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In the low-lying, river-rich Pearl River Delta in South China, an extensive network of flood defense levees, spanning over 4400 km, plays a crucial role in urban flood management. These levees are designed to withstand floods and storm surges, yet their failure can lead to significant human and economic losses, highlighting the need for robust urban flood defense strategies. This necessitates the development of a sophisticated geographic information system for the levee network and rapid, accurate assessment methods for levee conditions to support water management and flood mitigation efforts. This study focuses on the levees along the Hengmen waterway in the Pearl River Delta, utilizing airborne Light Detection and Ranging (LiDAR) technology to gather 3D spatial data of the levees. Employing the Cloth Simulation Filter (CSF) algorithm, non-ground point cloud data were extracted. The study improved upon the region-growing algorithm, using a seed point set approach for the automatic extraction of levee point cloud data. The accuracy and completeness of levee extraction were evaluated using the quality index. This method achieved effective extraction of four levee types, showing significant improvements over traditional algorithms, with extraction quality ranging from 72% to 83%. Key research outcomes include the development of a novel method for detecting localized levee depressions based on the computation of the variance of angles between normal vectors in single-phase levee point cloud data. An adaptive optimal neighborhood approach was utilized to accurately determine the normal vectors, effectively representing the local morphology of the levee point clouds. Applied in three levee depression detection experiments, this method proved effective, demonstrating the capability of single-phase data in identifying regions of levee depression deformation. This advancement in levee monitoring technology marks a significant step forward in enhancing urban flood defense capabilities in regions such as the cities of the Pearl River Delta in China. [ABSTRACT FROM AUTHOR]

Automatizing the extraction of different objects from remote sensing data with deep learning methods has been a popular research topic. Buildings have been one of those popular objects to be extracted. Not only does the selection of neural network affect the results and accuracy of extracted buildings, but also the selection of different types of data for the task. Digital surface models (DSMs) are increasingly used in remote sensing and their demand has increased. Retrieving height information from surface models has proved helpful for accurate extraction of buildings. In this study was investigated, if the use of light detection and ranging (LiDAR) DSMs and DEMs with 25 cm pixel resolution will lead to more accurate building extraction results in comparison to the use of aerial DSMs. Results with UNet models trained with building vector labels, DSMs, DEMs and true orthophotos from multiple areas of Finland, were produced with different data combinations from two Finnish cities, Savonlinna and Pudasj¨arvi, to see, which combination would lead to the most accurate building detection results. Results were evaluated partly by visual inspection, and partly by quantitative assessment. Based on the tests carried out, combining the information from true orthophotos with LiDAR DSMs and 25 cm DEMs provided the most accurate results. In forest area, using LiDAR data increased the accuracy of building detection. However, in urban area, due to missing buildings from LiDAR data, its advantages were compromised. We suggest that the use of both imagery and LiDAR data should be the optimal solution. [ABSTRACT FROM AUTHOR]