Spatial Implicit Neural Representations for Global-Scale Species Mapping (2306.02564v1)

Abstract: Estimating the geographical range of a species from sparse observations is a challenging and important geospatial prediction problem. Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. This problem has a long history in ecology, but traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets which can include tens of millions of records for hundreds of thousands of species. In this work, we use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously. We find that our approach scales gracefully, making increasingly better predictions as we increase the number of species and the amount of data per species when training. To make this problem accessible to machine learning researchers, we provide four new benchmarks that measure different aspects of species range estimation and spatial representation learning. Using these benchmarks, we demonstrate that noisy and biased crowdsourced data can be combined with implicit neural representations to approximate expert-developed range maps for many species.

• The paper introduces SINRs to model 47,000 species ranges accurately from sparse presence-only data.
• The paper analyzes loss functions, showing that the full assume-negative loss improves model generalization with noisy, crowdsourced data.
• The paper presents four geospatial benchmarks that validate SINRs and open new avenues for ecological and conservation applications.

Spatial Implicit Neural Representations for Global-Scale Species Mapping

The paper presents an exploration of Spatial Implicit Neural Representations (SINRs) for species distribution modeling (SDM), addressing the challenge of estimating geographical species ranges from sparse presence-only data. Employing SINRs, the paper simultaneously models the distribution of 47,000 species, a task that leverages the SDM framework's capacity to operate at scale, capitalizing on large, noisy crowdsourced datasets. The key findings affirm that as more data and species are integrated, the predictive accuracy of such models notably enhances. The authors supply four benchmark tasks to advance research in this domain, illustrating the model's ability to approximate expert-developed range maps.

Key Contributions

1. Spatial Implicit Neural Representations (SINRs): The research leverages SINRs to estimate dense species ranges from sparse presence-only data. This innovative approach mitigates the limitations of traditional species distribution models that require presence-absence data.
2. Loss Function Analysis: A detailed empirical investigation of loss functions specific to learning from presence-only data is presented. The paper evaluates these loss functions across several criteria such as coping with data noise and scalability.
3. Geospatial Benchmark Tasks: The paper introduces four geospatial benchmarks, focusing on species mapping and fine-grained image classification. These benchmarks are crucial for evaluating the SINRs' learning ability from spatially sparse datasets and assessing the SDM.

Results and Implications

• Scaling of SINRs: A salient result is that SINRs' performance improves with an increased data size for more species, highlighting the scalability of deep learning in addressing ecological modeling challenges. The predictive models become more refined and accurate as the volume of data expands.
• Loss Function Efficiency: Among the tested loss functions, the full assume-negative loss exhibited superior performance in most scenarios. This loss function leverages pseudo-negatives to enhance the model's ability to generalize predictions.
• Environmental vs. Coordinate Features: SINRs trained using coordinates alone are nearly as effective as those using environmental features, indicating that SINRs can infer ecological habitats' spatial patterns from presence-only data without additional covariates.

Future Directions

• Temporal Dynamics and Bias Mitigation: Future work could integrate temporal data into SINRs to facilitate dynamic species range predictions over time. Mitigating spatial bias inherent in crowdsourced data remains a critical challenge and area for further exploration.
• Cross-disciplinary Applications: These findings hold potential for applications beyond ecology, such as epidemiology and urban planning, where spatial predictions from limited data are vital. The SINR methodology could serve as a tool across several fields requiring nuanced spatial modeling.
• AI and Conservation Efforts: As artificial intelligence begins to tackle environmental and conservation challenges, frameworks such as SINRs offer potent methods for developing comprehensive species distribution models. This can lead to more informed conservation strategies, vital for biodiversity preservation amid climate change and habitat loss.

In conclusion, the introduction of SINRs for global-scale species mapping represents a noteworthy advancement in species distribution modeling. This approach's ability to learn effectively from vast yet noisy datasets positions it as a valuable tool for ecological research and conservation efforts, offering promising avenues for future development and application in related fields.