📋 Tender Overview
Sber has presented Verda (<<green>> in Esperanto), an all-new AI-powered land restoration solution designed for end-to-end operations to remedy degraded land and desertification. The system is already tracking and assessing soil changes over time with the help of artificial intelligence. Moving forward, it will make forecasts and recommend land restoration measures. The presentation took place at the CIPR Forum. The Verda initiative is part of an agreement between Sber and the Republic of Kalmykia, which they signed at AI Journey 2024. The technological solution was presented by Anastasia Fedotova, managing director and head of Sberbanks ESG Directorate, at the Russian Ministry of Natural Resources and Environments session <<Development of Digital Infrastructure in Ecology and Nature Management.>> The system combines advanced methods of analyzing remote sensing data and machine learning technology. It tracks and classifies land according to the degree of degradation and evaluates the effectiveness of restoration, from planting protective forest belts to changes in land use. Based on the data, Verda shows areas where phytomelioration and other conservation measures should be implemented first. The Verda pilot is being implemented in the Republic of Kalmykia, one of the regions with the highest risk rate, with further consideration being given to scaling the solution to other parts of Russia. According to the UN, the annual global damage from desertification reaches USD 878 bn, with investments in land restoration yielding up to USD 8 for every dollar invested. The use of digital technology and AI can significantly reduce costs and increase the impact of conservation. At CIPR, Sber and partners present technologies capable of transforming everyday life, business processes, and key industries, focusing on innovative solutions powered by artificial intelligence, including the GigaChat 2.0 neural network model. These designs significantly increase efficiency when solving complex tasks and implementing large-scale projects.
