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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...

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Qiusheng Wu

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25,904 просмотров • 10 месяцев назад •via X (Twitter)

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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
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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

12,194 просмотров • 11 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 • 9 месяцев 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

16,607 просмотров • 1 год 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

12,809 просмотров • 8 месяцев назад

🚀 GeoAI Tutorial 19: Train a Semantic Segmentation Model for Objection Detection from Remote Sensing Imagery 🌍 Leverage any PyTorch segmentation model from — with hundreds of image encoders & pretrained weights available. 🎥 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 19: Train a Semantic Segmentation Model for Objection Detection from Remote Sensing Imagery 🌍 Leverage any PyTorch segmentation model from — with hundreds of image encoders & pretrained weights available. 🎥 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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20,082 просмотров • 11 месяцев 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

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16,568 просмотров • 1 год 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

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

12,637 просмотров • 3 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

18,696 просмотров • 1 год 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

18,211 просмотров • 1 год назад

🚀 New Tutorial Alert! I just published a step-by-step guide on building an interactive dashboard with Python and Leafmap. 🌍📊 Explore the Jupyter Notebook: Watch the full video tutorial: #leafmap #geospatial #python #dataviz
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🚀 New Tutorial Alert! I just published a step-by-step guide on building an interactive dashboard with Python and Leafmap. 🌍📊 Explore the Jupyter Notebook: Watch the full video tutorial: #leafmap #geospatial #python #dataviz

Qiusheng Wu

16,755 просмотров • 9 месяцев назад

Google used Atlas to visualize its LLM embeddings 🗺️ - Find out what you can learn by interactively exploring 8M embeddings.
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Google used Atlas to visualize its LLM embeddings 🗺️ - Find out what you can learn by interactively exploring 8M embeddings.

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73,657 просмотров • 3 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

23,929 просмотров • 1 год назад

What if you could search 50M satellite image embeddings with no server, no database, and no API? Part 2 of Caleb Robinson and my series on Compressing Earth Embeddings is live at We binarized the global Clay v1.5 Sentinel-2 embeddings from 183GB → 7GB and built TerraBit — a retrieval demo that runs with zero backend. DuckDB-WASM, cleverly partitioned GeoParquet on S3, and brute-force Hamming in an in-memory index makes the experience feel too good to be true. If you also wondered how Google's TurboQuant fares on compressing earth embeddings -- don't worry, we got you covered in the blog -- hint: TurboQuant int4 is a solid choice
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What if you could search 50M satellite image embeddings with no server, no database, and no API? Part 2 of Caleb Robinson and my series on Compressing Earth Embeddings is live at We binarized the global Clay v1.5 Sentinel-2 embeddings from 183GB → 7GB and built TerraBit — a retrieval demo that runs with zero backend. DuckDB-WASM, cleverly partitioned GeoParquet on S3, and brute-force Hamming in an in-memory index makes the experience feel too good to be true. If you also wondered how Google's TurboQuant fares on compressing earth embeddings -- don't worry, we got you covered in the blog -- hint: TurboQuant int4 is a solid choice

Isaac Corley

42,624 просмотров • 2 месяцев назад

Today, we're introducing AlphaEarth Foundations from Google DeepMind , an AI model that functions like a virtual satellite which helps scientists make informed decisions on critical issues like food security, deforestation, and water resources. AlphaEarth Foundations provides a powerful new lens for understanding our planet by solving two major challenges: data overload and inconsistent information. 1️⃣ It combines information from dozens of sources to analyze the world's land and coastal waters in 10x10 meter squares, allowing for remarkable precision while tracking changes over time. 2️⃣ The system's key innovation is creating a highly compact summary for each square. These summaries require 16x less storage than those produced by other AI systems and enables scientists to create detailed, consistent maps of our planet, on-demand. AlphaEarth Foundations represents a significant step forward in understanding the state and dynamics of our changing planet. 🌎🌎🌎
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Today, we're introducing AlphaEarth Foundations from Google DeepMind , an AI model that functions like a virtual satellite which helps scientists make informed decisions on critical issues like food security, deforestation, and water resources. AlphaEarth Foundations provides a powerful new lens for understanding our planet by solving two major challenges: data overload and inconsistent information. 1️⃣ It combines information from dozens of sources to analyze the world's land and coastal waters in 10x10 meter squares, allowing for remarkable precision while tracking changes over time. 2️⃣ The system's key innovation is creating a highly compact summary for each square. These summaries require 16x less storage than those produced by other AI systems and enables scientists to create detailed, consistent maps of our planet, on-demand. AlphaEarth Foundations represents a significant step forward in understanding the state and dynamics of our changing planet. 🌎🌎🌎

