Implicit neural representations as a coordinate-based framework for continuous environmental field reconstruction from sparse ecological observations

TL;DR

This paper explores the use of implicit neural representations as a flexible, scalable method for reconstructing continuous environmental fields from sparse, irregular ecological data, demonstrating their advantages over traditional approaches.

Contribution

It evaluates implicit neural representations for environmental modeling, highlighting their stability, scalability, and suitability for integration into ecological data analysis workflows.

Findings

Neural fields provide stable, continuous environmental representations.

They outperform classical smoothers and tree-based methods in certain tasks.

Neural representations are scalable and resolution-independent.

Abstract

Reconstructing continuous environmental fields from sparse and irregular observations remains a central challenge in environmental modelling and biodiversity informatics. Many ecological datasets are heterogeneous in space and time, making grid-based approaches difficult to scale or generalise across domains. Here, we evaluate implicit neural representations (INRs) as a coordinate-based modelling framework for learning continuous spatial and spatio-temporal fields directly from coordinate inputs. We analyse their behaviour across three representative modelling scenarios: species distribution reconstruction, phenological dynamics, and morphological segmentation derived from open biodiversity data. Beyond predictive performance, we examine interpolation behaviour, spatial coherence, and computational characteristics relevant for environmental modelling workflows, including scalability, resolution-independent querying, and architectural inductive bias. Results show that neural fields provide stable continuous representations with predictable computational cost, complementing classical smoothers and tree-based approaches. These findings position coordinate-based neural fields as a flexible representation layer that can be integrated into environmental modelling pipelines and exploratory analysis frameworks for large, irregularly sampled datasets.