Hello friends, thanks for following my journey so far. To catch you up, I'm trying to solve the problem of farmers and traders relying on weak and untimely predictions of corn yield. Weak because they are at the national level and untimely because the predictions come once a month.

So here's the deal: farmers and traders have been relying on national-level corn yield predictions that are not only weak but also painfully slow, arriving just once a month. Imagine making critical decisions based on a single data point each month.

Not ideal, right? That's exactly the issue we're tackling in this blog post series.

VIDA uses dozens of the latest generation of climate models to have the most up-to-date climate information, collectively part of the Coupled Model Intercomparison Project 6 (CMIP6). These models provide a range of possible futures, under different emission scenarios, as well as differences in how each model does its forecast. Using this information, we can create ensembles from the models, and have higher confidence in the risks and hazards we derive from the models, and which we present to our customers.

In this blog post, I show how I created a UDF to pre-process and visually inspect the Zarrs we generate as the output from our climate risk models.

Dr. Qiusheng Wu is an Associate Professor of Geography and Sustainability at the University of Tennessee and a Founding Editorial Board Member at the Cloud-Native Geospatial Forum (CNG). As part of his commitment to making open-source geospatial analysis and visualization more accessible, he has developed several widely used open-source packages, including geemap, leafmap, and segment-geospatial.

In this Notebook Qiusheng shows a few examples of how Cloud Native Geospatial datasets help you easily load data into a Jupyter Notebook environment using leafmap. His practical examples showcase how you can call the Overture Maps UDF and Foursquare Places UDF to load data into a custom area of interest and render it in a leaflet map.

Naty is a Senior Software Engineer and a contributor to Ibis, the portable Python dataframe library. One of her main contributions was enabling the DuckDB spatial extension for Ibis in 2023.

In this blog post, she shows us how to leverage the spatial extension in DuckDB with Ibis to query Overture data. Ibis works by compiling Python expressions into SQL, you write Python dataframe-like code, and Ibis takes care of the SQL. Thanks to Ibis integration with Pandas and GeoPandas, you only need to do to_pandas() to get your expression as a GeoDataFrame.

On December 2023, I visited the Institute for Metropolitan Planning (IMP) in Lima. The director had invited me to share some of my geospatial analysis projects from my master's studies and explore potential collaborations. Around that time, Lima's mayor had announced a bold infrastructure initiative: building 60 flyover bridges to ease traffic congestion in one of the most gridlocked cities in Latin America.

When I asked how the city was simulating the impact of new network designs on urban mobility, the answer was: "We are not simulating anything, our budget is constrained, and there is no political will to solve this problem." I couldn't think of anything else after this meeting. I started thinking about how I could create an easy-to-use tool to simulate urban mobility using open-source data, tools with no subscriptions or licenses, and without data privacy concerns.

My first attempt with FastAPI and React came to an unfortunate halt. Fused allowed me to revisit the idea and easily create an API endpoint and lightweight app I could easily share with anyone.

As a research specialist focused on remote sensing applications in semi-arid rangelands, I'm constantly seeking tools that can enhance our ability to process and analyze large-scale geospatial data. The excitement of discovering new platforms that streamline complex workflows never gets old, especially when dealing with the massive datasets typical in remote sensing research.

My journey with Fused began unexpectedly through the "Minds Behind Maps" podcast, where host Maxime Lenormand interviewed Sina Kashuk, Co-Founder and CEO of Fused (see episode). The conversation sparked my curiosity, leading me to explore Fused's community examples and documentation. After joining their waitlist and receiving access, I knew exactly how I wanted to test it: an interactive tool for exploring NEON's Airborne Observation Platform (AOP) data.

Sameer, co-founder of DigitalTwinSim, leads the development of advanced geospatial analysis tools to support the telecom industry in strategic network planning. DigitalTwinSim specializes in using high-resolution data to optimize the placement of network towers ensuring reliable, high-speed connectivity.

In this blog post, Sameer shares how he leverages Ibis with a DuckDB backend, and Fused to model wireless networks at high resolution. This approach enables him to quickly generate network coverage models for his clients. He explains and shares a Fused UDF that processes data in an H3 grid to evaluate optimal locations for network towers.

Check out Sameer's UDF on Workbench here.

Foursquare just released an open dataset of over 100M global places of interest.

We at Fused have partitioned these points into easily accessible GeoParquet files, and hosted them on Source Cooperative

On top of that, we've build a publicly available User Defined Function (UDF) that anyone can use to easily look at & download to GeoJSON, all from the browser

Try it out for yourself!

You don't need to login or create an account to easily access the Foursquare POI points

• Join our waitlist to get full access to UDFs that you can edit in your browser
• Get familiar with Fused by checkout out our Quickstart docs
• Follow us on LinkedIn to keep up with updates
• Read this more in-depth look at the whole dataset from our community member Mark Litwintschik

I am a self taught developer and data enthusiast. I first came across the spatial data community when I saw a Matt Forrest video on LinkedIn where he demonstrated how to visualize buildings from the Vida Combined Building Footprints dataset with DuckDB. Immediately I thought, what if you could see all the buildings in a country, say, Egypt? I set out to do just that and made this map with DuckDB and Datashader.

Buildings in Egypt.

Neighborhoods within a city have consistent characteristics but often have ill-defined boundaries. Some neighborhoods are more similar than others even though they’re not nearby. Understanding these local boundaries and the demographics, dynamics and behaviors of different areas affects a wide range of business applications, including advertising, site selection, business analytics, and many more.

The rate of prototyping the Fused App Builder unlocks is unrivaled. Recently I used it to create a sophisticated prototype app to accelerate my ML workflow. You can follow along by using the "Cube Factory" app I built.

