Stepan Lavrinenko

It's easy to be critical, but far more challenging to help solve the underlying issues. Specific feedback can be incredibly helpful in this context. Geospatial isn't failing. The people who write standards aren't failing either. Geospatial is extremely exciting right now. The industry isn't refusing to innovate. Innovation is happening faster than ever before. In the near future, chatgpt will be writing standards and designing data models without even asking us first. If that makes anyone feel any better. Another thought-provoking article, thanks Will.

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Fortran codes for height anomaly computation on or outside geoid using Stokes/Hotine numerical integral https://lnkd.in/eYU5UFWh

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The second webinar in our Embracing ArcGIS Pro series is just around the corner. On May 16, join my Esri Canada colleague's Maggie Samson & Michelle B. as they show how access to ArcGIS Pro is managed through the ArcGIS Online and ArcGIS Enterprise user management and licensing tools. https://lnkd.in/eR_9Z4gV #Esri #ArcGISPro #Desktop #ArcGISOnline #webmapping #ArcGISEnterprise #webinar #ArcGIS

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🛰️ CSRS-SCT 2024 Featured Workshop 🛰️ At this year's 45th Canadian Symposium on Remote Sensing in Halifax, NS, Maryam Jameela will be leading a workshop on deep learning for remote sensing. The workshop will introduce deep learning approaches through theory and examples. It will include two sessions; the first will focus on image datasets (2D), and the second will focus on LiDAR datasets (3D). The first session will overview deep learning, explaining important theories and terminologies in convolutional neural networks. Following this, we will review the latest 2D object detection network such as Oriented RCNN which will be the focus of this session’s demonstration for detecting pylon and span in utility corridor data. The second session will cover a lecture that discusses LiDAR point-cloud processing. We will be reviewing RandLA and SUNet to understand the demonstration of urban scene understanding and semantic segmentation of utility corridors. For more information: https://bit.ly/4aPkxzK

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Geoscience ANALYST’s Python API, geoh5py, allows you to connect open-source geophysical, machine learning, and other computational codes to the powerful visualization environment of Geoscience ANALYST.   With Geoscience ANALYST Pro, you can also write and execute your own Python code directly from the Geoscience ANALYST user interface.   👉 Visit https://lnkd.in/gFE9PVJ5 to access more documentation on geoh5py and the geoapps repository, openly available through ReadTheDocs.   👉 Download Geoscience ANALYST today and give it a try: https://lnkd.in/gEKWxYQf #GeoscienceANALYST #powerful #visualization #software #geoscience #data #3Dmodels #Python #API #GEOH5 #geoh5py #geoapps #opensource #code #geophysics #machinelearning

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If you work with 12d files and have faced challenges with opening or transferring data into GIS and spatial data platforms, you’re not alone! Many engineers and data managers rely on complex workarounds that often strip out essential attributes or compromise data integrity. Civillo simplifies this process. With Civillo, you can upload 12d models directly, keeping all attributes intact for seamless integration into your spatial workflows. Interested in a hands-on Civillo demonstration with your own 12d data? Book a call with one of our experts today and explore the potential for your projects.  https://lnkd.in/g4D5bMBK #12d #civillo #integration #civilconstruction #infrastructure #productivity #efficiency 

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Join Esri's new web course Maintaining Spatial Data Integrity Using ArcGIS Pro and "work" for the District of North Vancouver, British Columbia, to help them: 🍁add new curb stops 🍁reshape the topological edges of zones 🍁create and validate a geodatabase topology Join the course ➡️ https://ow.ly/WCLu50RSq4y

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Spatial analysis is a core concept of modern GIS. It's a method of analyzing and interpreting data that contains a geographic or spatial component using #GIS and other tools. Use this resource for incorporating #spatialanalysis in your curriculum. https://ow.ly/U1uq30sE5ZL

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Searching for Geospatial data… how many tabs do you have open? Enough is enough. No more downloading unnecessary files or opening a million tabs! Galileo’s Instant Previews let you dive into datasets with a single click, previewing on the same page. Preview your data before you commit, ensuring you have the right information at your disposal. https://galileo.gisdata.io

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#RRM - #RiskReduction_Monitoring from AssetAssurance Monitoring, includes archival processing across a variety of datasets. Here featuring U.S. Geological Survey (USGS) #Landsat_8 data from 2014. What were you doing on October 8, 2014 ? I was ramping up #civil_works and #mining sector deployments with IDS GeoRadar, and promoting data integration. Landsat_8 was orbiting on it mission - an ongoing collaboration with Landsat_7 -- a part of the now eleven years of #Free_and_Open_Data. Below is an image over central #Florida -- where #Phosphate products production remain a principle #mining activity in the state. Massive #TSF can be seen across the otherwise low relief. Groundwater challenges, protections, and always awareness are a feature of the environmental responsibility of the state's mineral resources department (https://lnkd.in/gjMkGj2y) - 27 operations are under licensing and permitting oversight. Stay tuned as we catalog and engage #RRM practices across the portfolio. Additional shares will include site data, links to resources, and educational materials. #TIRI, Proqio, KorrAI, #data_alignment Satshot

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With the new package {arcgisgeocode}, part of the {arcgis} metapackage and R-ArcGIS Bridge, you can now geocode directly from R! Learn how in this quick tutorial: https://ow.ly/YJxh50SrAI3

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One week until some of the GMAP team will be attending the Spatial Data Science Conference 2024 #SDSC2024! Will you be there? Let us know as we'd love to catch up! 🌍🌎🌏 #SpatialDataScience #locationintelligence #locationanalytics #gis

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Join me this Thursday for our year-end webinar, where I'll be sharing some tips and tricks on how to combine existing maps with your own data in ArcGIS Pro. 🪆 🗺️

