mlsanityData to 14 Oct 2026

LD-SDM: Language-Driven Hierarchical Species Distribution Modeling

arXiv 2312.08334 ↗First posted 13 Dec 2023cs.CVcited by 4

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Abstract (arXiv)

We focus on species distribution modeling using global-scale presence-only data, leveraging geographical and environmental features to map species ranges, as in previous studies. However, we innovate by integrating taxonomic classification into our approach. Specifically, we propose using a large language model to extract a latent representation of the taxonomic classification from a textual prompt. This allows us to map the range of any taxonomic rank, including unseen species, without additional supervision. We also present a new proximity-aware evaluation metric, suitable for evaluating species distribution models, which addresses critical shortcomings of traditional metrics. We evaluated our model for species range prediction, zero-shot prediction, and geo-feature regression and found that it outperforms several state-of-the-art models.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 27 of its 55 references that are in the map. First posted on arXiv 13 Dec 2023.

Builds on

27 of 55 references.

References · 27

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
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  7. 2020Geography-Aware Self-Supervised Learningcited by 190
  8. 2021Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completioncited by 112
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  11. 2023Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networkscited by 61
  12. 2023Spatial Implicit Neural Representations for Global-Scale Species Mappingcited by 59
15 more
  1. 2019Geo-Aware Networks for Fine-Grained Recognitioncited by 44
  2. 2021Simple and Robust Loss Design for Multi-Label Learning with Missing Labelscited by 30
  3. 2022Acknowledging the Unknown for Multi-label Learning with Single Positive Labelscited by 24
  4. 2021Species Distribution Modeling for Machine Learning Practitioners: A Reviewcited by 21
  5. 2016Deep Multi-Species Embeddingcited by 20
  6. 2023Active Learning-Based Species Range Estimationcited by 13
  7. 2023BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mappingcited by 12
  8. 2020The GeoLifeCLEF 2020 Datasetcited by 8
  9. 2023Bird Distribution Modelling using Remote Sensing and Citizen Science datacited by 8
  10. 2023Robust Asymmetric Loss for Multi-Label Long-Tailed Learningcited by 8
  11. 2018Bias Reduction via End-to-End Shift Learning: Application to Citizen Sciencecited by 6
  12. 2017Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encodercited by 5
  13. 2023A Near-Linear Time Algorithm for the Chamfer Distancecited by 5
  14. 2023Asymmetric Polynomial Loss For Multi-Label Classificationcited by 4
  15. 2023SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Datacited by 3

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024TaxaBind: A Unified Embedding Space for Ecological Applicationscited by 35
  2. 2024Combining Observational Data and Language for Species Range Estimationcited by 12
  3. 2025Feedforward Few-shot Species Range Estimationcited by 3
  4. 2025CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversitycited by 0