GLS (General Logistics Systems) is an international parcel delivery service provider, primarily operating in Europe and North America. To stay ahead in the fast-paced logistics industry, GLS launched GLS Studio—an innovation lab aimed at optimising and modernising its depots and processes through cutting-edge technology.

Stefano co-founded GLS Studio to build the next generation of data-driven products. In this post, he shares how GLS Studio uses Fused to drive efficiency and innovation in parcel delivery.

In this blog post, Stefano shows how his team powers GLS's ParcelPlanner app, which helps GLS evaluate delivery routes efficiently. The app uses Fused to query Snowflake and serve H3-partitioned geospatial data to the frontend, which is powered by Honeycomb Maps and DuckDB WASM.

A raster, a vector, and an array walk into a bar…

Ok I will spare you the corny jokes.

But seriously, I was facing a problem with these three data types when I approached Fused. It felt impossible to join this information together in a meaningful way. Fortunately, I was quickly proven wrong with the power of UDFs. Let me catch you up.

In this video from the FOSS4G 2024 conference, Isaac Brodsky, CTO and co-founder of Fused, shows the power of combining H3 with DuckDB to enhance geospatial data analysis.

Over the last few decades, it has become increasingly evident that passenger vehicles are by far the most dangerous way to travel. As a result, it has become more and more important to find an efficient and effective method to predict traffic risk. However, predicting traffic accidents and where they are likely to occur is a very complex problem, with large amounts of data being needed for most meaningful predictions.

At Pacific Spatial Solutions, we are currently trying to tackle this problem by training a machine learning model to predict road and intersection risk in Japan nationwide. As we are trying to predict traffic risk on a national level it is only natural that the data we use cover the same area.

High-quality satellite imagery is essential to assess the carbon impact of nature-based forest conservation and restoration projects [1]. However, getting that high quality imagery is uniquely difficult in areas that need carbon financing the most: tropical forests. Tropical forests present a unique challenge for satellite imagery analysis due to persistent cloud cover, which often renders optical imagery unusable and creates data gaps.

Example composites highlight how the HLS-L30 product alone can have gaps when attempting to make a seasonal composite, as fewer cloud-free observations.

Ever tried to make sense of the myriad file types in spatial data science and felt like you've wandered into a linguistic labyrinth? Trust me, you're not alone. As a data scientist who's spent more time wrangling datasets than I care to admit, I thought I'd take a casual stroll down memory lane with an old high school friend: regression models. Just a simple plot of actual vs. predicted, right? But when spatial data's involved, you can't just sit back and relax—you've got to keep one eye on the geometries.

I'm currently working on an agricultural project, and growing up on a farm gives me a personal stake in this. This blog illustrates my solution to the geometry debacle. I'll first take you to the area where I grew up: Lyon County.

Overture is an open data project that publishes interoperable map datasets. It aims to foster an ecosystem of developers creating downstream map services around its data products. Fused emerged as a solution to enrich Overture datasets on the fly and serve them with XYZ Tile endpoints.

This clip shows how coverage expands in (top right) Astoria when I add building heights from the NSI dataset (as num_story * 3) to Overture buildings.

This article explores a User Defined Function (UDF) that utilizes global precipitation data to compare rainfall patterns between different locations worldwide and then creates an interactive app!

Fused is a powerful and versatile tool that can do nearly anything with just Python. Its versatility is its strength, but it is also an obstacle. It's easy to walk about wondering: what, concretely, can Fused do for me?

Here are six concrete ways you can use Fused today.

In this article I show how to create an object detection layer on 50cm imagery in realtime. It explains how to create a Fused User Defined Function (UDF) to load satellite image tiles to call an inference model, then publish it as an interactive map app.

In this video tutorial, I show a complete data app workflow in Fused. Starting with exploring the data in Fused, the tutorial walks through developing a UDF to serve the data, and then a Fused App to share results.

With Fused, this whole workflow takes just minutes from beginning to end. Fused helps me visualize the data at every step, iterate on my analytical logic, and finally publish a dashboard.

In an era where data-driven decisions are vital, accurate and scalable vegetation analysis plays a crucial role across various industries, from environmental monitoring to urban planning. While AI and machine learning have transformed image analysis, they often bring complexities and resource demands that aren't always practical for large-scale, real-time applications.

Pachama is a technology company that harnesses satellite data and AI to empower companies to confidently invest in nature. The engineering team at Pachama created a Land Suitability Tool to help landowners and project developers qualify parcels of land to implement carbon projects. They turned to Fused to simplify their data workflows.

Isaac Brodsky, the CTO of Fused, delved into the power of Fused during a LinkedIn live session with Matt Forrest. They discussed the contrast of Python vs. SQL for data analytics, the advantages of serverless geospatial processing, and showcased a live demo of the UDF Builder. During the demo, Isaac created a User Defined Function visualize Overture building footprints that are within a certain proximity of water.

The combination of Fused serverless operations and DuckDB offers blazing fast data processing. Fused embraced Python to create serverless User Defined Functions (UDFs). Now, with the help of DuckDB, Fused enables developers to leverage the ease and familiarity of SQL in these functions  -  without compromising performance and parallelism.

Fused is a modern geospatial toolkit for companies to code, scale, and ship geospatial workflows of any size.

This week we are unveiling Fused, a toolkit to enable interoperability between all geospatial datasets and tools in the modern data stack. Fused is the glue layer that integrates data platforms with data tools via a managed serverless API.

Fused enables interoperability between datasets and tools in the modern data stack. It's a glue layer to integrate data platforms with data tools via a managed serverless API.