LD-SDM: Language-Driven Hierarchical Species Distribution Modeling
- URL: http://arxiv.org/abs/2312.08334v1
- Date: Wed, 13 Dec 2023 18:11:37 GMT
- Title: LD-SDM: Language-Driven Hierarchical Species Distribution Modeling
- Authors: Srikumar Sastry, Xin Xing, Aayush Dhakal, Subash Khanal, Adeel Ahmad,
Nathan Jacobs
- Abstract summary: We focus on the problem of species distribution modeling using global-scale presence-only data.
To capture a stronger implicit relationship between species, we encode the taxonomic hierarchy of species using a large language model.
We propose a novel proximity-aware evaluation metric that enables evaluating species distribution models.
- Score: 9.620416509546471
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We focus on the problem of species distribution modeling using global-scale
presence-only data. Most previous studies have mapped the range of a given
species using geographical and environmental features alone. To capture a
stronger implicit relationship between species, we encode the taxonomic
hierarchy of species using a large language model. This enables range mapping
for any taxonomic rank and unseen species without additional supervision.
Further, we propose a novel proximity-aware evaluation metric that enables
evaluating species distribution models using any pixel-level representation of
ground-truth species range map. The proposed metric penalizes the predictions
of a model based on its proximity to the ground truth. We describe the
effectiveness of our model by systematically evaluating on the task of species
range prediction, zero-shot prediction and geo-feature regression against the
state-of-the-art. Results show our model outperforms the strong baselines when
trained with a variety of multi-label learning losses.
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