Due to the lack of empirical inundation data, a novel method was explored for extracting flood inundation information from social media uploads using social media and deep learning technologies, addressing the need for urban area inundation depth measurement data. Firstly, the image segmentation model based on the YOLOv5 and Mask-RCNN was constructed and the identification dataset of various key parts of vehicles was constructed. A novel method for extracting the inundation height during urban flooding events was proposed according to the model training results. By inputting inundation images, the prediction of submerged locations and depths in urban inundation was conducted. These predictions were then compared with the data obtained from the inundation recurrence model. A validation dataset was formed by conducting the simulated inundation experiments, to verify the feasibility of the proposed model. Results showed that the Nash efficiency coefficient of the proposed model was 0.98. Moreover, the social media images from the actual urban flooding during the "7·20" event in Zhengzhou City were used to verify the reliability of the proposed model. Results showed that the proposed method can provide an effective data source for the process of urban inundation.