Over the past year, we've been amazed by what our users have accomplished with Felt. Teams from various industries have used Felt to align and deliver on complex projects, inspiring us to further our mission and make mapping effortless for everyone. Today, we are thrilled to introduce Felt 2.0, which includes a groundbreaking set of spatial analysis features that make data processing significantly more accessible. Alongside these new capabilities, we are also launching a redesigned Felt interface, making common workflows faster and easier.

First-of-their-kind spatial analysis tools

Every day, teams are solving problems by transforming spatial data into insights.

Here are a few examples of questions that geospatial tools can answer:

1. Which farmlands are producing the most crops?
2. Which buildings were most damaged by a wildfire?
3. Which sites are best suited for power generation?

Traditionally, answering these kind of mapping questions has required complex desktop software and hours of waiting, resulting in a frustrating and slow process. With the launch of Transformations, we are revolutionizing this approach, making it easy to move quickly and confidently through your workflow. The core transformations we're launching today include Bounds, Buffer, Centroid, Clip, Count points, Dissolve, Intersect, and Subtract.

What sets this apart from other approaches is Live Previews. With Live Previews, you can see exactly what you'll get before running an analysis, eliminating guesswork. This is just the beginning of our spatial analysis tools, with many more to come.

Upload anything, visualize instantly

One of the most powerful features of Felt is its ability to instantly visualize any file format. Instead of relying on you to tell Felt about your dataset, we take care of it behind the scenes. Simply drag and drop your files onto the map, and Felt handles the rest. Here are just a few examples of supported file formats:

• Vector Data: Shapefiles, GeoJSON, GeoPackage, Geodatabase, KML, GPX;
• Raster Data: GeoTIFFs, GeoPackages, PNGs with World files, ASCII grids;
• Spreadsheets: Automatically geocode addresses and join against census and EuroState data;
• URLs: XYZ Raster Tiles, ESRI MapServer & FeatureServer, Hosted Files, WMTS, PMTiles.

Behind the scenes, we leverage our open-source tiling engine, Tippecanoe, combined with machine learning to produce highly performant, automatically-projected versions of your data. Our in-house rendering engine, built on top of MapLibre, allows you to easily style vector and raster data with just a few clicks, achieving high-quality visualizations with solid cartographic defaults. Visualizations styles include categorical, choropleths, heat maps, size-by, hillshades, and dynamic raster styling.