Rapid situational awareness during disasters such as floods, earthquakes, and wildfires demands 3D scene representations that can update seamlessly as new aerial data arrive. Neural Radiance Fields (NeRFs) are a technique that constructs such 3D scene representations. Conventional NeRF-based pipelines remain static and require full retraining when additional observations become available, leading to delays that hinder real-time decision-making. In this paper, we introduce a city-scale NeRF framework for high-speed adaptation and real-time rendering under rapidly evolving data conditions. Our approach combines three key components: meta-continual learning (MCL) for fast adaptation without catastrophic forgetting, modularized neural architectures to support large-scale spatial decomposition, and optimization based on Instant Neural Graphics Primitives (Instant-NGP) for efficient training and compact representation. Together, these components enable real-time refinement of city-scale NeRFs as new observations become available.