According to AI at Meta, Meta announced Canopy Height Maps v2 (CHMv2), an open source model for high‑resolution global forest canopy mapping built with the World Resources Institute, leveraging the DINOv3 Sat-L vision model optimized for satellite imagery to improve canopy height estimation accuracy and coverage. As reported by AI at Meta, CHMv2 enables near-global inference from multispectral satellite data, offering finer spatial resolution for forestry monitoring, biomass estimation, and carbon accounting use cases. According to AI at Meta, the open release lowers costs for governments, NGOs, and climate tech startups to integrate canopy height layers into geospatial AI pipelines for MRV (measurement, reporting, and verification) and nature-based solutions.

According to @ai_darpa, the iconic 2007 Earthrise photo captured by Japan's Kaguya orbiter highlights the potential for AI-powered image analysis in space data. Advanced artificial intelligence techniques are increasingly being used to process, classify, and extract valuable insights from satellite and lunar imagery. These AI applications enable enhanced environmental monitoring, resource management, and early warning systems for businesses and governments. The use of AI in analyzing images like Kaguya's Earthrise creates new market opportunities in space-based services, satellite data analytics, and precision Earth observation, as reported by @ai_darpa.