Forest Research Deploys DINOv2 for National-Scale Tree Canopy Monitoring in England
Forest Research, the UK Forestry Commission's research agency, is using Meta's DINOv2 computer vision model—trained on 18 million satellite images in collaboration with the World Resources Institute—to build enhanced tree canopy height maps at 1-meter resolution for England. The approach aims to replace expensive LiDAR and survey data with open-source AI-derived canopy height models applied to national aerial photography, enabling rolling three-year monitoring cycles. The deployment supports the UK government's Environmental Improvement Plan targets and the Natural Capital and Ecosystem Assessment program. Meta also announced DINOv3 as a successor to further improve visual intelligence for such applications.
Related guides (2)
Related events (8)
Meta and World Resources Institute Release Canopy Height Maps v2 Using DINOv3 Self-Supervised Vision Model
Meta AI and the World Resources Institute have released Canopy Height Maps v2 (CHMv2), an open-source global forest mapping system powered by DINOv3, Meta's self-supervised vision model pre-trained on SAT-493M, a large satellite imagery dataset. The new model improves R² accuracy from 0.53 to 0.86 over the previous DINOv2-based version, with better performance on tall trees and greater geographic consistency. CHMv2 is already being adopted by the UK Forestry Commission, the European Commission's Joint Research Centre, and multiple US city planning initiatives. The model, maps, and dataset are publicly available.
Orakl Oncology uses Meta's DINOv2 to accelerate cancer organoid analysis and drug response prediction
Orakl Oncology, a spinoff from the Gustave Roussy Institute, has deployed Meta's open-source DINOv2 vision model to analyze cancer organoid images and predict patient drug responses in clinical trials. In collaboration with CentraleSupelec and the Jaulin Lab under the RHU ORGANOMIC initiative, the team found DINOv2 outperformed prior specialized models by 26.8% accuracy. The model enabled quantitative extraction of imaging data from organoid videos, replacing labor-intensive frame-by-frame analysis and significantly accelerating their biomedical platform development.
DeepMind: Mapping, Modeling, and Understanding Nature with AI
DeepMind published a blog post highlighting AI applications for environmental and ecological research, including species mapping, forest protection, and bioacoustic monitoring of birds. The post describes how AI models are being deployed to address biodiversity and conservation challenges at scale. This represents DeepMind's continued positioning of AI as a tool for scientific and environmental impact beyond core ML research.
UPenn PRONTO Team Uses Meta's SAM 2 and DINO for Autonomous Military Medical Triage in DARPA Challenge
The University of Pennsylvania's PRONTO team is applying Meta's Segment Anything Model 2 (SAM 2) and DINO/Grounding DINO models to autonomous robotic triage in DARPA's three-year mass casualty incident challenge. The multi-robot system uses drones and ground robots to locate victims, then runs parallel injury classification pipelines combining SAM, DINO, and pose estimation to assess heart rate, respiration, wounds, and amputations without requiring labeled training data. Results are surfaced to first responders via a mobile interface for real-time prioritization. Phase 2 concluded in October 2025, with Phase 3 expected to push toward deployment-ready performance.
USRA Applies SAM 2 Fine-Tuning for Real-Time Flood and River Monitoring
The Universities Space Research Association (USRA) and Meta are collaborating with the U.S. Geological Survey (USGS) to apply a fine-tuned version of SAM 2 for automated water segmentation in drone and satellite imagery, targeting real-time flood detection and river extent mapping. The fine-tuned model replaces a labor-intensive manual digitization workflow that was a key bottleneck in rapid-response image analysis. The system integrates with PlanetScope satellite imagery and USGS 3D Hydrography data, with case studies in the Chesapeake Bay area showing promise for nationwide deployment. The collaboration also anticipates leveraging the recently released SAM 3 for unified detection, segmentation, and tracking.
Meta Introduces TRIBE v2: Predictive Foundation Model for Human Brain Activity
Meta AI has released TRIBE v2, a foundation model that predicts high-resolution fMRI brain activity in response to visual, auditory, and language stimuli. Trained on data from over 700 healthy volunteers, it achieves a 70x resolution increase over comparable models and supports zero-shot generalization to new subjects, languages, and tasks. The release includes model weights, codebase, a research paper, and an interactive demo under a CC BY-NC license. Meta positions the work as a bridge between neuroscience and AI development, enabling hypothesis testing without requiring human subjects in every experiment.
FINO: Label-free adaptation of vision foundation models using metadata in scientific domains
Researchers propose FINO, a self-supervised method for adapting vision foundation models to specialized scientific domains without task labels, using metadata as a guidance signal instead. The approach combines a standard self-supervised objective with flexible handling of both discrete and continuous metadata to preserve informative factors while suppressing spurious ones. Evaluated across subcellular fluorescence microscopy, Earth observation, wildlife monitoring, and medical imaging, FINO outperforms both unsupervised domain adaptation and fully supervised fine-tuning, including domain-specific state-of-the-art models.
DeepMind Perch Model Advances Bioacoustics for Endangered Species Conservation
DeepMind has released a new model called Perch designed to help conservationists analyze bioacoustic audio data more efficiently. The model targets wildlife monitoring applications, including tracking endangered species such as Hawaiian honeycreepers and assessing coral reef health. This represents an applied AI deployment in ecological science rather than a frontier capability announcement.