  We propose a new active learning approach for efficiently estimating the
geographic range of a species from a limited number of on the ground
observations. We model the range of an unmapped species of interest as the
weighted combination of estimated ranges obtained from a set of different
species. We show that it is possible to generate this candidate set of ranges
by using models that have been trained on large weakly supervised community
collected observation data. From this, we develop a new active querying
approach that sequentially selects geographic locations to visit that best
reduce our uncertainty over an unmapped species' range. We conduct a detailed
evaluation of our approach and compare it to existing active learning methods
using an evaluation dataset containing expert-derived ranges for one thousand
species. Our results demonstrate that our method outperforms alternative active
learning methods and approaches the performance of end-to-end trained models,
even when only using a fraction of the data. This highlights the utility of
active learning via transfer learned spatial representations for species range
estimation. It also emphasizes the value of leveraging emerging large-scale
crowdsourced datasets, not only for modeling a species' range, but also for
actively discovering them.
