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🎯 AI Agents for Microsoft Planetary Computer: Explore Satellite Data with Zero Code Unlock satellite data without writing a single line of code! 🌍 In this step-by-step tutorial, you’ll discover how to harness the power of AI agents to search, filter, and visualize geospatial datasets from the Microsoft Planetary...

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Qiusheng Wu

53,938 subscribers

10,380 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 2

Фото профиля hanstoo
hanstoo1 год назад

amazing!

Фото профиля Rongwei Ji
Rongwei Ji1 год назад

it's insane , LLM based image retrieve

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🚀 Exciting news! The GeoAI Python package just got a major upgrade. It now supports interactive geospatial data visualization powered by AI Agents. 🌍✨ GitHub: Full tutorial: 🌍 In this tutorial, you’ll learn how to build interactive maps powered by AI agents that respond to natural language queries. Watch how you can fly to any city, add raster or vector layers, visualize 3D buildings, and explore geospatial data—all without writing code. Perfect for developers, researchers, and anyone curious about AI, GIS, and interactive mapping. #geospatial #mapping #python #GeoAI #AI
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🚀 Exciting news! The GeoAI Python package just got a major upgrade. It now supports interactive geospatial data visualization powered by AI Agents. 🌍✨ GitHub: Full tutorial: 🌍 In this tutorial, you’ll learn how to build interactive maps powered by AI agents that respond to natural language queries. Watch how you can fly to any city, add raster or vector layers, visualize 3D buildings, and explore geospatial data—all without writing code. Perfect for developers, researchers, and anyone curious about AI, GIS, and interactive mapping. #geospatial #mapping #python #GeoAI #AI

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12,908 просмотров • 1 год назад

How to Create a Spinning Globe 🌍 with Your Own Data Layers (No Coding Required) Learn how to create a spinning 3D globe map without writing any code using a simple web-based tool. In this step-by-step geospatial tutorial, you'll see how to build an interactive spinning globe, overlay raster and vector data layers, and visualize datasets directly in your browser or in a Jupyter Notebook environment. Web-based demo: Jupyter notebook demo: Video tutorial: #geospatial #opensource #python #jupyter #maplibre
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How to Create a Spinning Globe 🌍 with Your Own Data Layers (No Coding Required) Learn how to create a spinning 3D globe map without writing any code using a simple web-based tool. In this step-by-step geospatial tutorial, you'll see how to build an interactive spinning globe, overlay raster and vector data layers, and visualize datasets directly in your browser or in a Jupyter Notebook environment. Web-based demo: Jupyter notebook demo: Video tutorial: #geospatial #opensource #python #jupyter #maplibre

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12,745 просмотров • 8 месяцев назад

I built an AI data visualization Agent. The AI Agent can automatically generate the code and visualize the data using code interpreter sandbox. 100% Opensource Code with step-by-step tutorial.
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I built an AI data visualization Agent. The AI Agent can automatically generate the code and visualize the data using code interpreter sandbox. 100% Opensource Code with step-by-step tutorial.

Shubham Saboo

68,488 просмотров • 1 год назад

🚀 GeoAI Tutorial 12: Wetland Mapping with Deep Learning and GeoAI! 🌍 Unlock the power of your geospatial data! Learn to train an wetland detection model and run inference on large datasets—all with just a few lines of code. 🎥 Watch the full tutorial here: 📓 Explore the notebook: 🛠️ Check out the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python #DeepLearning
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🚀 GeoAI Tutorial 12: Wetland Mapping with Deep Learning and GeoAI! 🌍 Unlock the power of your geospatial data! Learn to train an wetland detection model and run inference on large datasets—all with just a few lines of code. 🎥 Watch the full tutorial here: 📓 Explore the notebook: 🛠️ Check out the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python #DeepLearning

