Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data

Exploring the Quantitative Assessment of Spatial Risk in Response to Major Epidemic Disasters in Megacities: A Case Study of Qingdao

With the global spread of various human-to-human epidemics, public health issues have become a focus of attention. Therefore, it is of great importance to improve the quantitative risk assessment of the construction of resilient cities in terms of epidemic disasters. Starting with the dimensions of social activities and material space, this paper took Qingdao, China, with a population of 5 million, as an example, and took its seven municipal districts as the research scope. In this paper, five risk factors, including the Population density index, Night light index, Closeness index of roads, Betweenness index of roads and Functional mixed nuclear density index were selected for weighted superposition analysis. We conducted a quantitative assessment of the spatial risk of epidemic disaster so as to obtain the classification and spatial structure of the epidemic disaster risk intensity. The results show that: ① The roads with a large traffic flow are most likely to lead to the risk of urban spatial agglomeration, and the areas with a large population density and large mixture of infrastructure functions are also important factors causing the risk of epidemic agglomeration. ② The analysis results regarding the population, commerce, public services, transportation, residence, industry, green space and other functional places can reflect the high-risk areas for epidemic diseases with different natures of transmission. ③ The risk intensity of epidemic disasters is divided into five risk grade areas. Among them, the spatial structure of epidemic disasters, composed of the first-level risk areas, is characterized by "one main area, four secondary areas, one belt and multiple points" and has the characteristics of spatial diffusion. ④ Catering, shopping, life services, hospitals, schools and transportation functional places are more likely to cause crowd gathering. The management of these places should be focused on prevention and control. At the same time, medical facilities should be established at fixed points in all high-risk areas to ensure the full coverage of services. In general, the quantitative assessment of the spatial risk of major epidemic disasters improves the disaster risk assessment system in the construction of resilient cities. It also focuses on risk assessment for public health events. It is helpful to accurately locate the agglomeration risk areas and epidemic transmission paths that are prone to outbreak or cause epidemic transmission in cities so as to assist the relevant practitioners in containing the epidemic from the initial stage of transmission in a timely manner and prevent the further spread of the epidemic.

Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a Landsat Time Series

Development and a growing population in Saudi Arabia have led to a substantial increase in the size of its urban areas. This sustained development has increased policymakers’ need for reliable data and analysis regarding the patterns and trends of urban expansion throughout the country. Although previous studies on urban growth in Saudi cities exist, there has been no comprehensive research that focused on all 13 regional capitals within the country. Our study addressed this gap by producing a new annual long-term dataset of 30 m spatial resolution that covered 35 years (1985–2019) and maintained a high overall accuracy of annual classifications across the study period, ranging between 93 and 98%. Utilizing the continuous change detection and classification (CCDC) algorithm and all available Landsat data, we classified Landsat pixels into urban and non-urban classes with an annual frequency and quantified urban land cover change over these 35 years. We implemented a stratified random sampling design to assess the accuracy of the annual classifications and the multi-temporal urban change. The results revealed that Saudi capitals experienced massive urban growth, from 1305.28 ± 348.71 km2 in 1985 to 2704.94 ± 554.04 km2 in 2019 (± values represent the 95% confidence intervals). In addition to the high accuracy of the annual classifications, the overall accuracy of the multi-temporal urban change map was also high and reached 91%. The urban expansion patterns varied from city to city and from year to year. Most capital cities showed clear growth patterns of edge development, that is, a continuous expansion of built-up lands radiating from existing urban areas. This study provides distinct insights into the urban expansion characteristics of each city in Saudi Arabia and a synoptic view of the country as a whole over the past four decades. Our results provided a dataset that can be used as the foundation for future socioeconomic and environmental studies.

Building-Level Urban Functional Area Identification Based on Multi-Attribute Aggregated Data from Cell Phones—A Method Combining Multidimensional Time Series with a SOM Neural Network

Portraying functional urban areas provides useful insights for understanding complex urban systems and formulating rational urban plans. Mobile phone user trajectory data are often used to infer the individual activity patterns of people and for functional area identification, but they are difficult to obtain because of personal privacy issues and have the drawback of a sparse spatial and temporal distribution. Deep learning models have been widely utilized in functional area recognition but are limited by the difficulty of acquiring training samples with large data volumes. This paper aims to achieve a fast and automatic identification of large-scale urban functional areas without prior knowledge. This paper uses Nanjing city as a test area, and a self-organizing map (SOM) neural network model based on an improved dynamic time warping (Ndim-DTW) distance is used to automatically identify the function of each building using mobile phone aggregated data containing work and residence attributes. The results show that the recognition accuracy reaches 88.7%, which is 12.4% higher than that of the K-medoids method based on the DTW distance using a single attribute and 7.8% higher than that of the K-medoids method based on the Ndim-DTW distance with multiple attributes, confirming the effectiveness of the multi-attribute mobile phone aggregated data and the SOM model based on the Ndim-DTW distance. Furthermore, at the traffic analysis zone (TAZ) level, this paper detects that Nanjing has seven functional area hotspots with a high degree of mixing. The results can provide a data basis for urban studies on, for example, the urban spatial structure, the separation of occupations and residences, and environmental suitability evaluation.

