Under the background of rapid urban development and continuous climate change, frequent floods around the world have caused serious economic losses and social problems, which has become the main reason for the sustainable development of cities. Flood disaster risk assessment is an important non-engineering measure in urban disaster prevention and mitigation, and scientific flood disaster risk assessment is the premise and foundation of flood disaster risk management. This paper summarizes the current situation of flood risk assessment by analyzing the international literature in recent 20 years. The mechanism of flood disaster is mainly discussed. The flood disaster assessment methods are summarized, including historical disaster statistics method, multi-criteria index system method, remote sensing and GIS (Geographic Information System) coupling method, scenario simulation evaluation method and machine learning method. Furthermore, the development status of flood risk analysis and forecasting is summarized. Finally, the development trend and direction of flood risk assessment are put forward.

In urban point cloud scenarios, due to the diversity of different feature types, it becomes a primary challenge to effectively obtain point clouds of building categories from urban point clouds. Therefore, this paper proposes the Enhanced Local Feature Aggregation Semantic Segmentation Network (ELFA-RandLA-Net) based on RandLA-Net, which enables ELFA-RandLA-Net to perceive local details more efficiently by learning geometric and semantic features of urban feature point clouds to achieve end-to-end building category point cloud acquisition. Then, after extracting a single building using clustering, this paper utilizes the RANSAC algorithm to segment the single building point cloud into planes and automatically identifies the roof point cloud planes according to the point cloud cloth simulation filtering principle. Finally, to solve the problem of building roof reconstruction failure due to the lack of roof vertical plane data, we introduce the roof vertical plane inference method to ensure the accuracy of roof topology reconstruction. The experiments on semantic segmentation and building reconstruction of Dublin data show that the IoU value of semantic segmentation of buildings for the ELFA-RandLA-Net network is improved by 9.11% compared to RandLA-Net. Meanwhile, the proposed building reconstruction method outperforms the classical PolyFit method.

Urban waterlogging is a serious urban disaster, which brings huge losses to the social economy and environment of the city. As an important means of urban rainfall inundation analysis, numerical simulation plays an important role in promoting the risk assessment of urban waterlogging. Scientific and accurate assessment of waterlogging disaster losses is of scientific significance for the formulation of disaster prevention and mitigation measures and the guidance of post-disaster recovery and reconstruction. In this study, the SCS-CN hydrological model and GIS coupling numerical simulation method were used to simulate the inundation of urban waterlogging under four different rainfall return periods and to realize the visualization of the inundation range and waterlogging depth in Zhengzhou. At the same time, based on the numerical simulation results, the building is used as the basic assessment unit to construct a refined assessment framework for urban waterlogging risk at the street community level based on hazard, exposure, and vulnerability analysis. The refined risk assessment results have an important reference value for optimizing the working ideas of waterlogging control and providing a reference for local management departments to effectively deal with waterlogging disasters.

The development of high nature value farmland (HNVf) can effectively improve the problems of biodiversity reduction, non-point source pollution and carbon loss in intensive farmland. To this end, we developed a set of general indicators based on Landsat 8 OLI imagery, including land cover (LC), normalized difference vegetation index (NDVI), Shannon diversity (SH) and Simpson’s index (SI). Combined with a Kohonen neural network (KNN), we assigned weights and developed the first potential HNVf map of the Yellow River Delta in China. The results showed that the four indicators were very effective for the expression of HNVf characteristics in the study area, and that SH and SI, in particular, could reflect the potential characteristics of HNVf at the edge of intensive farmland. LC, NDVI, SH and SI were weighted as 0.45, 0.25, 0.15 and 0.15, respectively. It was found that the potential HNVf type 2 (i.e., low-intensity agriculture, and natural and structural elements such as shrubs, woodlands and small rivers) in the study area was concentrated at the edges of intensive farmland, the transition zones from farmland to rivers and the estuary wetland areas of northern and eastern rivers. LC played a leading role in identifying HNVf. Based on six randomly selected real-world verification data from Map World, it was found that the accuracy of the validation set for HNVf type 2 was 83.33%, which exhibited the good development potential of HNVf in the study area. This is the first potential HNVf type 2 map of the Yellow River Delta in China and could provide a great deal of potential guidance for the development and protection of farmland biodiversity and regional carbon sequestration.