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Analyzing large vector datasets on Source Cooperative with #DuckDB 🦆Using the 75 GB National Wetlands Inventory as an example. It used to take me hours to get summary statistics of the dataset, now it takes seconds with DuckDB 🚀 Dataset: Colab: #geospatial #dataviz #opendata

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Qiusheng Wu

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13,309 görüntüleme • 2 yıl önce •via X (Twitter)

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Spatial AI ( is building large real-world datasets to teach robots how to navigate the world and complete tasks. Their first open source dataset, SEA (Spatial Everyday Activities), is the largest curated egocentric dataset of people carrying out real tasks, with 10,000 hours of data.
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Spatial AI ( is building large real-world datasets to teach robots how to navigate the world and complete tasks. Their first open source dataset, SEA (Spatial Everyday Activities), is the largest curated egocentric dataset of people carrying out real tasks, with 10,000 hours of data.

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What Postgres can’t do alone, it can tackle with DuckDB. The pg_duckdb extension shows how two databases can work hand in hand by querying analytical data with DuckDB and joining it with transactional data in Postgres. The extension embeds DuckDB directly into Postgres. DuckDB literally runs inside a Postgres process. How it works (see diagram): 1. An app sends a query to Postgres joining portfolio data (Postgres) with historical market data (lakehouse) 2. pg_duckdb detects lakehouse access and delegates execution to DuckDB 3. DuckDB pulls required market data from the lakehouse, queries Postgres tables, and performs the join 4. Postgres returns the final result to the app

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Google just released the AlphaEarth Foundations with 64 dimensions of satellite embeddings at 10-m resolution at the global scale! It is very interesting! Check it out Blog post: Dataset: Paper: #AI #geospatial #remotesensing #geoai
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Google just released the AlphaEarth Foundations with 64 dimensions of satellite embeddings at 10-m resolution at the global scale! It is very interesting! Check it out Blog post: Dataset: Paper: #AI #geospatial #remotesensing #geoai

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One line of code. That’s all it takes to get access to Google Open Buildings, the largest building dataset, for any country. 100% free and available globally.
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One line of code. That’s all it takes to get access to Google Open Buildings, the largest building dataset, for any country. 100% free and available globally.

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you can now get data from auth'd APIs with DuckDB 🤯 ya know, to get your stripe data or whatever (it's not documented yet)
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you can now get data from auth'd APIs with DuckDB 🤯 ya know, to get your stripe data or whatever (it's not documented yet)

archie 🦋

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We know it takes the world to map the world. Together with our partners at Overture Maps Foundation, we've launched the first worldwide open map dataset that will power the next generation of innovations in mapmaking. What will you make with it? Read more:

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🌎Visualizing the Agricultural Field Boundary dataset - Fields of The World (FTW, - using #leafmap on PyCafe ☕️ Check it out: Chris Holmes
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🌎Visualizing the Agricultural Field Boundary dataset - Fields of The World (FTW, - using #leafmap on PyCafe ☕️ Check it out: Chris Holmes

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A Rerun Viewer for the DROID Dataset! DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset is a robot manipulation dataset by Alexander Khazatsky et al. with 76k demonstration trajectories or 350h of interaction data, collected across 564 scenes and 86 tasks.
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A Rerun Viewer for the DROID Dataset! DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset is a robot manipulation dataset by Alexander Khazatsky et al. with 76k demonstration trajectories or 350h of interaction data, collected across 564 scenes and 86 tasks.

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📢📢𝐍𝐞𝐑𝐒𝐞𝐦𝐛𝐥𝐞 𝐯𝟐 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 𝐑𝐞𝐥𝐞𝐚𝐬𝐞📢📢 Head captures of 7.1MP from 16 cameras at 73fps: * More recordings (425 people) * Better color calibration * Convenient download scripts The new version of our dataset adds 156 participants for a total of 425 different people. In its entirety, the dataset provides now 65 million images from over 15 hours of diverse human facial expression performances. We improved the color consistency of the recorded images with a better color calibration procedure. As a result, 3D reconstructions with images from the NeRSemble dataset should become better and look more realistic. Finally, we made it much easier to download the recordings with our new download repository. It now just takes a single command to download all frontal hair shake videos of all participants or to download all recordings of a single participant. Check it out: Awesome work by Tobias Kirschstein Simon Giebenhain !!!

