From 3D Scene Understanding to Hazard Forecasting: A Critical Review of Remote Sensing–Driven Emergency Digital Twins

Emergency forecasts benefit from scene detail when it constrains the relevant physical process and reaches decision-makers in time. This critical review examines flood inundation, wildfire spread and marine drift through 148 sources and 16 purpose-selected study families. Within this selected corpus, four families develop dedicated 3D conversion, chiefly for hydraulic-domain or fuel-material preparation; the coding rule excludes routine terrain input and existing ocean analyses. Forecast cases mainly update states, controls or initial trajectories. Hydraulic connectivity can alter local inundation. Corrected fire fronts remain exposed to propagation error, while wider marine search regions improve coverage at increased search cost. These mechanisms explain why geometric accuracy and analysis fit alone cannot establish predictive value. We propose a framework that selects scene attributes, update targets and observation timing jointly. Matched alternatives share a future response quantity, information cutoff and resource budget; deployment-specific tolerances connect their comparison to evacuation requirements and search capacity. Physically coupled neural representations require calibrated material properties and an online delivery-time account. The proposed tests assess the forecast effect of additional scene detail, the persistence of a correction and the time left for response.