Rymba is an AI-driven environmental intelligence platform that analyzes real-time forest, land-use, and environmental data to predict and mitigate climate-related health crises. It uses satellite imaging, drone-based LIDAR scanning, and machine learning to provide actionable insights for anticipating disease outbreaks, air pollution risks, and extreme weather-related health impacts.
Funding
Funding not disclosed
Founders
Product
Problem
Forests face increasing threats from climate change, unsustainable land use, and environmental degradation, impacting biodiversity, carbon sequestration, and overall ecosystem health. Monitoring these vast and complex ecosystems using traditional methods is often costly, time-consuming, and lacks the real-time insights needed for effective management and conservation.
Solution
Rymba offers an AI-driven environmental intelligence platform that provides accurate, real-time insights for forest monitoring, biodiversity protection, and carbon management. The platform leverages a combination of drone-based LiDAR scanning, satellite imagery, and advanced machine learning algorithms to deliver comprehensive data on forest health, species identification, and carbon stock assessments. By providing actionable intelligence, Rymba empowers organizations to make informed decisions, optimize resource allocation, and implement sustainable forestry practices. The platform enables near real-time monitoring of forest ecosystems, facilitating early detection of changes and supporting effective conservation efforts.
Target Audience
Rymba serves forestry departments, conservation organizations, businesses, and government bodies involved in forest management, biodiversity conservation, and carbon reduction initiatives.
Features
- High-resolution data collection using drones, LiDAR, and satellite imagery
- AI-powered analytics for accurate forest monitoring and change detection
- Biodiversity index calculation and ecosystem impact assessments
- Carbon stock calculation for carbon credit projects, verification, and compliance
- Near real-time monitoring of forest health and biodiversity
- Multispectral and hyperspectral data analysis for topography assessment and vegetation analysis