Qiusheng Wu

16,644 просмотров • 1 год назад

An AI assistant for Google Earth Engine is now available in QGIS! You can explore and visualize any dataset from the 80+ PB Earth Engine Data Catalog using natural language, and run scalable geospatial analysis on demand without writing code. Every interaction automatically generates reusable code, making it easy to reproduce results, customize workflows, and transition from no-code to full scripting when needed. Upgrade the plugin to get started and bring AI-powered geospatial analytics directly into your QGIS workflow. GitHub: QGIS plugin: GeoAgent: #EarthEngine #QGIS #OpenSource #GeoAI #Geospatial
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An AI assistant for Google Earth Engine is now available in QGIS! You can explore and visualize any dataset from the 80+ PB Earth Engine Data Catalog using natural language, and run scalable geospatial analysis on demand without writing code. Every interaction automatically generates reusable code, making it easy to reproduce results, customize workflows, and transition from no-code to full scripting when needed. Upgrade the plugin to get started and bring AI-powered geospatial analytics directly into your QGIS workflow. GitHub: QGIS plugin: GeoAgent: #EarthEngine #QGIS #OpenSource #GeoAI #Geospatial

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17,592 просмотров • 6 месяцев назад

🚀 Exciting update! The GeoAI Python package is getting even smarter — soon it will support AI agents. That means you’ll be able to use natural language to effortlessly download, analyze, and visualize geospatial data without coding 🌍🤖 Stay tuned for the upcoming release! 🔗 GitHub: #geospatial #GeoAI #Python #AI
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🚀 Exciting update! The GeoAI Python package is getting even smarter — soon it will support AI agents. That means you’ll be able to use natural language to effortlessly download, analyze, and visualize geospatial data without coding 🌍🤖 Stay tuned for the upcoming release! 🔗 GitHub: #geospatial #GeoAI #Python #AI

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17,073 просмотров • 1 год назад

🚀 Visualize Big Vector Data in Python—FAST! Tired of slow and clunky tools for visualizing big vector dataset in Python? Discover this one-line Python trick using Leafmap + DuckDB to instantly render huge vector datasets—even gigabytes in size—right in your Jupyter notebook. In this video, you'll learn how to: ✅ Visualize millions of vector features with ease ✅ Use Leafmap + DuckDB to serve vector tiles dynamically ✅ Handle GeoParquet, GeoPackage, cloud-hosted data & more ✅ Create fully interactive maps—without crashing your browser! 📌 Watch the full video tutorial on YouTube: 👉 Notebook example: #Visualization #Leafmap #BigData #DuckDB #GeoPython #GIS #OpenSource
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🚀 Visualize Big Vector Data in Python—FAST! Tired of slow and clunky tools for visualizing big vector dataset in Python? Discover this one-line Python trick using Leafmap + DuckDB to instantly render huge vector datasets—even gigabytes in size—right in your Jupyter notebook. In this video, you'll learn how to: ✅ Visualize millions of vector features with ease ✅ Use Leafmap + DuckDB to serve vector tiles dynamically ✅ Handle GeoParquet, GeoPackage, cloud-hosted data & more ✅ Create fully interactive maps—without crashing your browser! 📌 Watch the full video tutorial on YouTube: 👉 Notebook example: #Visualization #Leafmap #BigData #DuckDB #GeoPython #GIS #OpenSource

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13,147 просмотров • 1 год назад

🚀 GeoAI Tutorial 23: Building Detection from Aerial Imagery and LiDAR Data 🏙️🛰️ In this tutorial, I demonstrate how to train a deep learning segmentation model for building detection by combining aerial/satellite imagery with LiDAR data. You'll learn how to fuse multi-source remote sensing data to improve model accuracy and robustness. 🔗 Tutorial video: 📓 Notebook example: 💻 GitHub repo: 📚 Documentation: 🎥 Full playlist: #GeoAI #geospatial #AI #Python #DeepLearning
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🚀 GeoAI Tutorial 23: Building Detection from Aerial Imagery and LiDAR Data 🏙️🛰️ In this tutorial, I demonstrate how to train a deep learning segmentation model for building detection by combining aerial/satellite imagery with LiDAR data. You'll learn how to fuse multi-source remote sensing data to improve model accuracy and robustness. 🔗 Tutorial video: 📓 Notebook example: 💻 GitHub repo: 📚 Documentation: 🎥 Full playlist: #GeoAI #geospatial #AI #Python #DeepLearning

