Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning

arXiv:2402.18064v1 Announce Type: cross
Abstract: The efficient collection of samples is an important factor in outdoor information gathering applications on account of high sampling costs such as time, energy, and potential destruction to the environment. Utilization of available a-priori data can be a powerful tool for increasing efficiency. However, the relationships of this data with the quantity of interest are often not known ahead of time, limiting the ability to leverage this knowledge for improved planning efficiency. To this end, …

abstract applications arxiv automated automated testing collection costs cs.lg cs.ro data destruction efficiency energy environment environmental information samples sampling testing the environment through tool transfer transfer learning type