DigiForest integrates heterogeneous autonomous robots for data collection, automated tree trait extraction, a decision support system for growth forecasting, and autonomous harvesters for selective logging, with real-world tests in European forests.
Building Forest Inventories With Autonomous Legged Robots—System, Lessons, and Challenges Ahead
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This survey defines the Federated Continual Learning problem, proposes a taxonomy for approaches, reviews applications and metrics, and identifies open challenges in lifelong privacy-preserving learning on non-stationary distributed data.
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DigiForest: Digital Analytics and Robotics for Sustainable Forestry
DigiForest integrates heterogeneous autonomous robots for data collection, automated tree trait extraction, a decision support system for growth forecasting, and autonomous harvesters for selective logging, with real-world tests in European forests.