Qiusheng Wu

12,331 просмотров • 1 год назад

Visualize 350TB of LiDAR Point Clouds (75 Trillion Points in Your Browser!) Learn how to access and visualize massive USGS LiDAR datasets, including over 350TB and 75 trillion points, directly in your browser using two powerful open-source formats: - COPC (Cloud Optimized Point Cloud) - Entwine Point Tiles (EPT) for large-area streaming No downloads needed. Stream and explore massive point cloud datasets instantly using cloud-native datasets from AWS Open Data Program and Microsoft Planetary Computer. Try it now: LiDAR Viewer: GitHub: Full video tutorial: #lidar #COPC #USGS #opensource #opendata
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Visualize 350TB of LiDAR Point Clouds (75 Trillion Points in Your Browser!) Learn how to access and visualize massive USGS LiDAR datasets, including over 350TB and 75 trillion points, directly in your browser using two powerful open-source formats: - COPC (Cloud Optimized Point Cloud) - Entwine Point Tiles (EPT) for large-area streaming No downloads needed. Stream and explore massive point cloud datasets instantly using cloud-native datasets from AWS Open Data Program and Microsoft Planetary Computer. Try it now: LiDAR Viewer: GitHub: Full video tutorial: #lidar #COPC #USGS #opensource #opendata

Qiusheng Wu

56,352 просмотров • 9 месяцев назад

Introducing the NASA Earthdata Plugin for QGIS! NASA’s Earthdata archive hosts over 120 petabytes of satellite imagery and geospatial datasets, but accessing it has often required navigating complex web portals or writing code. This new QGIS plugin changes that. With the NASA Earthdata plugin, you can search, filter, preview, and download NASA datasets directly inside QGIS, no programming needed. It’s designed for anyone who wants fast, seamless access to NASA data within their existing GIS workflow. Plugin page: GitHub: Step-by-step video tutorial: #QGIS #NASA #satelliteimagery #opensource
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Introducing the NASA Earthdata Plugin for QGIS! NASA’s Earthdata archive hosts over 120 petabytes of satellite imagery and geospatial datasets, but accessing it has often required navigating complex web portals or writing code. This new QGIS plugin changes that. With the NASA Earthdata plugin, you can search, filter, preview, and download NASA datasets directly inside QGIS, no programming needed. It’s designed for anyone who wants fast, seamless access to NASA data within their existing GIS workflow. Plugin page: GitHub: Step-by-step video tutorial: #QGIS #NASA #satelliteimagery #opensource

Qiusheng Wu

43,560 просмотров • 10 месяцев назад

🎁 A New Year’s gift to the geospatial community: a brand-new QGIS plugin that unlocks access to 80+ petabytes of satellite imagery and geospatial datasets with zero coding required. In my first video of 2026, I’m excited to share what might be one of the most powerful QGIS plugins I’ve built so far. This plugin makes it incredibly easy to explore the official Earth Engine Data Catalog and the Awesome GEE Community Catalog directly inside QGIS. With just a few clicks, you can browse massive Earth Engine datasets, visualize them on the map, generate time series from ImageCollections, export data for further analysis, and even convert Earth Engine JavaScript workflows to Python, all without writing a single line of code. If you’ve ever wanted the power of Earth Engine inside QGIS without the learning curve, this plugin is for you. 👉 Download the plugin and follow the full step-by-step video tutorial to get started today: QGIS Plugin Page: GitHub Repository: Video Tutorial: Happy New Year, and I hope this tool helps you do more with geospatial data in 2026 🚀 #QGIS #geospatial #EarthEngine #Python #datascience
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🎁 A New Year’s gift to the geospatial community: a brand-new QGIS plugin that unlocks access to 80+ petabytes of satellite imagery and geospatial datasets with zero coding required. In my first video of 2026, I’m excited to share what might be one of the most powerful QGIS plugins I’ve built so far. This plugin makes it incredibly easy to explore the official Earth Engine Data Catalog and the Awesome GEE Community Catalog directly inside QGIS. With just a few clicks, you can browse massive Earth Engine datasets, visualize them on the map, generate time series from ImageCollections, export data for further analysis, and even convert Earth Engine JavaScript workflows to Python, all without writing a single line of code. If you’ve ever wanted the power of Earth Engine inside QGIS without the learning curve, this plugin is for you. 👉 Download the plugin and follow the full step-by-step video tutorial to get started today: QGIS Plugin Page: GitHub Repository: Video Tutorial: Happy New Year, and I hope this tool helps you do more with geospatial data in 2026 🚀 #QGIS #geospatial #EarthEngine #Python #datascience

