Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data.

Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-scale topographical information. This paper addresses this issue by providing a novel method for evaluating surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging) measurements. The developed method derives topographical properties and runoff accumulation by applying a semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales and identify problematic low points that could be susceptible to water ponding. Municipalities and property owners can use this information to take targeted corrective maintenance actions. • The study developed a method that integrates computer vision and flow direction model to assess surface drainage condition. • This study applied the developed method to 50 street blocks in a neighborhood in Houston, Texas. • The developed model can identify key drainage and land features within a street block with an accuracy of 88.5%. • The results can show problematic areas susceptible to water ponding and inform drainage maintenance decisions. [ABSTRACT FROM AUTHOR]