🤖 AI Summary
Traditional grid-based methods struggle to effectively reconstruct continuous environmental fields from sparse and irregular ecological observation data. This work proposes implicit neural representations (INRs) as a coordinate-driven modeling framework, leveraging coordinate-based neural networks to directly learn spatial or spatiotemporal continuous fields. This approach inherently supports resolution-agnostic querying, preserves spatial coherence, and offers controllable computational costs. The method integrates seamlessly into existing ecological analysis pipelines and achieves stable, high-fidelity reconstructions in tasks such as species distribution modeling, phenological dynamics, and morphological segmentation. Empirical results demonstrate that INRs match or exceed the performance of classical smoothing techniques and tree-based models, highlighting their strong scalability and practical utility for ecological applications.
📝 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.