Natural and human-caused disasters, including uncontrolled urbanization pose multiple short-term and long-term risks, such as building inhabitants being vulnerable to heat or cold waves, older people or other vulnerable individuals struggling to reach safe places during emergencies, and citizens not receiving timely alerts from public authorities regarding impending flash floods, or firestorms, among others. The RETIME project aims to address impactful changes at both the contextual district level and individual homes. It will introduce a data-driven tool that aggregates existing data from weather stations, sensor networks, and satellite images, automated on-site surveys to simulate the impacts of current phenomena and future projections. The innovative aspect lies in an advanced computational analysis that generates prospective scenarios based on socio-architectural and environmental studies, combined with local, territorial remote and on-site surveys. RETIME will develop a suite of 4 innovative adaptation solutions for reducing risk in urban areas: 1) A sensor-based IT automated alert system; 2) A Digital Building Twin (DBT); 3) A digital Building Renovation Passport (BRP); 4) A Resilience Knowledge Hub and Decision Support platform. These tools will be sensitive and capable of prioritizing alerts based on the architectural and societal context-specific features in three pilot areas (in Portugal, Slovakia, and Estonia). RETIME will meet citizens' real-time needs while supporting control and decision-making processes by: identifying vulnerable groups and critical hotspots, enhancing building resilience; utilizing existing datasets and forecasting computational tools; delivering adaptation plans with a focus on carbon-neutral lifecycle awareness; and increasing citizens' understanding of natural disasters and their multi-scale impacts. Overall, RETIME strives to create a more resilient, informed, and prepared urban environment for all citizens.

Floods are major natural disasters with severe impacts in urban areas, where increasingly high population density and infrastructure together with worsening climate change trends exacerbate the risk. Reliable and timely monitoring is critical for resilience in terms of pre-disaster preparedness, emergency response, and post-disaster relief. Remote Sensing accurately evaluates flood extent, but data can be expensive and inaccessible. Open RS represents a not fully exploited potential limited by the relatively coarse resolution. Moreover, recent advances in computer vision techniques based on deep learning (DL) and novel observational opportunities can provide valuable information on both flood extent and depth in combination with RS. STURM aims to advance urban flood knowledge by combining open RS Sentinel imagery and crowdsourcing (semantic and visual data) using DL with the ambition of overcoming the constraints of spatial resolution and limited information. STURM leverages free and newly available opportunistic observing systems providing a globally consistent, open-source-based, smart method for improved multi-source observations of hydroclimatic hazardous events in urban areas. The research objectives are to assess and accurately map urban flood extent and depth with enhanced spatial resolution (sub-pixel mapping and measurements from street-level images) and validate the methodology against real disaster events. STURM’s novel data fusion paradigm suits the demand to fill data and knowledge gaps at the urban scale while offering a benchmark solution for hydrological model validation. The DL-based pipeline combines the strengths of globally available data sources while reducing human and economic resource consumption. Global applicability, low cost, and immediate usability are methodological pillars of the STURM project that determine its high impact potential to enhance urban flood resilience and face the global hydrological challenges of the 2030s and beyond.