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.
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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.
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.
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.
SAM 3.1: Meta Releases Faster Real-Time Video Segmentation Model with Object Multiplexing
Meta has released SAM 3.1, an incremental update to Segment Anything Model 3, introducing object multiplexing that allows tracking up to 16 objects in a single forward pass. This doubles video processing throughput from 16 to 32 FPS on a single H100 GPU, reducing GPU resource requirements and enabling real-time tracking on smaller hardware. SAM 3.1 is a drop-in replacement for SAM 3 and is available via updated model checkpoints and codebase. The broader SAM 3 release also includes text and exemplar prompting, a new Segment Anything Playground, the SA-Co evaluation dataset, and SAM 3D for 3D reconstruction.
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.
Meta SAM 3 (Segment Anything Model 3) Released on GitHub
Meta / Facebook Research has released SAM 3, the third generation of their Segment Anything Model, with code for inference and finetuning, pretrained model checkpoints, and example notebooks. The repository has accumulated over 10,000 stars with strong daily momentum (+93). SAM 3 continues Meta's open-weights tradition in computer vision foundation models. No accompanying paper or technical blog post is referenced in this item.
Anthropic awarded $200M DOD agreement to prototype frontier AI for national security
The U.S. Department of Defense's Chief Digital and Artificial Intelligence Office (CDAO) has awarded Anthropic a two-year, $200M ceiling prototype other transaction agreement to develop frontier AI capabilities for national security applications. Work will include fine-tuning models on DOD data, adversarial AI risk mitigation, and responsible AI adoption across the defense enterprise. Anthropic will leverage its Claude Gov models and existing partnerships with Palantir and AWS-hosted infrastructure. This is a significant expansion of Anthropic's federal footprint, building on prior deployments with defense and intelligence agencies.
D4RT: DeepMind's Unified 4D Reconstruction and Tracking System, Up to 300x Faster
DeepMind has announced D4RT, a system for unified four-dimensional (spatial + temporal) scene reconstruction and tracking. The method claims up to 300x speed improvements over prior approaches. The announcement positions D4RT as a significant efficiency advance in dynamic 3D scene understanding, with potential applications in robotics, video understanding, and embodied AI.