## 2 Methodology
### Extract Multimodal Social Sensing Disaster Information  
#### 2.3.1 Disaster Image Segmentation Using Deeplabv3+  
This study utilizes social media images for disaster damage analysis, enhancing situational awareness through image segmentation techniques. The core method employed is semantic segmentation, which classifies disaster-affected areas in images on a per-pixel basis. The sample dataset for the disaster damage image segmentation model was primarily derived from the \"Deep Learning Benchmarks and Datasets for Social Media Image Classification in Disaster Response\" [39], developed by [PERSON] et al. in 2020, supplemented with images from Typhoon Chaba in 2022. Since the original dataset was not suitable for image segmentation tasks, we manually annotated flood images using the EISeg tool and collected additional disaster damage images via Weibo. After filtering, a total of 2226 images were used. These images were annotated into five categories: urban flooding, submerged vehicles, fallen trees, damaged buildings, and landslides. The segmentation model is based on the pre-trained DeepLabv3+ [44] architecture, with MobileNetV2 as the backbone network, incorporating Inverted Residual Blocks and depthwise separable convolutions to extract both shallow and deep features (Figure 7). The dataset was split  
Figure 6: Network structure of the MobileNetV3 model. into training and validation sets in a 9:1 ratio. During training, the backbone network was frozen for the first 100 epochs, after which it was unfrozen for 300 additional epochs. The batch size was set to 8 during the frozen phase and 16 during the unfrozen phase. The optimizer used was Stochastic Gradient Descent (SGD), with an initial learning rate of 0.01, a momentum of 0.9, and an L2 regularization value of 0.0001. The loss functions included cross-entropy, focal loss, and Dice loss, with a learning rate decay strategy applied during training. To evaluate the performance of pixel classification, we employed four metrics: Overall Accuracy (OA), Mean Intersection Over Union (MIoU), Mean Pixel Accuracy (MPA), and Recall (RC). These metrics were calculated using a confusion matrix based on four cases: True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN). TP represents the number of image pixels correctly classified as belonging to the flood category, while TN refers to the number of pixels correctly classified as part of the non-flood (background) category. FP denotes the number of pixels incorrectly classified as flood-related, even though they do not belong to the flood category, and FN represents the number of pixels that should have been classified as flood-related but were mistakenly associated with the background category. The formulas for calculating these evaluation metrics are as follows:  
Overall Accuracy (OA) refers to the ratio between the number of correctly classified pixels and the total number of pixels. \[\text{OA}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}\]  
Mean Intersection Over Union (MIoU) is the average ratio of the intersection to the union between the segmentation result and the ground truth. A higher MIoU indicates that the segmentation result is closer to the actual ground truth, and the segmentation algorithm performs better across all categories. \[\text{MIou}=\frac{1}{k+1}\sum_{i=0}^{k}\frac{TP}{TP+FP+FN}\]  
We evaluated the performance of the Deeplabv3+-based disaster monitoring method through experiments on the test set. Although the overall accuracy of the dataset was relatively high, the MIoU for the category-specific metric was 60.78, indicating that it  
Figure 7: Network structure of Deeplabv3+ model. is challenging to accurately align the boundaries of floodwater with the ground truth. This may be due to the issue of sample imbalance, where the number of pixels in the floodwater category is much smaller than that of the background category, causing the model to favor background predictions and resulting in a lower MIoU.