Qiusheng Wu

85,313 просмотров • 10 месяцев назад

🌍 New Tutorial Alert: Visualizing Google's AlphaEarth Satellite Embeddings in 3D! 🛰️🏙️ Google DeepMind has released AlphaEarth, a groundbreaking satellite embedding dataset on Google Earth Engine. It includes annual global embeddings from 2017 to 2024, where each pixel covers a 10×10 meter area. In this tutorial, you'll learn how to: ✅ Access and visualize AlphaEarth embeddings in 3D ✅ Leverage them for machine learning and change detection 🎥 Watch the tutorial: 📘 Try the notebook: #GeoAI #geospatial #AI #Python #DeepLearning
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🌍 New Tutorial Alert: Visualizing Google's AlphaEarth Satellite Embeddings in 3D! 🛰️🏙️ Google DeepMind has released AlphaEarth, a groundbreaking satellite embedding dataset on Google Earth Engine. It includes annual global embeddings from 2017 to 2024, where each pixel covers a 10×10 meter area. In this tutorial, you'll learn how to: ✅ Access and visualize AlphaEarth embeddings in 3D ✅ Leverage them for machine learning and change detection 🎥 Watch the tutorial: 📘 Try the notebook: #GeoAI #geospatial #AI #Python #DeepLearning

Qiusheng Wu

26,075 просмотров • 1 год назад

Working with Planet Tanager Hyperspectral Data (426 Bands) in QGIS with HyperCoast Learn how to access, visualize, and analyze Planet’s Tanager hyperspectral imagery in QGIS using the HyperCoast plugin. In this tutorial, you will explore freely available Planet Tanager open data with over 426 spectral bands and learn how to stream imagery, download HDF5 datasets, inspect spectral signatures, and perform hyperspectral visualization directly inside QGIS. Video Tutorial: HyperCoast: QGIS Plugin: Tanager STAC Repo: Tanager STAC Browser: #opendata #geospatial #hypercoast #QGIS #Tanager #hyperspectral
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Working with Planet Tanager Hyperspectral Data (426 Bands) in QGIS with HyperCoast Learn how to access, visualize, and analyze Planet’s Tanager hyperspectral imagery in QGIS using the HyperCoast plugin. In this tutorial, you will explore freely available Planet Tanager open data with over 426 spectral bands and learn how to stream imagery, download HDF5 datasets, inspect spectral signatures, and perform hyperspectral visualization directly inside QGIS. Video Tutorial: HyperCoast: QGIS Plugin: Tanager STAC Repo: Tanager STAC Browser: #opendata #geospatial #hypercoast #QGIS #Tanager #hyperspectral

Qiusheng Wu

12,338 просмотров • 5 месяцев назад

📢 Exciting news! Leafmap now supports creating 3D 🌎 maps with Mapbox! This demo showcases how to visualize the ESA global land cover from #EarthEngine on a 3D globe. Explore the 100 PB+ Earth Engine Data Catalog and display any geospatial datasets in 3D 🚀 Notebook: #leafmap #geospatial #opensource #python
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📢 Exciting news! Leafmap now supports creating 3D 🌎 maps with Mapbox! This demo showcases how to visualize the ESA global land cover from #EarthEngine on a 3D globe. Explore the 100 PB+ Earth Engine Data Catalog and display any geospatial datasets in 3D 🚀 Notebook: #leafmap #geospatial #opensource #python

Qiusheng Wu

18,763 просмотров • 2 лет назад

I built an AI startup insight tool Agent using Firecrawl FIRE-1 and GPT-4o. FIRE-1 AI Agent navigates websites, clicks buttons, and extracts company data autonomously without writing a single line of custom code. 100% Opensource Code with step-by-step tutorial.
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I built an AI startup insight tool Agent using Firecrawl FIRE-1 and GPT-4o. FIRE-1 AI Agent navigates websites, clicks buttons, and extracts company data autonomously without writing a single line of custom code. 100% Opensource Code with step-by-step tutorial.

