# Active Learning-Based Species Range Estimation

arXiv: https://arxiv.org/abs/2311.02061 · HTML: https://mlsanity.com/paper/2311.02061/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 3 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Presence-Only Geographical Priors for Fine-Grained Image Classification](https://mlsanity.com/paper/1906.05272/) (2019)
- [Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks](https://mlsanity.com/paper/2310.06743/) (2023)
- [Spatial Implicit Neural Representations for Global-Scale Species Mapping](https://mlsanity.com/paper/2306.02564/) (2023)
- [Species Distribution Modeling for Machine Learning Practitioners: A Review](https://mlsanity.com/paper/2107.10400/) (2021)
- [Deep Multi-Species Embedding](https://mlsanity.com/paper/1609.09353/) (2016)
- [Mapping oil palm density at country scale: An active learning approach](https://mlsanity.com/paper/2105.11207/) (2021)
- [Bird Distribution Modelling using Remote Sensing and Citizen Science data](https://mlsanity.com/paper/2305.01079/) (2023)
- [Bias Reduction via End-to-End Shift Learning: Application to Citizen Science](https://mlsanity.com/paper/1811.00458/) (2018)
- [Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs](https://mlsanity.com/paper/2106.03774/) (2021)

## Built on by (most cited first, 13 of 13)

- [The iNaturalist Sounds Dataset](https://mlsanity.com/paper/2506.00343/) (2025)
- [Combining Observational Data and Language for Species Range Estimation](https://mlsanity.com/paper/2410.10931/) (2024)
- [GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations](https://mlsanity.com/paper/2503.16683/) (2025)
- [LD-SDM: Language-Driven Hierarchical Species Distribution Modeling](https://mlsanity.com/paper/2312.08334/) (2023)
- [Generating Binary Species Range Maps](https://mlsanity.com/paper/2408.15956/) (2024)
- [Feedforward Few-shot Species Range Estimation](https://mlsanity.com/paper/2502.14977/) (2025)
- [MaskSDM with Shapley values to improve flexibility, robustness, and explainability in species distribution modeling](https://mlsanity.com/paper/2503.13057/) (2025)
- [MiTREE: Multi-input Transformer Ecoregion Encoder for Species Distribution Modelling](https://mlsanity.com/paper/2412.18995/) (2024)
- [Hybrid Spatial Representations for Species Distribution Modeling](https://mlsanity.com/paper/2410.10937/) (2024)
- [Latent Domain Modeling Improves Robustness to Geographic Shifts](https://mlsanity.com/paper/2503.02036/) (2025)
- [CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations](https://mlsanity.com/paper/2508.06704/) (2025)
- [BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models](https://mlsanity.com/paper/2510.19749/) (2025)
- [Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference](https://mlsanity.com/paper/2609.27409/) (2026)

## Abstract (arXiv)

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.