Climplicit: Climatic Implicit Embeddings for Global Ecological Tasks

2025-04-07

AI Summary

To address the high storage/computation costs and technical barriers hindering deep learning adoption in macroecology, this paper proposes a climate implicit encoding paradigm and introduces a lightweight climate implicit embedding model for global ecological tasks. The model employs an implicit neural representation (INR)-based spatiotemporal geocoder that directly generates climate representations for arbitrary spatiotemporal coordinates—eliminating the need to download raw raster data or train dedicated feature extractors. It follows a pretraining + linear probing framework, ensuring compatibility with heterogeneous climate variables and diverse downstream ecological tasks. Experiments demonstrate that our method matches or exceeds end-to-end trained models in biome classification, species distribution modeling, and plant trait regression, while substantially outperforming conventional geographic encoding approaches. It reduces storage requirements by approximately three orders of magnitude, significantly lowering the entry barrier for ecologists without deep learning expertise.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Representation Learning for Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Deep learning on climatic data holds potential for macroecological applications. However, its adoption remains limited among scientists outside the deep learning community due to storage, compute, and technical expertise barriers. To address this, we introduce Climplicit, a spatio-temporal geolocation encoder pretrained to generate implicit climatic representations anywhere on Earth. By bypassing the need to download raw climatic rasters and train feature extractors, our model uses x1000 fewer disk space and significantly reduces computational needs for downstream tasks. We evaluate our Climplicit embeddings on biomes classification, species distribution modeling, and plant trait regression. We find that linear probing our Climplicit embeddings consistently performs better or on par with training a model from scratch on downstream tasks and overall better than alternative geolocation encoding models.

Problem

• Reduces barriers to using climatic data in ecology
• Generates implicit climatic representations globally
• Improves performance in ecological classification tasks

Innovation

• Pretrained spatio-temporal geolocation encoder
• Generates implicit climatic representations globally
• Reduces disk space and computational needs significantly