Wildfire Spread Scenarios: Increasing Sample Diversity of Segmentation Diffusion Models with Training-Free Methods

Abstract

Predicting future states in uncertain environments, such as wildfire spread, medical diagnosis, or autonomous driving, requires models that can consider multiple plausible outcomes. While diffusion models can effectively learn such multi-modal distributions, naively sampling from these models is computationally inefficient, potentially requiring hundreds of samples to find low-probability modes that may still be operationally relevant. In this work, we address the challenge of sample-efficient ambiguous segmentation by evaluating several training-free sampling methods that encourage diverse predictions. We adapt two techniques, particle guidance and SPELL, originally designed for the generation of diverse natural images, to discrete segmentation tasks, and additionally propose a simple clustering-based technique. We validate these approaches on the LIDC medical dataset, a modified version of the Cityscapes dataset, and MMFire, a new simulation-based wildfire spread dataset introduced in this paper. Compared to naive sampling, these approaches increase the HM IoU* metric by up to 7.5% on MMFire and 16.4% on Cityscapes, demonstrating that training-free methods can be used to efficiently increase the sample diversity of segmentation diffusion models with little cost to image quality and runtime. Code and dataset: https://github.com/SebastianGer/wildfire-spread-scenarios

Figures (6)

• Figure 1: Diversity-biased sampling: We train a conditional diffusion model to generate different outputs for the same input data. If the goal is to find most, or all, different outputs for the current input, naive sampling can require a large number of samples, due to the redundancy in samples. To reduce this redundancy, we employ methods that bias the sampling towards higher diversity for the same number of samples.
• Figure 2: MMFire: We use a wildfire spread simulator to generate multiple plausible outcomes based on the current state of the fire. This is done by setting the wind direction to one of eight values across the whole $64\times64$ image. We impose a highly skewed probability distribution on the eight outcomes during training (see the probabilities above). This represents a difficult situation where naively sampling from the diffusion model is a very slow strategy for finding all modes.
• Figure 3: Multi-modal binary Cityscapes: The classes road, sidewalk, vegetation, and car are randomly flipped to the positive or negative class, with fixed probabilities, resulting in $2^4 = 16$ separate modes per image.
• Figure 4: Applying clustering-based sample-pruning at different sampling steps: Varying after which sampling step the clustering and pruning is performed influences the final performance. For all datasets, there is large gap between evaluating only the cluster centers and evaluating the full set of generated samples. The x-axis represents the index of the sampling step. However, the noise levels at each step do not decrease linearly. See appendix for details on the noise schedule.
• Figure D.1: Method comparison - MMFire. We compare the different diversity-encouraging methods on the MMFire dataset, sampled from the same starting noise and conditioning. The order of generated samples is determined via Hungarian matching, such that the samples are positioned below the closest ground truth. This example is cherry-picked for visualization. Non-cherry-picked examples often show the same number of correct samples across most methods, or can have duplicate ground truths. The low difference in visual appearance makes sense when we consider that the best method, SPELL, only performs 7.5% better than naive sampling, and that all methods use the same underlying diffusion model.