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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...

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Qiusheng Wu

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12,831 просмотров • 10 месяцев назад •via X (Twitter)

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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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🚀 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

Qiusheng Wu

17,073 просмотров • 10 месяцев назад

🎯 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 Computer — all using natural language. Perfect for researchers, developers, and environmental professionals who want fast, intuitive, code-free access to massive Earth observation datasets like Sentinel-2, Landsat, NAIP, and DEM. Learn how to: ✅ Use zero-code AI agents in Jupyter Notebooks ✅ Search satellite imagery by date, location, or cloud cover ✅ Visualize data instantly using Leafmap ✅ Integrate STAC data catalogs into your GeoAI workflow Full video tutorial available on YouTube: #geospatial #GeoAI #opensource #PlanetaryComputer
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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 Computer — all using natural language. Perfect for researchers, developers, and environmental professionals who want fast, intuitive, code-free access to massive Earth observation datasets like Sentinel-2, Landsat, NAIP, and DEM. Learn how to: ✅ Use zero-code AI agents in Jupyter Notebooks ✅ Search satellite imagery by date, location, or cloud cover ✅ Visualize data instantly using Leafmap ✅ Integrate STAC data catalogs into your GeoAI workflow Full video tutorial available on YouTube: #geospatial #GeoAI #opensource #PlanetaryComputer

Qiusheng Wu

10,346 просмотров • 9 месяцев 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

Qiusheng Wu

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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
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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

16,623 просмотров • 1 год назад

The GeoAI Python package now supports interactive segmentation of remote sensing imagery with the Moondream AI Vision Language Models (VLMs), which provides powerful and efficient visual reasoning. A great alternative to Meta's SAM 3. GitHub: Notebook example: #GeoAI #geospatial #AI #opensource
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The GeoAI Python package now supports interactive segmentation of remote sensing imagery with the Moondream AI Vision Language Models (VLMs), which provides powerful and efficient visual reasoning. A great alternative to Meta's SAM 3. GitHub: Notebook example: #GeoAI #geospatial #AI #opensource

Qiusheng Wu

11,655 просмотров • 8 месяцев 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

62,401 просмотров • 3 месяцев назад

🚀 Visualize Geospatial Data Instantly from the Command Line! You can now interactively explore raster and vector datasets without opening a desktop GIS or Jupyter Notebook. Simply run: pip install "leafmap[viewer]" Then visualize your data with one line: view-raster /path/to/raster.tif view-vector /path/to/vector.geojson Customize your visualization easily: view-raster /path/to/raster.tif --band 1 --colormap coolwarm view-vector /path/to/vector.geojson --style liberty These CLI tools are powered by Leafmap, MapLibre, and LocalTileserver, supporting all geospatial formats compatible with rasterio and geopandas. 👉 Check it out: #geospatial #datascience #mapping #python
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🚀 Visualize Geospatial Data Instantly from the Command Line! You can now interactively explore raster and vector datasets without opening a desktop GIS or Jupyter Notebook. Simply run: pip install "leafmap[viewer]" Then visualize your data with one line: view-raster /path/to/raster.tif view-vector /path/to/vector.geojson Customize your visualization easily: view-raster /path/to/raster.tif --band 1 --colormap coolwarm view-vector /path/to/vector.geojson --style liberty These CLI tools are powered by Leafmap, MapLibre, and LocalTileserver, supporting all geospatial formats compatible with rasterio and geopandas. 👉 Check it out: #geospatial #datascience #mapping #python

Qiusheng Wu

13,479 просмотров • 10 месяцев назад

🚀 A brand-new feature is coming to the SAMGeo Python package: interactive remote-sensing image segmentation with text prompts powered by SAM3. This update makes geospatial segmentation even more intuitive: just describe what you want to detect, and let GeoAI do the rest. ✨ GitHub PR: #GeoAI #SAM3 #OpenSource #Python
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🚀 A brand-new feature is coming to the SAMGeo Python package: interactive remote-sensing image segmentation with text prompts powered by SAM3. This update makes geospatial segmentation even more intuitive: just describe what you want to detect, and let GeoAI do the rest. ✨ GitHub PR: #GeoAI #SAM3 #OpenSource #Python

Qiusheng Wu

32,950 просмотров • 8 месяцев 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

Qiusheng Wu

17,453 просмотров • 3 месяцев 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

12,252 просмотров • 1 год назад

🚀 GeoAI Tutorial 6: Building Footprint Regularization Using Deep Learning and GeoAI! 🌍 Learn how to regularize building footprints detected from aerial imagery with three different regularization techniques. Transform building footprints with rugged edges into smooth and rectangular shapes. 🎥 Watch the full tutorial here: 📓 Access the notebook: 🛠️ Explore the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python
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🚀 GeoAI Tutorial 6: Building Footprint Regularization Using Deep Learning and GeoAI! 🌍 Learn how to regularize building footprints detected from aerial imagery with three different regularization techniques. Transform building footprints with rugged edges into smooth and rectangular shapes. 🎥 Watch the full tutorial here: 📓 Access the notebook: 🛠️ Explore the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python

