforming our innovative benefits programs and making a real impact on our team. #getoutside
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➡ What’s your mission? In our line of work, one of the first questions we pose to our clients is: Are you mapping, or are you monitoring? Understanding the distinction between these two mission types is crucial for effective mission design and data processing. #Aerospace #Geospatial #RemoteSensing #Mapping #Monitoring #Technology
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🚀 Uplifting news from Fused! 🚀 As Microsoft Planetary Computer Hub sunsets, its data and APIs will remain "available and unchanged". To continue to use PC data in your workflows, you can simply fetch PC COGs with Python's `pystac-client` then load them with xarray using `odc-stac`. This is the same access pattern as in the PC documentation. Fused UDFs help you load PC data into any environment with a few lines of code then process it at any scale on a #serverless backend. 🔍 Fetch PC data using the same Python access patterns 🗺️ Load data into interactive Tile maps 📊 Integrate with Notebooks and ETL for downstream analysis This UDF fetches the Sentinel 2 dataset from PC to render it on Workbench. A local Notebook then runs the UDF over the intersection of a custom polygon to load the raster for downstream analyses. Explore the possibilities with Fused and continue your data journey without interruption. Code and docs in the comments!
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In #AI every large player is spending truly absurd amounts of compute using roughly the same hardware with roughly the same AI tools and roughly the same data... to achieve roughly same results 🤷♂️ Gets worse, in #AIforEarth it is EXACTLY the same open data (Sentinel/Landsat/…). But, wait, plot twist, no large AI player is touching geospatial data, and I can't understand why: Literally Petabytes of data, with full global and historical coverage, fully cloud friendly, zero legal issues and can only augment multimodal models with actual Earth semantics. ...and that's why we started Clay. To provide a plug and play trained Earth model to improve other models. If you know why OpenAI, Mistral AI, Anthropic, Google DeepMind, ... are not adding open geospatial data (or if you can help us convince them), please comment below.
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#Earthobservation-derived analytics are in a very unsettled market space. Is this a shifting of gears? #geospatial #strategy https://lnkd.in/gKjkUPpx
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State of the Map US 2024 begins in two weeks 🥳 Are you ready?!? 🔗 buff.ly/4bxNdwP #StaetoftheMapUS #SOTMUS #SOTMUS2024 #OpenStreetMap
Does anyone know anyone? Tag them for Daniel!
I need some help! I am really interested in talking with people using LLM's to access and manipulate geospatial data. Do you know anyone?
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Several years ago when we were working on Pixel8earth we wrote a blog post about open maps and how they are funded. One concept was a "club good" where the "map" is non-rivalrous but the "club" charges a fee for access to it. Non-rivalrous being when multiple people can use the good simultaneously without diminishing its value or availability to others (e.g. Netflix, toll roads, country clubs). There are also public goods that are non-rivalrous and non-excludable (e.g. national defense, public schools). I was thinking of the success of Overture Maps Foundation and where it fit into these classifications in economics. The wild thing is I don't think there is a model in economics where the "club" doesn't control access to the "good", but instead charges to participate in the creation and direction of the "good". The end results is a club that privately funds a goo, which is "non-rivalrous" and also "non-excludable". Would this be an inverted club good or a privately funded public good? It seemed unique and I was wondering if others who are better versed in the literature had a category for where this concept from Marc Prioleau and company would fit? https://lnkd.in/e3ExzX6s
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Did you know we have one-day passes available for #SOTMUS? 🎟️ buff.ly/3R3QKeP #OpenStreetMap #OpenStreetMapUS #SaltLakeCity #geospatial #opendata