The Problem

◆ Exponential Growth in Collection Capacity

Driven by the development of advanced sensors and increased spectral diversity, new technologies now enable the capture of more detailed and varied data across different wavelengths. These innovations allow for greater accuracy and resolution in a growing number of remote sensing applications, both on Earth's surface and in the atmosphere.

◆ Wasted Data and Untapped Potential

Despite the vast amount of data generated by new remote sensing technologies, much of it remains underutilized as organizations struggle to manage and process the influx. Additionally, the advanced capabilities of new sensors often require specialized training, further limiting the ability to harness their full potential.

◆ Rapidly Evolving Targets

The rapidly evolving landscape of remote sensing demands quicker adaptation to meet user needs, as emerging applications require more precise and timely data. This is especially true for identifying rare or elusive targets, where delays in adapting to new sensor capabilities can result in missed opportunities or incomplete insights.

The Solution

The Data

Self-Supervised Learning unlocks Petabytes of unlabeled Remote Sensing Data.

The Tech

Remote Sensing Foundation Models enable Multi-Modal Latent Space Alignment.

The Bedrock

Rich, Semantic Labels from Diverse Open Source Data is the Backbone.

The Solution

Why Bedrock?

Choosing Bedrock Research means partnering with a company that delivers advanced capabilities tailored to complex needs. Our cross-modal foundation models go beyond traditional single-sensor approaches, offering seamless integration of different data types. With a commitment to consistent, mission-ready flexibility, Bedrock Research ensures reliable outcomes, no matter the sensor or objective.

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