Qiusheng Wu

16,568 просмотров • 1 год 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

18,736 просмотров • 2 лет назад

🚀 SamGeo v0.14.0 is here! This release brings interactive segmentation for remote sensing imagery powered by Meta’s SAM3. 🟦 Draw a bounding box to automatically detect all similar objects across the image. 📝 Or just type a text prompt to segment objects instantly. No complex setup. No manual labeling. Just intuitive, AI-driven geospatial analysis. 🔗 GitHub: 🧪 Notebook: #SAM3 #GeoAI #Geospatial #OpenSource #Python #DataScience
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🚀 SamGeo v0.14.0 is here! This release brings interactive segmentation for remote sensing imagery powered by Meta’s SAM3. 🟦 Draw a bounding box to automatically detect all similar objects across the image. 📝 Or just type a text prompt to segment objects instantly. No complex setup. No manual labeling. Just intuitive, AI-driven geospatial analysis. 🔗 GitHub: 🧪 Notebook: #SAM3 #GeoAI #Geospatial #OpenSource #Python #DataScience

Qiusheng Wu

33,586 просмотров • 8 месяцев назад

A sneak peek at the upcoming QGIS OpenGeoAgent plugin! Run geospatial analysis and visualization using natural language, powered by over 480 geoprocessing tools from WhiteboxTools. This brings a new level of accessibility to advanced spatial workflows directly inside QGIS, making it easier to explore, analyze, and visualize data without writing code. Explore the projects: • GeoAgent: • whitebox-python: • WhiteboxTools: Huge credit to Dr. John Lindsay Whitebox Geospatial for developing WhiteboxTools! #QGIS #geospatial #opensource
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A sneak peek at the upcoming QGIS OpenGeoAgent plugin! Run geospatial analysis and visualization using natural language, powered by over 480 geoprocessing tools from WhiteboxTools. This brings a new level of accessibility to advanced spatial workflows directly inside QGIS, making it easier to explore, analyze, and visualize data without writing code. Explore the projects: • GeoAgent: • whitebox-python: • WhiteboxTools: Huge credit to Dr. John Lindsay Whitebox Geospatial for developing WhiteboxTools! #QGIS #geospatial #opensource

Qiusheng Wu

28,016 просмотров • 3 месяцев назад

📢Exciting new feature in #Leafmap! 🌟 You can now visualize #EarthEngine data on a stunning 3D terrain with #MapLibre 🗺️ Browse the massive 100+ PB Earth Engine Data Catalog and overlay any dataset on an interactive 3D map. 🌍 This demo showcases the ESA global land cover dataset with 3D buildings on top 🌳 GitHub: Notebook: #geospatial #opensource #dataviz #python
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📢Exciting new feature in #Leafmap! 🌟 You can now visualize #EarthEngine data on a stunning 3D terrain with #MapLibre 🗺️ Browse the massive 100+ PB Earth Engine Data Catalog and overlay any dataset on an interactive 3D map. 🌍 This demo showcases the ESA global land cover dataset with 3D buildings on top 🌳 GitHub: Notebook: #geospatial #opensource #dataviz #python

Qiusheng Wu

18,219 просмотров • 2 лет назад

🚀 Introducing the Earth Engine Dataset Explorer, powered by Google #Gemini! 🌍✨ Struggling to find the right #geospatial datasets for your research? Our Dataset Explorer recommends tailored datasets and offers an interactive exploration experience. 📹 Check out the video tutorial: 📒 Explore the notebook: #geemap #OpenSource #AI #EarthEngine
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🚀 Introducing the Earth Engine Dataset Explorer, powered by Google #Gemini! 🌍✨ Struggling to find the right #geospatial datasets for your research? Our Dataset Explorer recommends tailored datasets and offers an interactive exploration experience. 📹 Check out the video tutorial: 📒 Explore the notebook: #geemap #OpenSource #AI #EarthEngine

Qiusheng Wu

23,939 просмотров • 1 год назад

🚀 Visualizing Planet’s Tanager Hyperspectral Data in 3D! 🌍🛰️ Create an interactive image cube to explore the internal structure of all 426 spectral bands captured by Planet’s Tanager mission — going far beyond the traditional RGB view of desktop GIS tools. This approach allows for a deeper comparison of spectral characteristics across different land cover types and supports more advanced geospatial analysis. 📓 Try the notebook example: 💻 Explore the code on GitHub: #Hyperspectral #Geospatial #Tanager #Python #DataScience #OpenSource
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🚀 Visualizing Planet’s Tanager Hyperspectral Data in 3D! 🌍🛰️ Create an interactive image cube to explore the internal structure of all 426 spectral bands captured by Planet’s Tanager mission — going far beyond the traditional RGB view of desktop GIS tools. This approach allows for a deeper comparison of spectral characteristics across different land cover types and supports more advanced geospatial analysis. 📓 Try the notebook example: 💻 Explore the code on GitHub: #Hyperspectral #Geospatial #Tanager #Python #DataScience #OpenSource

Qiusheng Wu

11,341 просмотров • 1 год 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

26,015 просмотров • 1 год 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

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29,139 просмотров • 8 месяцев назад

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):
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I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

Matt Dancho (Business Science)

11,799 просмотров • 1 год назад