Community-scale urban flood monitoring through fusion of time-lapse imagery, terrestrial lidar, and remote sensing data

Abstract. High-frequency flood events in urban areas pose significant cumulative hazards. These floods are often difficult to detect and monitor using existing infrastructure, making the development of alternative approaches critical. This study presents the implementation of a computer vision-based urban flood monitoring network deployed in Cahokia Heights, Illinois, USA. Flood observations were collected at 30-minute intervals using consumer-grade trail cameras. Water surface elevations were estimated from the intersection of segmented flood masks with 2D-projected terrestrial lidar data. Flood extents and depths were extrapolated using a terrain depression-filling algorithm. Camera-derived peak flood extents and depths were compared to independent predictions from a 2D HEC-RAS Rain-on-Grid flood model. This procedure was applied to two flood events, one moderate and one severe, using imagery from two camera sites. For the severe event, water level estimates agreed closely between cameras, with a median difference of less than 3 cm and a peak difference of less than 2 cm. For the moderate event, differences were larger (median <10 cm, peak <16 cm). Agreement between modeled and camera-derived peak flood extents exceeded 90 % for the severe event but ranged between 21 % and 42 % for the moderate event. We use the convergence and divergence of independent camera observations to infer differences in spatiotemporal flood connectivity, disconnected in the moderate event and connected in the severe one. This study demonstrates the utility of low-cost, camera-based systems for high-resolution monitoring of flood dynamics in complex urban environments and highlights their potential integration with hydrodynamic modeling.