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[Geospatial book recommendations #5] If you are dealing with loads of geospatial data, especially on cloud services, even more so on AWS, this book is right for you: 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐨𝐧 𝐀𝐖𝐒 by Scott Bateman, Janahan Gnanachandran, and Jeff DeMuth who guide you through it all via the following 16 chapters:   𝘗𝘢𝘳𝘵 1: 𝘐𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘵𝘰 𝘵𝘩𝘦 𝘎𝘦𝘰𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘋𝘢𝘵𝘢 𝘌𝘤𝘰𝘴𝘺𝘴𝘵𝘦𝘮 - Chapter 1: Introduction to Geospatial Data in the Cloud - Chapter 2: Quality and Temporal Geospatial Data Concepts 𝘗𝘢𝘳𝘵 2: 𝘎𝘦𝘰𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘋𝘢𝘵𝘢 𝘓𝘢𝘬𝘦𝘴 𝘶𝘴𝘪𝘯𝘨 𝘔𝘰𝘥𝘦𝘳𝘯 𝘋𝘢𝘵𝘢 𝘈𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 - Chapter 3: Geospatial Data Lake Architecture - Chapter 4: Using Geospatial Data with Amazon Redshift - Chapter 4: Using Geospatial Data with Amazon Redshift - Chapter 5: Using Geospatial Data with Amazon Aurora PostgreSQL - Chapter 6: Serverless Options for Geospatial - Chapter 7: Querying Geospatial Data with Amazon Athena 
 𝘗𝘢𝘳𝘵 3: 𝘈𝘯𝘢𝘭𝘺𝘻𝘪𝘯𝘨 𝘢𝘯𝘥 𝘝𝘪𝘴𝘶𝘢𝘭𝘪𝘻𝘪𝘯𝘨 𝘎𝘦𝘰𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘋𝘢𝘵𝘢 𝘪𝘯 𝘈𝘞𝘚 - Chapter 8: Geospatial Containers on AWS - Chapter 9: Using Geospatial Data with Amazon EMR - Chapter 10: Geospatial Data Analysis Using R on AWS - Chapter 11: Geospatial Machine Learning with SageMaker - Chapter 12: Using Amazon QuickSight to Visualize Geospatial Data 
𝘗𝘢𝘳𝘵 4: 𝘈𝘤𝘤𝘦𝘴𝘴𝘪𝘯𝘨 𝘖𝘱𝘦𝘯 𝘚𝘰𝘶𝘳𝘤𝘦 𝘢𝘯𝘥 𝘊𝘰𝘮𝘮𝘦𝘳𝘤𝘪𝘢𝘭 𝘗𝘭𝘢𝘵𝘧𝘰𝘳𝘮𝘴 𝘢𝘯𝘥 𝘚𝘦𝘳𝘷𝘪𝘤𝘦𝘴 - Chapter 13: Open Data on AWS - Chapter 14: Leveraging OpenStreetMap on AWS - Chapter 15: Feature Servers and Map Servers on AWS - Chapter 16: Satellite and Aerial Imagery on AWS Thanks Packt for the supply! 𝐌𝐨𝐫𝐞 𝐨𝐧 𝐭𝐡𝐞 𝐛𝐨𝐨𝐤: https://lnkd.in/dh22WcYF 𝐌𝐨𝐫𝐞 𝐨𝐧 𝐦𝐲 𝐜𝐨𝐮𝐫𝐬𝐞: https://lnkd.in/dG8SqMEP #GIS #spatialanalytics #geospatialdata #geospatial #datascience #datavisualization

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Utilize the #ArcGISBusinessAnalyst Target Marketing Wizard to harness your customer data, identify key segments, and locate potential new customers effectively. #BATipsandTricks Learn How ➡️https://ow.ly/ElHz50TyK3Q

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Learn how to use six Arcade expressions (our portable scripting language) in #ArcGIS Dashboard to calculate values ⚙️ and improve the formatting of three dashboards 🎨👩‍🎨. This session also covers Arcade's best practices: https://ow.ly/PWwh50TkBS2 #EsriDevSummit2024

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STOP: Struggling with Geospatial Data Downloads Finding and downloading geospatial data shouldn't require programming expertise or expensive hardware. Manually managing datasets is time-consuming, inefficient, and frustrating, creating a large barrier of entry. START: Simplifying Downloads with Galileo With Galileo, downloading feature layers is effortless. One click is all it takes—no external tools or expensive hardware needed. Save time and focus on what matters. Learn more, link in the comments!

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The future of Earth observation is not about capturing more images—it’s about extracting insights from the billions we already capture each day. Satellites deliver a constant stream of data, but making sense of it requires new approaches. Enter Geospatial Foundation Models (GeoFMs)—advanced AI models reshaping how we process Earth data at scale. GeoFMs streamline analysis by automatically identifying patterns, changes, and features in diverse environments. From forests to urban centers, these models deliver: 1. Precision: GeoFMs provide highly accurate insights, minimizing the need for manual data labeling. 2. Efficiency: By pre-training on massive datasets, GeoFMs cut down on computational time and cost. 3. Flexibility: These models adapt to varied geospatial needs, from coastal erosion monitoring to urban planning. One powerful feature of GeoFMs? They create embeddings for each location on Earth, enabling rapid similarity searches and complex analyses. This approach integrates data from satellites, drones, and ground stations, making it invaluable for real-time, edge-deployed solutions like those at Godel Space. GeoFMs don’t just capture the planet—they help us understand it. The impact is already here, paving the way for actionable, high-stakes decisions in sustainability, conservation, and urban resilience. For companies, research groups, or governments wanting to stay ahead in environmental monitoring, it’s clear: the future lies in foundation models.

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