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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

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Qiusheng Wu

52,391 subscribers

18,736 просмотров • 2 лет назад •via X (Twitter)

Наука и технологииОбразованиеНовости и политика#EarthEngine
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Комментарии: 5

Фото профиля Joe McCann
Joe McCann2 лет назад

@cloudnativegeo You are crushing it man!

Фото профиля Marc Compere
Marc Compere2 лет назад

now this is pretty interesting. 🤔🧐😂

Фото профиля CEO del Socialismo Liberal 🌹🕊️ 自由社会主义首席执行官
CEO del Socialismo Liberal 🌹🕊️ 自由社会主义首席执行官2 лет назад

Good for make one with +4m sea level?

Фото профиля çaliw🇸🇳❤
çaliw🇸🇳❤2 лет назад

Impressive😍

Фото профиля Wietse Buwalda
Wietse Buwalda2 лет назад

Would love to see @gebcobathymetry @seabed2030 data integrated with this

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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
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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

🚀 Leafmap now supports visualizing geospatial data on a stunning 3D globe with MapLibre 🌍. Create stunning 3D maps with just a few lines of Python code! 🎥 Check out the full tutorial: 📓 Dive into the notebook: 🌐 Try the demo: #Geospatial #3DMapping #Python
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🚀 Leafmap now supports visualizing geospatial data on a stunning 3D globe with MapLibre 🌍. Create stunning 3D maps with just a few lines of Python code! 🎥 Check out the full tutorial: 📓 Dive into the notebook: 🌐 Try the demo: #Geospatial #3DMapping #Python

Qiusheng Wu

10,980 просмотров • 1 год назад

Visualize real-time movement data in 3D with #Leafmap and #MapLibre 🗺️💻 Update GPS tracking data from vehicles, animals, hikers, sports, and more on interactive 3D maps! 🚗🐘🚴‍♀️⚽ GitHub: Notebook: #geospatial #dataviz #opensource #python 🌍📊🐍
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Visualize real-time movement data in 3D with #Leafmap and #MapLibre 🗺️💻 Update GPS tracking data from vehicles, animals, hikers, sports, and more on interactive 3D maps! 🚗🐘🚴‍♀️⚽ GitHub: Notebook: #geospatial #dataviz #opensource #python 🌍📊🐍

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20,587 просмотров • 2 лет 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

12,704 просмотров • 5 месяцев назад

🚀 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

Qiusheng Wu

12,831 просмотров • 10 месяцев назад

The anymap-ts Python package now supports creating choropleth maps from vector data with a few simple clicks! Give it a try: Web demo: Jupyter demo: GitHub: Documentation: #geospatial #opensource
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The anymap-ts Python package now supports creating choropleth maps from vector data with a few simple clicks! Give it a try: Web demo: Jupyter demo: GitHub: Documentation: #geospatial #opensource

Qiusheng Wu

12,496 просмотров • 5 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

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

🚨 New Tutorial Alert 🚨 Rendering Google Earth Engine & Cloud-Optimized GeoTIFF Tiles in 3D with Google Earth In this tutorial, I demonstrate how to visualize Google Earth Engine tile layers and Cloud Optimized GeoTIFFs (COGs) directly in Google Earth’s 3D environment. This approach lets you bring together high-resolution historical imagery and Street View data into one powerful platform. It’s a simple but effective way to make your geospatial data more accessible—just like sharing a Google Doc link. 🔗 Notebook: ▶️ Video: #geospatial #googleearth #dataviz #earthengine
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🚨 New Tutorial Alert 🚨 Rendering Google Earth Engine & Cloud-Optimized GeoTIFF Tiles in 3D with Google Earth In this tutorial, I demonstrate how to visualize Google Earth Engine tile layers and Cloud Optimized GeoTIFFs (COGs) directly in Google Earth’s 3D environment. This approach lets you bring together high-resolution historical imagery and Street View data into one powerful platform. It’s a simple but effective way to make your geospatial data more accessible—just like sharing a Google Doc link. 🔗 Notebook: ▶️ Video: #geospatial #googleearth #dataviz #earthengine

Qiusheng Wu

16,391 просмотров • 11 месяцев 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

🎁 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

85,121 просмотров • 7 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

25,950 просмотров • 1 год 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

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This is pretty cool 👓 Google Announces Geospatial Creator powered by ARCore & Google Maps and it allows you to visualize, design, and publish world-anchored content by using Unity ! Few features available: 📌 Photorealistic 3D Tiles: Visualize the 3D geometry of the world and deploy location-anchored content accurately with high-resolution, photorealistic 3D tiles. 📌 Geospatial anchors: Place and anchor 3D content at any given latitude, longitude, and altitude with sub-meter accuracy in areas covered by Google Street View. 📌 Terrain anchors: Place and anchor 3D content with only latitude and longitude coordinates, using data from Google Maps to determine ground level. 📌 Rooftop anchors: Use rooftops to place and anchor 3D content with respect to the building geometry and terrain in areas covered by Google Street View. 📢 To get started with Unity follow these steps: #AR #Google

Dilmer

58,292 просмотров • 3 лет назад

GeoLibre now supports rendering Google Photorealistic 3D Tiles! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. Try it out with your own Google Maps API key: - Launch GeoLibre web: - GitHub: - Documentation: #GIS #Geospatial #OpenSource #RemoteSensing #MapLibre #GeoLibre
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GeoLibre now supports rendering Google Photorealistic 3D Tiles! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. Try it out with your own Google Maps API key: - Launch GeoLibre web: - GitHub: - Documentation: #GIS #Geospatial #OpenSource #RemoteSensing #MapLibre #GeoLibre

Qiusheng Wu

19,409 просмотров • 1 месяц 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

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Qiusheng Wu

16,623 просмотров • 1 год назад

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Mapbox has a brand new 3D map style, still in beta! But it didn’t have a hackable live demo, so at Skydio we created one 😎 , try it out here: The updates include: - Hundreds of new 3D models for famous landmarks - New symbolic design that works hand in hand with the style - Day/night light shift built in! How does this help? Through more accurate 3D maps: - Data displayed on the map is more useful as it’s easier to understand where you are (our brains think in 3D) - More immersive 3D experiences are unlocked, not just navigation Looking forward to seeing where Mapbox takes this next

Alberto

43,945 просмотров • 3 лет назад

Beautiful new raster data layers are now available on Google Earth’s intuitive, no-code canvas. 🗺️👉 With the analytical power of Google Earth Engine directly integrated into Google Earth, geospatial analysis is as easy as point and click. Rolling out over the coming days, you will have access to ten new raster layers from Earth Engine's iconic data catalog, including: • Slope • Aspect • Land cover • and more It's never been easier to quickly combine raster data, vector data, imagery, and your own data to save time and make better decisions. Explore these new data layers, now.
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Beautiful new raster data layers are now available on Google Earth’s intuitive, no-code canvas. 🗺️👉 With the analytical power of Google Earth Engine directly integrated into Google Earth, geospatial analysis is as easy as point and click. Rolling out over the coming days, you will have access to ten new raster layers from Earth Engine's iconic data catalog, including: • Slope • Aspect • Land cover • and more It's never been easier to quickly combine raster data, vector data, imagery, and your own data to save time and make better decisions. Explore these new data layers, now.

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15,609 просмотров • 4 месяцев назад