Study on the Spatial Classification of Construction Land Types in Chinese Cities: A Case Study in Zhejiang Province

Identifying the land-use type and spatial distribution of urban construction land is the basis of studying the degree of exposure and the economic value of disaster-affected bodies, which are of great significance for disaster risk predictions, emergency disaster reductions, and asset allocations. Based on point of interest (POI) data, this study adopts POI spatialization and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to accomplish the spatial classification of construction land. Zhejiang province is selected as a study area, and its construction land is divided into 11 land types using an accurate spatial classification method based on measuring the area of ground items. In the research, the POI dataset, which includes information, such as spatial locations and usage types, was constructed by big data cleaning and visual interpretation and approximately 620,000 pieces in total. The overall accuracy of the confusion matrix is 76.86%, which is greatly improved compared with that constructed with EULUC data (61.2%). In addition, compared with the official statistical data of 11 cities in Zhejiang Province, the differences between the calculated spatial proportions and statistics are not substantial. Meanwhile, the spatial characteristics of the studied land-use types are consistent with the urban planning data but with higher accuracy. The research shows that the construction land in Zhejiang Province has a high degree of land intensity, concentrated assets, and high economic exposure. The approach proposed in this study can provide a reference for city management including urbanization process, risk assessment, emergency management and asset allocation.

Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method

Urban land use mapping is critical to understanding human activities in space. The first national mapping result of essential urban land use categories of China (EULUC-China) was released in 2019. However, the overall accuracies in some of the plain cities such as Beijing, Chengdu, and Zhengzhou were lower than 50% because many parcel-based mapping units are large with mixed land uses. To address this shortcoming, we proposed an area of interest (AOI)-based mapping approach, choosing Beijing as our study area. The mapping process includes two major steps. First, grids with different sizes (i.e., 300 m, 200 m, and 100 m) were derived from original land parcels to obtain classification units with a suitable size. Then, features within these grids were extracted from Sentinel-2 spectral data, point of interest (POI), and Tencent Easygo crowdedness data. These features were classified using a random forest (RF) classifier with AOI data, resulting in a 10-category map of EULUC. Second, we superimposed the AOIs layer on classified units to do some rectification and offer more details at the building scale. The overall accuracy of the AOI layer reached 98%, and the overall accuracy of the mapping results reached 77%. This study provides a fast method for accurate geographic sample collection, which substantially reduces the amount of fieldwork for sample collection and improves the classification accuracy compared to previous EULUC mapping. The detailed urban land use map could offer more support for urban planning and environmental policymaking.

Intelligent Mapping of Urban Forests from High-Resolution Remotely Sensed Imagery Using Object-Based U-Net-DenseNet-Coupled Network

The application of deep learning techniques, especially deep convolutional neural networks (DCNNs), in the intelligent mapping of very high spatial resolution (VHSR) remote sensing images has drawn much attention in the remote sensing community. However, the fragmented distribution of urban land use types and the complex structure of urban forests bring about a variety of challenges for urban land use mapping and the extraction of urban forests. Based on the DCNN algorithm, this study proposes a novel object-based U-net-DenseNet-coupled network (OUDN) method to realize urban land use mapping and the accurate extraction of urban forests. The proposed OUDN has three parts: the first part involves the coupling of the improved U-net and DenseNet architectures; then, the network is trained according to the labeled data sets, and the land use information in the study area is classified; the final part fuses the object boundary information obtained by object-based multiresolution segmentation into the classification layer, and a voting method is applied to optimize the classification results. The results show that (1) the classification results of the OUDN algorithm are better than those of U-net and DenseNet, and the average classification accuracy is 92.9%, an increase in approximately 3%; (2) for the U-net-DenseNet-coupled network (UDN) and OUDN, the urban forest extraction accuracies are higher than those of U-net and DenseNet, and the OUDN effectively alleviates the classification error caused by the fragmentation of urban distribution by combining object-based multiresolution segmentation features, making the overall accuracy (OA) of urban land use classification and the extraction accuracy of urban forests superior to those of the UDN algorithm; (3) based on the Spe-Texture (the spectral features combined with the texture features), the OA of the OUDN in the extraction of urban land use categories can reach 93.8%, thereby the algorithm achieved the accurate discrimination of different land use types, especially urban forests (99.7%). Therefore, this study provides a reference for feature setting for the mapping of urban land use information from VHSR imagery.

Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China

Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition.