Wherobots

What is Wherobots? It's a question we get asked sometimes, so we wanted to make it super easy to understand. That's why we created this handy explainer video. Check it out here 👇

🎉 We're excited to welcome Matthew Powers, CFA as our new Staff Developer Relations Engineer! Matthew joins us from Databricks, and he’ll be working closely with the Apache Sedona community. Here’s a little more about him: "I’m a long-time contributor, blogger, and OSS developer in the Spark and Dask communities, and I’m excited to introduce Sedona to the Spark ecosystem! I originally worked in finance, but taught myself how to code and transitioned to data engineering. I’m looking forward to deepening my knowledge of geospatial data, understanding common production workloads, and helping grow the Sedona community of contributors and users."

Missed our session at AWS re:Invent? Here’s the TLDR: 🛰️ Inferring objects and detecting change in satellite imagery was once reserved for companies with the resources to build and manage complex ML inference solutions. 🖥️ Raster Inference is a fully managed computer vision solution built for planetary-scale imagery. - Pick the model you want or bring your own. - Optimized for raster ETL and computer vision. - On-demand, low-cost inference. - Enrich inference results in line with your workflow. Raster Inference is now generally available! Reach out to us for more info or link in comments on how to get started. 👇 https://lnkd.in/gDYZ569b

🚲 If you want to really learn how to work with data, you need to work with messy data. This post from Zhengpei Luo does a great job of showing this with the Citi Bike Dataset which is...messy. Here's why: 🤐 The data is stored in zipped directories (by year) or zipped CSVs by month (but just for 2015 and forward). 😡 The dataset changes schemas 3 times and in the final update the point is tied to the exact pickup location, not the station location. Plus there is a lot of data: 53,083,660 total trips. But here is the data for you to check out! This is just a sample of trips in June 2024 that I processed in Wherobots! #gis #moderngis #geospatial #spatialanalytics #spatialsql #duckdb #wherobots

🌍 Exciting news! WherobotsAI Raster Inference is Generally Available! 🔍 What is it? A planetary-scale computer vision solution that extracts insights from aerial imagery at scale. What's in GA? 🚀 Wherobots-hosted Models: Choose from our optimized, ready-to-use computer vision models to analyze aerial data 💫  Bring Your Own Model: Easily port in your own models and leverage our inference pipeline to get to critical data insights faster 🏎️ Fast Performance: We've made our already fast inference even faster with async data loading ⚡ Effortlessly Scale: Deploy a scalable, serverless inference pipeline that seamlessly scales with increasing workload needs. 💡 Why it matters: Easily extract information at scale from noisy overhead imagery data with a geospatial optimized computer vision platform. Read more about it on our blog: https://bit.ly/3VErLRy On Business Wire: https://lnkd.in/gfbWA4X4 #WherobotsAI #RasterInference #ComputerVision #GeospatialAI #DataScience

🌍 In today’s data-driven world, geospatial data is being collected at an unprecedented scale, yet traditional data lakes and warehouses struggle to manage its complexity. From GPS locations to satellite imagery to IoT sensor data, Havasu is Wherobots' spatial data lakehouse solution. Based on Apache Iceberg, it's designed to help organizations: ✔️ Achieve performance at scale ✔️ Store massive volumes efficiently ✔️ Streamline data management 🔓 Learn how Havasu bridges the gap, enabling organizations to unlock the full potential of their geospatial data with ease and efficiency. https://bit.ly/4gzq5AE

Check out the recap of the GeoParty we hosted with CARTO at #AWSreInvent last week! 👀🌍🎉

Check it out! So grateful to Wing Venture Capital for their ongoing support both in our Series A as well as featuring us in their billboard coming off the 101 into San Francisco. 👀 ➡️ Be sure to check it out if this is part of your regular commute! 💙

🚀 What an incredible week at #AWSreInvent! It was amazing to connect with both new and old friends and learn about all the exciting projects everyone is working on. 🌟 Key highlights: 1) We’re thrilled about AWS’s announcement of S3 Tables, which will continue to make Apache Iceberg a key component of open data lake architectures. This means that geospatial engines like Apache Sedona and Wherobots can now process S3 table data and read geospatial columns in WKB format. 🎉 2) It was great connecting with so many people at AWS re:Invent, learning about how they’re currently working with geospatial data, the tools they’re using, and how Apache Sedona is solving these needs today. ⚙️ 3) We hear you on how challenging and manual it can be to extract insights from imagery data. You can leverage AI/ML to gain faster insights and help lift the heavy load. Talk to us to learn more! 🤖 4) A big shout-out to the AWS Geo and Earth Observation teams for their curiosity and collaboration. We’re grateful to be welcomed into their community with such open arms. 🌎

🚀 What an incredible week at #AWSreInvent! It was amazing to connect with both new and old friends and learn about all the exciting projects everyone is working on. 🌟 Key highlights: 1) We’re thrilled about AWS’s announcement of S3 Tables, which will continue to make Apache Iceberg a key component of open data lake architectures. This means that geospatial engines like Apache Sedona and Wherobots can now process S3 table data and read geospatial columns in WKB format. 🎉 2) It was great connecting with so many people at AWS re:Invent, learning about how they’re currently working with geospatial data, the tools they’re using, and how Apache Sedona is solving these needs today. ⚙️ 3) We hear you on how challenging and manual it can be to extract insights from imagery data. You can leverage AI/ML to gain faster insights and help lift the heavy load. Talk to us to learn more! 🤖 4) A big shout-out to the AWS Geo and Earth Observation teams for their curiosity and collaboration. We’re grateful to be welcomed into their community with such open arms. 🌎