Predicting Geographic Information with Neural Cellular Automata

TL;DR

This paper introduces a neural cellular automata framework for predicting geographic information, demonstrating its versatility and potential in applications like traffic condition forecasting.

Contribution

The study extends neural cellular automata to geographic data prediction, showcasing a novel application and verifying biological analogy to enhance model capabilities.

Findings

Model effectively predicts traffic conditions from geographic data

NCA demonstrates high versatility across different geographic applications

Experimental results confirm the model's usability and potential

Abstract

This paper presents a novel framework using neural cellular automata (NCA) to regenerate and predict geographic information. The model extends the idea of using NCA to generate/regenerate a specific image by training the model with various geographic data, and thus, taking the traffic condition map as an example, the model is able to predict traffic conditions by giving certain induction information. Our research verified the analogy between NCA and gene in biology, while the innovation of the model significantly widens the boundary of possible applications based on NCAs. From our experimental results, the model shows great potentials in its usability and versatility which are not available in previous studies. The code for model implementation is available at https://redacted.