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Today we’re releasing SAIR, the Structurally Augmented IC50 Repository. SAIR is the Largest Open-Sourced Binding Affinity Dataset with Cofolded 3D Structures. It includes more than 5 million protein-ligand structures, generated using our Large Quantitative Models and labeled with binding affinity data. By providing this unprecedented scale of structure-activity data, we aim to enable researchers to train and evaluate new AI models for drug discovery, bridging the historical gap between molecular structures and drug potency prediction. The SAIR dataset was created using the NVIDIA DGX Cloud and is now publicly available on the Google Cloud Platform. Access and build with SAIR today! 📰Read the Press Release: 📥Learn More and Download the Dataset at #DrugDiscovery #LQMs #SAIR #AIforScience #SandboxAQ
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Today we’re releasing SAIR, the Structurally Augmented IC50 Repository. SAIR is the Largest Open-Sourced Binding Affinity Dataset with Cofolded 3D Structures. It includes more than 5 million protein-ligand structures, generated using our Large Quantitative Models and labeled with binding affinity data. By providing this unprecedented scale of structure-activity data, we aim to enable researchers to train and evaluate new AI models for drug discovery, bridging the historical gap between molecular structures and drug potency prediction. The SAIR dataset was created using the NVIDIA DGX Cloud and is now publicly available on the Google Cloud Platform. Access and build with SAIR today! 📰Read the Press Release: 📥Learn More and Download the Dataset at #DrugDiscovery #LQMs #SAIR #AIforScience #SandboxAQ

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Display a video on an interactive map 🗺️ with #MapLibre and #Leafmap. Check it out: Map: GitHub: Notebook: #geospatial #opensource #dataviz #python

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This is the power of YOLO, trained on a laptop for ~1 hour, with a Kaggle dataset. Oh, and just ~100 lines of Python. I can make a startup on this and it took me literally a couple of hours.

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With only one line of code, you can get access to Google Open Buildings, the largest building dataset, for any country.
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With only one line of code, you can get access to Google Open Buildings, the largest building dataset, for any country.

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Football players detection + tracking using Ultralytics YOLO26 ⚽️ It took me 30 minutes to train the YOLO26 model on a custom dataset using Ultralytics Platform. Later, I used the object tracker (Bytetrack) for tracking the players; tracking could be improved further, and I am working on it this week. More info 👇 #soccer #football #MachineLearning

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🇶🇦 Qatar Airways flight QR339 used to go Doha to Moscow in about 5 hours, cutting straight through Iraq and Turkey. Now with airspace closing, it takes around 7.5 hours and swings way east over the Caspian Sea to get there. That extra 2.5 hours burns about 20,000 kg more fuel, adding roughly $20,000 to $25,000 per flight for the airline. Open the flight map and it’s basically a ghost town now. Another example of how this war is quietly messing with the global economy. Source: Flightradar24

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🚀 The #GeoAI Python package now supports feature segmentation from high-resolution satellite and aerial imagery using text prompts, such as trees, buildings, etc. It efficiently processes large datasets with automatic tiling and can save results as a single image. Stay tuned for more features coming soon! 📓 Access the notebook: 🛠️ Explore the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python #DeepLearning
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🚀 The #GeoAI Python package now supports feature segmentation from high-resolution satellite and aerial imagery using text prompts, such as trees, buildings, etc. It efficiently processes large datasets with automatic tiling and can save results as a single image. Stay tuned for more features coming soon! 📓 Access the notebook: 🛠️ Explore the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python #DeepLearning

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How to deal with very messy dataset in Excel.
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How to deal with very messy dataset in Excel.

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You can now build on top of the Aviato dataset. Enrich your platform with our 700M+ people records and 8M+ company profiles.
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You can now build on top of the Aviato dataset. Enrich your platform with our 700M+ people records and 8M+ company profiles.

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Heads up! Mosaic dropped a pretty wild dataset of 1.26 million 360° images of Prague 🤯 If you're a researcher, creator or developer into 3D/AI/Geo, I think you're gonna wanna play with this Here's the scoop on this 15 TERAPIXEL dataset & the crazy things you can do with it 🧵

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