LD-SDM: Language-Driven Hierarchical Species Distribution Modeling

Autor: Sastry, Srikumar, Xing, Xin, Dhakal, Aayush, Khanal, Subash, Ahmad, Adeel, Jacobs, Nathan
Rok vydání: 2023
Předmět:
Druh dokumentu: Working Paper
Popis: 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.
Comment: 17 pages, 9 figures
Databáze: arXiv