Auto Neural Architecture Generator for UAV-Based Geospatial Surveillance for Aerial Crisis Mapping in Dark

Abstract

Natural disasters such as flood, tsunami, earthquake and cyclones usually influence the microspaces and in urban areas, thereby causing the problem to rescuers to make way to the victims. Rescue operations in these situations are also hampered due to darkness caused by power cut and unavailability of other light sources to rescue people in peril or to even carry out evacuation operations. Therefore, we need solution to map all the essential large-scale feature spaces in dark to avail safety and saving numerous lives in disaster environments. This study presents a soft framework for crisis mapping in dark to map aerial view of geo-specific terrain in disaster struck areas so that effective map of debris and localization of victims can be achieved to enable strategic planning of rescue operations.