Google AI

130,171 просмотров • 10 месяцев назад

Last week, we launched "Attention in Transformers: Concepts and Code in PyTorch" instructed by Joshua Starmer! In this course, you'll: ✅ Learn how the attention mechanism in LLMs helps convert base token embeddings into rich context-aware embeddings. ✅ Understand the Query, Key, and Value matrices, what they are for, how to produce them, and how to use them in attention. ✅ Learn the difference between self-attention, masked self-attention, and cross-attention, and how multi-head attention scales the algorithm. 🔗 Enroll for free:
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Last week, we launched "Attention in Transformers: Concepts and Code in PyTorch" instructed by Joshua Starmer! In this course, you'll: ✅ Learn how the attention mechanism in LLMs helps convert base token embeddings into rich context-aware embeddings. ✅ Understand the Query, Key, and Value matrices, what they are for, how to produce them, and how to use them in attention. ✅ Learn the difference between self-attention, masked self-attention, and cross-attention, and how multi-head attention scales the algorithm. 🔗 Enroll for free:

DeepLearning.AI

36,832 просмотров • 1 год назад

Here is a killer way to use embeddings: I built a notebook to show you how to do anomaly detection on images. Anomaly detection is one of the most common problems in the real world. Learn how to implement anomaly detection, and you'll be able to solve a ton of use cases that happen every day: • Predicting equipment failures • Identifying defective products • Identifying tumors in medical images • Spotting counterfeit products • Detecting fraud • Flagging fake reviews • Detecting bot traffic My notebook focuses on images. It uses: • Langchain • OpenClip embeddings • Oracle vector store This is the way my example works: • You start with a collection of similar images • You can then check whether a new image "belongs" to that collection I generate embeddings and use Oracle 26ai to store them and retrieve similar images on demand.
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Here is a killer way to use embeddings: I built a notebook to show you how to do anomaly detection on images. Anomaly detection is one of the most common problems in the real world. Learn how to implement anomaly detection, and you'll be able to solve a ton of use cases that happen every day: • Predicting equipment failures • Identifying defective products • Identifying tumors in medical images • Spotting counterfeit products • Detecting fraud • Flagging fake reviews • Detecting bot traffic My notebook focuses on images. It uses: • Langchain • OpenClip embeddings • Oracle vector store This is the way my example works: • You start with a collection of similar images • You can then check whether a new image "belongs" to that collection I generate embeddings and use Oracle 26ai to store them and retrieve similar images on demand.

Santiago

24,452 просмотров • 2 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

84,845 просмотров • 5 месяцев назад

How to Write Faceless AI Stories Videos Scripts in 10 Minutes (Tutorial) ▶️ Tools used⚙️: Gemini AI NoteGPT Google Docs See the Next Thread for Prompts used in the tutorial 🧵
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Himanshu - YouTuPreneur

28,650 просмотров • 11 месяцев назад

🚀 The GeoAI QGIS Plugin is here 🔥 You can run Moondream vision-language models, object detection, image segmentation (SAM 3), and even train your own geospatial segmentation model end-to-end. Website: GitHub: Short demo: Full video tutorial: #QGIS #GeoAI #SAM3 #Geospatial #DeepLearning #ComputerVision #OpenSource #Python
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🚀 The GeoAI QGIS Plugin is here 🔥 You can run Moondream vision-language models, object detection, image segmentation (SAM 3), and even train your own geospatial segmentation model end-to-end. Website: GitHub: Short demo: Full video tutorial: #QGIS #GeoAI #SAM3 #Geospatial #DeepLearning #ComputerVision #OpenSource #Python

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11,682 просмотров • 6 месяцев назад