Many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing. In this study, we propose a three-layer architecture of emergency caching networks focusing on data collection and reliable transmission, by leveraging efficient perception and edge caching technologies. Based on this architecture, we propose a disaster map collection framework that integrates coded caching technologies. Our framework strategically caches coded fragments of maps across unmanned aerial vehicles (UAVs), fostering collaborative uploading for augmented transmission reliability. Additionally, we establish a comprehensive probability model to assess the effective recovery area of disaster maps. Towards the goal of utility maximization, we propose a deep reinforcement learning (DRL) based algorithm that jointly makes decisions about cooperative UAVs selection, bandwidth allocation and coded caching parameter adjustment, accommodating the real-time map updates in a dynamic disaster situation. Our proposed scheme is more effective than the non-coding caching scheme, as validated by simulation.

When a flood hits a town or a village, satellites can capture images of the disaster, but when it comes to a quick assessment of the damage, there would be no better tool than artificial intelligence. It would help the relief workers to save more people in much less time. For the past 20 years, billions of people have been affected by disasters like floods. It is about time the governments stepped up their game in disaster management. On this ground, United Nations Institute for Training and Research (UNITAR) and the Operational Satellite Applications Programme (UNOSAT) have joined hands with UN Global Pulse to apply artificial intelligence for analyzing satellite imagery.