LD-SDM: Language-Driven Hierarchical Species Distribution Modeling
Srikumar Sastry Xin Xing Aayush Dhakal Subash Khanal Adeel Ahmad Nathan Jacobs
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.