Integrated urban land cover analysis using deep learning and post-classification correction

Abstract

The quantification of urban impervious area has important implications for the design and management of urban water and environmental infrastructure systems. This study proposes a deep learning model to classify 15-cm aerial imagery of urban landscapes, coupled with a vector-oriented post-classification processing algorithm for automatically retrieving canopy-covered impervious surfaces. In a case study in Corpus Christi, TX, deep learning classification covered an area of approximately 312 km2 (or 14.86 billion 0.15-m pixels), and the post-classification effort led to the retrieval of over 4 km2 (or 0.18 billion pixels) of additional impervious area. The results also suggest the underestimation of urban impervious area by existing methods that cannot consider the canopy-covered impervious surfaces. By improving the identification and quantification of various impervious surfaces at the city scale, this study could directly benefit a variety of environmental and infrastructure management practices and enhance the reliability and accuracy of processed-based models for urban hydrology and water infrastructure.