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The assessment of populations affected by urban flooding is crucial for flood prevention and mitigation but is highly influenced by the accuracy of population datasets. The population distribution is related to buildings during the urban floods, so assessing the population at the building scale is more rational for the urban floods, which is possible due to the abundance of multi-source data and advances in GIS technology. Therefore, this study assesses the populations affected by urban floods through population mapping at the building scale using highly correlated point of interest (POI) data. The population distribution is first mapped by downscaling the grid-based WorldPop population data to the building scale. Then, the population affected by urban floods is estimated by superimposing the population data sets onto flood areas, with flooding simulated by the LISFLOOD-FP hydrodynamic model. Finally, the proposed method is applied to Lishui City in southeast China. The results show that the population affected by urban floods is significantly reduced for different rainstorm scenarios when using the building-scale population instead of WorldPop. In certain areas, populations not captured by WorldPop can be identified using the building-scale population. This study provides a new method for estimating populations affected by urban flooding.

Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering residents' travel behavior. First, a flood simulation was conducted using the LISFLOOD-FP model to predict the spatiotemporal distribution of flooding. Second, an agent-based model was used to simulate residents' movements during the urban flooding period. Finally, to study the evolution and patterns of urban flooding exposure, the exposure of population, roads, and buildings to urban flooding was simulated using Lishui, China, as a case study. The results showed that water depth was the major factor affecting total urban exposure in Lishui. Urban exposure to fluvial flooding was concentrated along the river, while exposure to pluvial flooding was dispersed throughout the area (independent from the river). Additionally, the population distribution on weekends was more variable than on weekdays and was more sensitive to floods. In addition, residents' response behavior (based on their subjective consciousness) may result in increased overall exposure. This study presents the first fully formulated method for dynamic urban flood exposure simulation at a high spatiotemporal resolution. The quantitative results of this study can provide fundamental information for urban flood disaster vulnerability assessment, socioeconomic loss assessment, urban disaster risk management, and emergency response plan establishment.

Spatial relations are the main part of geographical information in natural language. Their extraction and semantic interpretation play a significant role in bridging the gap between geographical information system and natural language. Normally spatial relations are described with certain spatial terms and syntactic rules in natural language. To overcome the disadvantage of manual induction of syntactic rules, this paper proposes a new machine learning approach based on a sequence alignment algorithm. Firstly, the description instances of spatial relations in a large-scale annotated corpus are extracted and analyzed, and the sequence alignment algorithm is used to calculate the pattern similarity between instances of spatial relations. Then, the instances with high similarity are generalized aspopularly used syntactic rules. Finally, these rules are used for extraction spatial relations in a test data to evaluate their validation. The experimental results indicate that the generalized rules can achieve better performance than those rules induced according to occurrence frequency in the corpus.

Natural language text describes the nature of people's internal representation of space. It is investigated that 80% of unstructured text has location expressions e.g. place names and spatial relations. In the past few years, text has become a most important geospatial data resource as well as survey, map, satellite images and GPS. The most previous research focused on the recognition of place names in text and its integration with map services. Spatial relations play an important role in the fields of spatial data modelling, spatial query, spatial analysis, spatial reasoning and map generalization. Spatial relations in text are described in natural language with qualitative spatial expressions including place names, spatial terms, prepositions, verbs and so on. And these expressions are combined with certain syntactic patterns to represent their semantic functions. An instance of spatial relation in text can be simply formalized as (C1, P1, C2, P2, C3), where P1 and P2 are place names, and C1, C2 and C3 are the context. Support Vector Machine (SVM) is a pattern recognition method popularly used in information extraction from text. This paper investigates the extraction of spatial relations based on SVM model which can implement the recognition of spatial expressions and their classification synchronously. For the SVM model, a set of feature vectors are specified, such as lexical tokens, spatial terms, syntactic structures and geographical feature types of place names, and a multi-label classifier is presented to solve the multi-classification problem. Finally, an experimental evaluation is explored in a Chinese annotation corpus. This study proves that spatial terms are important indicators for identification of spatial relations in text. However, there is serious ambiguity of their classification. Therefore, integration of much more context information could potentially improve the performance of extraction of spatial relations in text.