LGND (pronounced "legend") is a geospatial AI company on a mission to make Earth understandable — for people and AI alike. We harness geospatial embeddings and large Earth observation models trained on satellite and aerial imagery to turn billions of images into a living, queryable record of the planet.

The result is infrastructure that delivers planet-scale datasets faster and more affordably than traditional pixel-by-pixel approaches. You can explore Earth through natural language chats, interactive maps, and developer APIs.

Traditional image classification required hundreds or thousands of annotated examples and custom-trained models for every new task to become expert “recognizers”. That meant months of data labeling and expensive ML infrastructure just to answer a simple question like "how many solar panels are in Texas?"

Large Earth observation models like Clay change the economics entirely. They're trained on massive corpora of satellite imagery and can generalize to new tasks with just a handful of reference examples. LGND wraps this capability into production-ready APIs and no-code applications — so organizations can get answers in hours instead of months, at a fraction of the previous cost.

Yes! LGND's API is designed to work with your own imagery sources, not just the built-in Sentinel-2 or NAIP feeds. You can submit custom raster tiles — from commercial satellite providers, drone captures, airborne sensors, or proprietary archives — and LGND will generate embeddings using the Clay foundation model.

This is particularly valuable for teams with access to higher-resolution commercial imagery (e.g., Vantor, Planet, Airbus) or specialized sensors (SAR, LiDAR-derived rasters, hyperspectral) that require custom analysis beyond publicly available data.

LGND works with standard geospatial raster formats. Key considerations for bringing your own imagery:

Format — GeoTIFF and cloud-optimized GeoTIFF (COG) are the preferred formats

Tile size — typically, chips are processed at 256×256 pixels; your imagery will be tiled automatically at this dimension but other dimensions are possible to set

Bands — any multispectral imagery with wavelengths within or near the pre-training range can be used; the model's wavelength encoding ensures robust generalization

Projection — standard projections (WGS84, UTM) are supported

Contact the LGND team for guidance on integrating custom sensor types or non-standard imagery sources.

LGND generates, hosts, and indexes the resulting embeddings for you. Once processed, your embeddings are accessible via the API for vector similarity search, classification, and downstream analytics — without you needing to manage a vector database or embedding infrastructure.

Embeddings are versioned and linked to their source imagery metadata (location, timestamp, sensor), making it straightforward to track lineage and run time-series analyses across imagery updates.

Yes. A common workflow is to use LGND's hosted Sentinel-2 or NAIP embeddings for broad-area search and screening, then refine results using higher-resolution custom imagery for the subset of locations that require more detail. This hybrid approach balances coverage and resolution cost-effectively.

The LGND API allows you to work across multiple embedding sets within the same project, enabling multi-source workflows without duplicating annotation work.

LGND currently hosts the Clay foundation model. Additional open-source models will be available in the near future. 

LGND can be run on any sized area. The unit of analysis is a raster tile. A raster tile represents a single remotely sensed image (satellite, aerial, drone) for a specific location on Earth and acquired at a specific time.

Chips are typically 256x256 pixels. A Sentinel-2 chip  that is 256 – where each pixel is 10 meters - would therefore have a corresponding area of 2.5km^2. 

LGND unlocks significant accuracy with just a few reference examples. Accuracy depends on many factors: how much training data is provided, how distinct an object is relative to its surroundings, and how variable the object is over space and time. It is rare for a model to work perfectly out of the box. Like with other AI tools, LGND’s analytics are refined through user prompting and feedback. 

Typically, model training requires a few minutes to hours. Inference takes a few minutes to hours. Both depend on the number of labels used (training) and the area of interest (inference).

The Clay model was trained on 10 bands from Sentinel-2 imagery, 10 bands from Landsat imagery, and all four bands of NAIP.

Wavelengths are encoded in the model. It can therefore extrapolate to wavelengths that are within or near the ranges used for pretraining.

You can run a model as many times as you’d like. If you're studying a phenomenon that changes frequently, you can run your model on each update of imagery. Landsat and Sentinel-2 offer updates roughly every five days. NAIP imagery updates every other year.

Models can be run each time new (cloud-free!) imagery becomes available.