Active Learning-Based Species Range Estimation

Christian Lange, Elijah Cole, Grant Van Horn, Oisin Mac Aodha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publication37th Conference on Neural Information Processing Systems (NeurIPS 2023)
PublisherCurran Associates Inc
Pages41892-41913
Number of pages22
Volume36
Publication statusPublished - 15 Dec 2023
EventThirty-seventh Conference on Neural Information Processing Systems - New Orleans, United States
Duration: 10 Dec 202316 Dec 2023
Conference number: 37
https://nips.cc/

Conference

ConferenceThirty-seventh Conference on Neural Information Processing Systems
Abbreviated titleNeurIPS 2023
Country/TerritoryUnited States
CityNew Orleans
Period10/12/2316/12/23
Internet address

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