*2.1. Dataset of Burnt EVs*

Original images of burnt EVs were collected from various accident cases of EV combustion in China and burning tests conducted by Tianjin Fire Research Institute of M.E.M. The dataset contains 314 raw images with pixel-level annotations of burnt EVs. Vehicle bodies of the dataset are labeled into 3 different levels of severity and background into pixel-level according to their visual appearance after combustion. Blue stands for intact (IN), brown stands for mild and moderate burnt (MB) regions, red stands for severely burnt (SB) regions, and black stands for background (BG). The proportion of the numbers of pixels in different classes is shown in Table 1. Detailed regions of different labels are shown in Figure 1. The distinction between MB and SB is mainly based on the visual appearance of the painting. In short, regions with painting burnt into yellow or black were labeled as MB, and regions with painting entirely burnt out and bottom metal exposed were labeled as SB. As for tires and glasses, MB and SB were labeled according to whether their basic structure were kept after burning. All images with labeled masks were resized to 560 × 420 to fit the input of the proposed network. Moreover, the whole dataset was divided into five folds uniformly for five-fold cross validation. While training the foreground extraction branch, the labeled images were transferred into foreground masks. More images with corresponding labeled masks for different tasks are shown in Figure 2.

**Table 1.** Proportion of numbers of pixels in different classes (%).


**Figure 1.** Original image, labeled image and details. (**a**) Original image of burnt EV. (**b**) Labeled image of different severity. (**c**) Detail of region labeled as SB. (**d**) Detail of region labeled as MB. (**e**) Detail of region labeled as IN.

**Figure 2.** Images from the dataset with corresponding labels. First row: original images, second row: labeled masks for fire trace segmentation, third row: labeled masks for foreground extraction.
