Zero-shot Microclimate Prediction with Deep Learning
This addresses the challenge of reliable climate prediction in remote areas for applications like agriculture or environmental monitoring, but appears incremental as it builds on zero-shot learning concepts.
The paper tackles the problem of predicting microclimate variables at new, unmonitored locations where sensor data is unavailable, achieving results that surpass conventional weather forecasting techniques.
Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations. To compound the issue, making local predictions often relies on sensor data that may not be accessible for a new, previously unmonitored location. In response to these challenges, we propose a novel zero-shot learning approach designed to forecast various climate measurements at new and unmonitored locations. Our method surpasses conventional weather forecasting techniques in predicting microclimate variables by leveraging knowledge extracted from other geographic locations.