Predicting Geographic Information with Neural Cellular Automata
This work addresses geographic prediction tasks, like traffic condition mapping, for applications in urban planning or navigation, but it appears incremental as it extends existing NCA methods to new data.
This paper tackles the problem of predicting geographic information, such as traffic conditions, by using neural cellular automata (NCA) trained on various geographic data, achieving results that show great potential in usability and versatility compared to previous studies.
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.