Description
We address the problem of monitoring forest disturbance using ML-heavy DAWs on earth observation data. Such DAWs analyze a steady stream of high-volume data under data and concept drifts, demanding frequent model updates. We aim to save computing and energy resources associated with the updates by decreasing the frequency (update only on significant data drifts) and by re-training solely DAWs components affected by new data. Moreover, we improve the detection accuracy and response time of the forest disturbance indicators.
Scientists
- Diellza Sherifi