Modern warfare increasingly takes place within cities, resulting in widespread damage, loss of life, population displacement, and destroyed livelihoods. Much conflict-induced urban damage mapping involves a before/after change mapping approach through expert visual interpretation or machine learning classification of very high resolution commercial imagery. These ad hoc analyses tend to be localized to a single city, are intended for acute detection rather than continuous monitoring of damage, and have limited means for independent validation. By comparison, open EO data have untapped potential for long-term, systematic monitoring of locations, patterns, and trends of urban damage across broad conflict regions. This talk presents the results of a purely EO-driven conflict damage monitoring framework applied to major cities affected by Syrian and Yemeni civil wars. In presenting never-before-mapped patterns and sequences of urban conflict damage, this talk highlights the need for broader uptake of open EO data in urban damage mapping.