Deep Learning Model for Wildfire Prediction: Spatiotemporal Modeling Practice with ConvLSTM U-Net

This article introduces the open-source project fire_forecasting (author: mobiiin, GitHub link: https://github.com/mobiiin/fire_forecasting, release date: May 25, 2026). The project adopts the ConvLSTM U-Net architecture to predict wildfire intensity or boundaries from multi-modal grid data (meteorology, terrain, vegetation, historical fire sites, etc.), exploring the application of spatiotemporal deep learning in disaster prediction, aiming to provide decision support for scenarios such as fire emergency response.

Global climate change has intensified the frequency and destructiveness of wildfire disasters. Traditional physical models and empirical formulas struggle to capture the complex nonlinear characteristics of wildfire spread. Deep learning methods (especially spatiotemporal modeling neural networks) bring new possibilities to this field. The project aims to use multi-modal environmental data to train models for predicting future wildfire intensity and boundaries, assisting in fire-fighting resource deployment, evacuation route planning, and reducing casualties and property losses.

The core of the model is the hybrid ConvLSTM U-Net architecture: ConvLSTM (Convolutional Long Short-Term Memory Network) encodes the temporal features of wildfire evolution, capturing the spatiotemporal dependencies of fire spread; U-Net decodes spatiotemporal features into pixel-level prediction maps through an encoder-decoder structure and skip connections. Inputs integrate meteorological (temperature, humidity, wind speed, etc.), terrain (elevation, slope, etc.), vegetation (type, coverage, etc.), and historical fire site data, which are uniformly gridded to the same resolution as input channels.

The task is spatiotemporal sequence prediction (given past T-step observations, predict future N-step wildfire states); weighted loss or focal loss is used to handle class imbalance issues; U-Net skip connections are utilized to optimize spatial accuracy, and Dice coefficient or IoU may be used as evaluation metrics.

Emergency response support: Predict fire spread direction, assess high-risk areas, optimize resource allocation; Risk assessment and insurance: Assist in formulating differentiated premium strategies; Ecological research: Analyze the impact of climate change on wildfire patterns, providing a basis for policy formulation.

Current challenges: Difficulty in acquiring and integrating high-quality multi-modal data, high real-time requirements, difficulty in predicting sudden changes in extreme events; Future directions: Multi-scale modeling (satellite + ground observations), integration of physical constraints (to improve interpretability), uncertainty quantification (to support risk decision-making).

The fire_forecasting project demonstrates the application potential of deep learning in the field of disaster prediction. ConvLSTM U-Net effectively combines temporal modeling and spatial decoding capabilities. With data accumulation and algorithm optimization, such technologies are expected to play a greater role in disaster prevention and control, and are worthy of attention and participation from developers in the fields of meteorology, disaster management, and spatiotemporal deep learning.