Improving Wildfire Simulations via Geometric Primitive Analysis in Noisy Crowdsourced Data
Ioannis Karakonstantis, George Xylomenos · Applied Sciences · 2025
A key challenge in real-time wildfire simulation is data acquisition from dynamic sources, such as user-submitted data collected via mobile phones. Information obtained from firefighting personnel in the field, or even bystanders, typically outperforms pre-existing information in terms of its spatial and time resolution and can be used to execute more accurate fire simulations; these can be continuously updated as new data are added. However, combining data from users with heterogeneous knowledge backgrounds and biased conceptual barriers introduces additional distortion to what we know about an evolving wildfire. We examine the problem of resolving geometric ambiguity, where users submit duplicate or distorted spatial entries about a modeled wildfire, under real-time constraints. We argue that an optimization algorithm from the Ant Colony Optimization family is a strong candidate to tackle this problem, taking into account the nature of the submitted data and the limitations introduced by mobile phones.