Wildlife monitoring at scale is one of ecology’s most data-intensive challenges. Camera traps deployed across remote landscapes can accumulate thousands of images in a single survey season — far more than any team can manually review in a reasonable time. Automatically identifying the species in each photograph would free researchers to focus on analysis rather than image sorting, but doing this accurately requires a model trained on a large and diverse set of wildlife images. This notebook shows how to combine two open tools to tackle this problem: the Atlas of Living Australia (ALA) — Australia’s national biodiversity data platform — and SpeciesNet, a deep learning model developed by Google specifically for wildlife image classification. We query ALA for camera-trap images of target species, download a sample, run SpeciesNet to automatically identify animals in each image, and then visualise the results.
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".