WildfireNet: Predicting Wildfire Profiles



Original Source Here

This excerpt is based on the paper we turned in at the 35th AAAI Student Abstract and Poster Program [7].

In recent years, wildfire has become an unavoidable natural disaster that continues to threaten fireprone communities. Due to the ongoing climate change, global warming, and fuel drying, the frequency of devastating wildfires increases every year [1]. The consequences of massive wildfires are brutal. For instance, in 2003, wildfires that occurred in San Diego County burned over 376,000 acres and 3,241 households. This sums up to approximately $2.45 billion in terms of total economic costs [2]. Traditional, physics and empirically-based wildfire spread models have been continuously studied to mitigate losses resulting from wildfire. However, these models often require extensive inputs. Thus, we present a deep learning method to determine dynamic wildfire profiles with basic input data: historical wildfire profiles, weather, and elevation data.

Figure 1. A positive trend of areas burned due to wildfire.

Model and Implementation

U-Net was first introduced solely for the purpose of image segmentation on biomedical images. The model has two major paths. It first begins with contraction, which consists of convolutions and maxpool to extract features of the image. Next, the model undergoes expansions where the size of the image resizes back to the original input to enable precise localization.

We decided to utilize the architecture of U-Net because 1) the model allows us to input an image and output a precisely segmented image, 2) it works well with a small dataset, 3) and it is capable of predicting a wildfire profile within a second.

WildfireNet Architecture

The U-Net model is adjusted so that it becomes more applicable to our study. Similar to the U-Net, WildfireNet is composed of two major paths: contraction and expansion. A sigmoid activation function is used at the last layer to output a probabilistic distribution of fires in the image. Binary classification is performed on each pixel of an image to determine whether there is a fire or not. Thus, binary cross-entropy is used as a loss function to train the model. To create a predicted binary map, an optimal threshold is set to label pixels with fire or non-fire.

In contrast to the U-Net, WildfireNet consists of fully connected layers at the bottom of the architecture. After the last downsampling, the 3D image is flattened into a 1D array, and weather data is added. The model is further trained with dense layers to learn the effect of weather variables in its prediction. Furthermore, past wildfire profiles can play a dominant role in the future shape of the wildfire. Therefore, 3D CNN was used instead of 2D. In 3D CNN, the model further extracts features from both the temporal and spatial dimensions, whereas, in 2D CNN, the model only focuses on spatial features [6]. In this study, 3 previous days of wildfire profiles are combined to convert input images from 2D to 3D. This allows the model to have a better sense on how historical fires are correlated to the fire on the next day.

Figure 2. Architecture of WildfireNet

Dataset and Preprocessing

Dynamic Wildfire Perimeters

A total of 302 daily fire perimeters were retrieved. The size of the data is limited compared to other deep learning studies. However, WildfireNet, derived from U-Net, is proven to perform well with small datasets [5]. Wildfire perimeters were obtained from NIFC FTP Server1. Perimeters are in the .kmz file, which contains an array of coordinates of boundaries. In this paper, only the fires that occurred in California from 2013 to 2019 were considered.

For each wildfire perimeter, as shown in Figure 3, an array of coordinates was used to fill inside the perimeter to create a binary map to reflect the overall shape of the wildfire. In other words, if a given pixel is within the perimeter, the pixel is labeled as 1 to indicate a fire. However, if the pixel is outside the boundary line, the pixel is labeled as 0 to indicate no fire. An Important assumption was made when creating a binary map. In some cases, there were spots of the region within the boundary that was not on fire. These spots were considered as a risk zone and were filled in as well.

Overall, a preprocessed binary map is used as an input to represent the wildfire profile. The binary map has a resolution of 0.5 degrees in both latitude and longitude. Maintaining the same resolution for every fire is important to distinguish fires in respect to their sizes.

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