Quantas Labs Secures Funding for AI Erosion Prediction
Quntas Labs, a London, UK-based commercial AI erosion impact prediction company, has recently secured $550K in Seed funding. The funding round saw participation from Faisal Al-Rajhi and other undisclosed investors. The company plans to utilize the funds to expand the adoption of its erosion solutions for sustainable land management. Led by CEO Alex Pourquery, Quantas Labs aims to serve various beneficiaries such as local governments, environmentalists, and the agricultural and construction sectors.
At the core of Quantas Labs' technology is a proprietary hybrid AI framework.
This framework combines a multi-layered convolutional neural network (CNN) architecture with recurrent neural network (RNN) components. By integrating these two neural network models, the company's technology facilitates the processing of complex geospatial datasets and temporal data sequences, enabling accurate and dynamic erosion predictions.
Quantas Labs' approach to erosion prediction encompasses several key components. Firstly, the company leverages API-driven real-time climatic data for responsive erosion forecasting. This real-time data integration ensures that predictions are up-to-date and can adapt to changing weather conditions. Additionally, Quantas Labs utilizes hyperspectral imaging to classify soil types, which is crucial for erosion assessments. By differentiating between different soil types, the company can provide targeted solutions based on the specific characteristics of the land. Lastly, Quantas Labs implements data augmentation and semi-supervised learning models to overcome the challenge of data sparsity, particularly in remote areas. This robust data handling approach helps ensure the accuracy and reliability of their erosion predictions, even in regions with limited data availability.
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