Frequent, low-intensity urban pluvial flooding is notoriously difficult to detect and monitor. This study introduces a novel, low-cost approach using computer vision to integrate time-lapse photos with lidar data to estimate water levels and flood extents. Applied to two case study flood events and validated against a two-dimensional flood model, this method shows how community-centered, adaptable monitoring systems can capture spatiotemporal flood dynamics often missed by traditional methods.