LGND is a new data technology startup founded by Nathaniel Manning and Bruno Sánchez-Andrade Nuño. They’re on a mission to change the way everyone—from city planners to self-driving cars—uses and interacts with geospatial data. The ultimate goal of the company is to be the “Standard Oil” of data. It aims to create geographical embeddings that best represent spatial information and enhance efficiency.
This is the heart of the company—that unique intersection of advanced technology and applied environmental science. LGND is transforming how we query geospatial data with their powerful embeddings. Their unique approach allows them to create the process 10 to 100 times more efficient than conventional methods. The technology allows users to quickly identify relationships between various geographical points, facilitating a range of applications from urban planning to vacation rentals.
Traditional geospatial models are more time-intensive and would have a difficult time with several queries at once. LGND’s approach leverages neural networks to efficiently train algorithms that can discern features in satellite imagery, which captures approximately 100 terabytes of data daily. This part of the capability is arguably the most important because it allows single family neighborhood level or even block level search criteria.
Nathaniel Manning, the company’s CEO, emphasizes the consumer-focused potential of LGND’s technology. He describes the need for precise information when searching for vacation spots, stating, “I want to be on a white sand beach. I want to know that there’s very little sea weed in February, when we’re going to go, and maybe most importantly, at this time of booking, there’s no construction happening within one kilometer of our house.” His recent observational insights emphasize the practical applications that LGND’s embeddings can make to everyday scenarios.
LGND’s chief scientist, Bruno Sánchez-Andrade Nuño, further explains the technical underpinnings of their embedding technology. “Embeddings get you 90% of all the undifferentiated compute up front,” he notes, highlighting the efficiency gained through their methodology. He goes on to describe how developing a dataset like that usually requires enormous investment—often hundreds of thousands of dollars—for little return.
“You probably sink, you know, couple hundred thousand dollars — if not multiple hundred thousand dollars — to try to create that data set, and it would only be able to do that one thing.” – Bruno Sánchez-Andrade Nuño
Despite the advanced capabilities of their technology, Nuño reassures stakeholders that LGND is not aiming to replace human professionals in the field. “We are not looking to replace people doing these things,” he states firmly. Rather, their intention is to spur innovation and productivity improvements in all sectors that depend on geospatial information.
The ground-breaking LGND story had caught the eye of high-profile investors in the tech world. Key stakeholders have included John Hanke of Keyhole / Google Earth fame, Karim Atiyeh, co-founder of Ramp, and Suzanne DiBianca, Salesforce executive. Their support further illustrates the increasing realization of LGND’s potential to make a positive impact in the data landscape.
LGND has been iteratively improving the geographic embeddings since its launch. It’s at the forefront of ushering in a new era for geospatial analysis. The firm combines new technologies with their expertise in environmental science. Both co-founders have PhDs in relevant fields from UC Berkeley, and they’re focused on unlocking new efficiencies and applications for users around the world.