Department of Computer Science and EngineeringA large-scale disaster such as earthquakes and tsunami can cause billion-dollar destruction to a city and kill many people. To mitigate the dead troll, fast disaster response to rescue survivors in a disaster zone is of paramount importance. However, it is difficult to find the location of the injured people in a disaster zone due to the debris and smoke in collapsed buildings as well as the disruption of communication networks. This can cause poor decisions of the disaster response team about where to deploy the rescue personnel and allocate the resource. Therefore, we propose to develop an AI system to predict the location of injured people in a disaster area.
In this research, our system has three major parts: (1) the prediction of the density of injured people in a gridand (2) the strategy of the rescue team to search for injured peopleand (3) the deployment the rescue team to search the location of the most density injured people area according to the first and second part. In the first part, we developed a deep learning software package that consists of state-of-the-art deep learning techniques such as attention module and data annotation to predict the density of injured civilians. Our work uses a disaster simulator called RoboCup Rescue Simulation (RCRS). To predict the density of injured people in RCRS, we train the machine learning model using the two cases of the image data: (1) single image frame such as a satellite imageand (2) multiple image sequence frame such as disaster video clip. Furthermore, we evaluate our ML model in the other two domains: (1) the prediction of the location of crime in Chicagoand (2) the prediction of the location of RSNA Pneumonia.
In the second part, we propose the Treasure Hunt Problem. In RCRS, the rescue team has to search more than one injured people and it is a complicated multi-agent problem. Therefore, study a simpler problem called the Treasure Hunt Problem, in which there is only one rescue crew search the only one injured civilian. In this problem, we assume that the location of the treasure is determined based on the probability distribution, and the ML model predicts the distribution of probability that treasure exists for each coordinate within the map. To solve this problem, we propose two search strategies that makes use of the ML model to improve the effectiveness of a search mission: (1) the probabilistic greedy search that the hunter searches preferentially for the cell with the highest probability of existing treasure given by ML modeland (2) the probabilistically admissible heuristic A* search that the hunter searches the cell determined by heuristic A* search with the probability of existing treasure given by ML model.
In the last part, we merge the first and second parts to search for the location of the most density injured people area. To predict the location, we predict the number of injured people with several ML models used in the first part and we convert the injured people density predicted to the probability distribution. And the rescue team search the most density injured people area according to the search strategy of the second part based on this probability distributionclos
