Deep Learning Approach for Data and Computing Efficient Situational Assessment and Awareness in Human Assistance and Disaster Response and Battlefield Damage Assessment Applications

Abstract

The importance of situational assessment and awareness (SAA) becomes increasingly evident for Human Assistance and Disaster Response (HADR) and military operations. During natural disasters in populated regions, proper HADR efforts can only be planned and deployed effectively when the damage levels can be resolved in a timely manner. In today’s warfare, such as battlefield and critical region monitoring and surveillance, prompt and accurate battlefield damage assessments (BDA) are of crucial importance to gain control and ensure robust operating conditions in highly dangerous and contested environments. To design an effective HADR and BDA approach, this paper utilizes the Dynamic Data Driven Applications System (DDDAS) approach within the growing utilization of Deep Learning (DL). DL can leverage DDDAS for near-real-time (NRT) situations in which the original DL-trained model is updated from continuous learning through the effective labeling of SAA updates. To accomplish the NRT DL with DDDAS, an image-based pre- and post-conditional probability learning (IP2CL) is developed for HADR and BDA SAA. Equipped with the IP2CL, the matching pre- and post-disaster/action images are effectively encoded into one image that is then learned using DL approaches to determine the damage levels. Two scenarios of crucial importance for practical uses are examined: pixel-wise semantic segmentation and patch-based global damage classification. Results achieved by our methods in both scenarios demonstrate promising performances, showing that our IP2CL-based methods can effectively achieve data and computational efficiency and NRT updates, which is of utmost importance for HADR and BDA missions.