Shubham Saboo

23,218 просмотров • 1 год назад

I am excited to introduce OpenGeoAgent, a powerful open-source multimodal AI agent for automated geospatial analysis and visualization! It supports QGIS, Jupyter notebook, and Python scripting. In this tutorial, you’ll learn how to automate GIS workflows using natural language, generate maps, analyze satellite data, and even run complex hydrological models. You can even interact with the agent using voice commands (no typing needed). Video: GitHub: QGIS Plugin: #geospatial #GeoAgent #OpenSource #AI
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I am excited to introduce OpenGeoAgent, a powerful open-source multimodal AI agent for automated geospatial analysis and visualization! It supports QGIS, Jupyter notebook, and Python scripting. In this tutorial, you’ll learn how to automate GIS workflows using natural language, generate maps, analyze satellite data, and even run complex hydrological models. You can even interact with the agent using voice commands (no typing needed). Video: GitHub: QGIS Plugin: #geospatial #GeoAgent #OpenSource #AI

Qiusheng Wu

63,819 просмотров • 6 месяцев назад

🚀 I am very excited to release the SamGeo QGIS plugin for geospatial image segmentation, powered by Meta’s Segment Anything Model (SAM 3) In this full tutorial, I’ll walk you through how to install, configure, and start segmenting satellite imagery in QGIS without writing a single line of code! 👉 Download the plugin here: 💻 Full video tutorial: #QGIS #SegmentAnything #SAM #GeoAI #RemoteSensing
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🚀 I am very excited to release the SamGeo QGIS plugin for geospatial image segmentation, powered by Meta’s Segment Anything Model (SAM 3) In this full tutorial, I’ll walk you through how to install, configure, and start segmenting satellite imagery in QGIS without writing a single line of code! 👉 Download the plugin here: 💻 Full video tutorial: #QGIS #SegmentAnything #SAM #GeoAI #RemoteSensing

Qiusheng Wu

29,228 просмотров • 10 месяцев назад

Free Maxar/Vantor Satellite Imagery for Disaster Response - QGIS and MapLibre Plugins Demo Want to download free high-resolution satellite imagery in QGIS? In this tutorial, I’ll show you how to access and visualize imagery from the Vantor (formerly Maxar) Open Data Program using two powerful plugins, no programming required. The Vantor Open Data Program provides free satellite imagery to support global disaster response and recovery efforts. These plugins make it easy to search, filter, preview, and download imagery directly inside QGIS or in web applications built with MapLibre GL. MapLibre GL Plugin GitHub: Live Demo: QGIS Plugin: GitHub: Plugin Page: Vantor Open Data Program: #QGIS #Maxar #SatelliteImagery #DisasterResponse #OpenSource
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Free Maxar/Vantor Satellite Imagery for Disaster Response - QGIS and MapLibre Plugins Demo Want to download free high-resolution satellite imagery in QGIS? In this tutorial, I’ll show you how to access and visualize imagery from the Vantor (formerly Maxar) Open Data Program using two powerful plugins, no programming required. The Vantor Open Data Program provides free satellite imagery to support global disaster response and recovery efforts. These plugins make it easy to search, filter, preview, and download imagery directly inside QGIS or in web applications built with MapLibre GL. MapLibre GL Plugin GitHub: Live Demo: QGIS Plugin: GitHub: Plugin Page: Vantor Open Data Program: #QGIS #Maxar #SatelliteImagery #DisasterResponse #OpenSource

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11,194 просмотров • 8 месяцев назад

I built an AI Data Analysis Agent. Just upload CSV/Excel files and analyze data through natural language. It uses DuckDB for lightning-fast processing. 100% Opensource Code with step-by-step tutorial.
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I built an AI Data Analysis Agent. Just upload CSV/Excel files and analyze data through natural language. It uses DuckDB for lightning-fast processing. 100% Opensource Code with step-by-step tutorial.

Shubham Saboo

92,162 просмотров • 1 год назад

I built an autonomous 3D Python Game Visulizer Agent team with browser use and DeepSeek R1. It has 4 AI agents working as a team to open, code, run, and visualize the game in the browser - all on autopilot. 100% Opensource code with step-by-step tutorial.
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I built an autonomous 3D Python Game Visulizer Agent team with browser use and DeepSeek R1. It has 4 AI agents working as a team to open, code, run, and visualize the game in the browser - all on autopilot. 100% Opensource code with step-by-step tutorial.

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