The identification of urban functional areas is essential for urban planning and sustainable development. Spatial grids are the basic units for the implementation of urban plans and management by cities or development zones. The emergence of internet “big data” provides new ideas for the identification of urban functional areas. Based on point of interest (POI) data from Baidu Maps, the Xicheng District of Beijing was divided into grids with side lengths of 200, 500, and 1000 m in this study. The kernel density method was used to analyze the spatial structure of POI data. Two indicators, that is, the frequency density and category ratio, were then used to identify single- and mixed-functional areas. The results show that (1) commercial and financial areas are concentrated in the city center and multiple business centers have not developed; (2) scenic areas account for the largest proportion of single-functional areas in the Xicheng District of Beijing, followed by education and training, residence, and party and government organizations areas; and (3) the 200 × 200 m and 500 × 500 m grids are the most suitable for the identification of single- and mixed-functional areas, respectively.

Machine learning has been successfully used for object recognition within images. Due to the complexity of the spectrum and texture of construction and demolition waste (C&DW), it is difficult to construct an automatic identification method for C&DW based on machine learning and remote sensing data sources. Machine learning includes many types of algorithms; however, different algorithms and parameters have different identification effects on C&DW. Exploring the optimal method for automatic remote sensing identification of C&DW is an important approach for the intelligent supervision of C&DW. This study investigates the megacity of Beijing, which is facing high risk of C&DW pollution. To improve the classification accuracy of C&DW, buildings, vegetation, water, and crops were selected as comparative training samples based on the Google Earth Engine (GEE), and Sentinel-2 was used as the data source. Three classification methods of typical machine learning algorithms (classification and regression trees (CART), random forest (RF), and support vector machine (SVM)) were selected to classify the C&DW from remote sensing images. Using empirical methods, the experimental trial method, and the grid search method, the optimal parameterization scheme of the three classification methods was studied to determine the optimal method of remote sensing identification of C&DW based on machine learning. Through accuracy evaluation and ground verification, the overall recognition accuracies of CART, RF, and SVM for C&DW were 73.12%, 98.05%, and 85.62%, respectively, under the optimal parameterization scheme determined in this study. Among these algorithms, RF was a better C&DW identification method than were CART and SVM when the number of decision trees was 50. This study explores the robust machine learning method for automatic remote sensing identification of C&DW and provides a scientific basis for intelligent supervision and resource utilization of C&DW.

With rapid urbanization, the disposal and management of urban construction waste have become the main concerns of urban management. The distribution of urban construction waste is characterized by its wide range, irregularity, and ease of confusion with the surrounding ground objects, such as bare soil, buildings, and vegetation. Therefore, it is difficult to extract and identify information related to urban construction waste by using the traditional single spectral feature analysis method due to the problem of spectral confusion between construction waste and the surrounding ground objects, especially in the context of very-high-resolution (VHR) remote sensing images. Considering the multi-feature analysis method for VHR remote sensing images, we propose an optimal method that combines morphological indexing and hierarchical segmentation to extract the information on urban construction waste in VHR images. By comparing the differences between construction waste and the surrounding ground objects in terms of the spectrum, geometry, texture, and other features, we selected an optimal feature subset to improve the separability of the construction waste and other objects; then, we established a classification model of knowledge rules to achieve the rapid and accurate extraction of construction waste information. We also chose two experimental areas of Beijing to validate our algorithm. By using construction waste separability quality evaluation indexes, the identification accuracy of construction waste in the two study areas was determined to be 96.6% and 96.2%, the separability indexes of the construction waste and buildings reached 1.000, and the separability indexes of the construction waste and vegetation reached 1.000 and 0.818. The experimental results show that our method can accurately identify the exposed construction waste and construction waste covered with a dust screen, and it can effectively solve the problem of spectral confusion between the construction waste and the bare soil, buildings, and vegetation.

Considering current problems in the identifying, locating, and supervising of construction waste, it is difficult to achieve real-time target judgement and identification in the short term. This study proposes a deep learning-based construction waste identification and classification system, named IUNet-IF, developed using unmanned aerial vehicle (UAV) images. High-resolution UAV aerial images were used to establish a sample database and train U-Net models. The U-Net model, which exhibited the highest overall performance and identification accuracy, was enhanced to develop an IUNet model. Subsequently, the texture and colour features of UAV images were extracted and input to the IUNet model to build a novel IUNet-IF model. The proposed method uses the optimal model and an input combination of texture features, colour features, and original images, which improves the segmentation results compared to those when only the original image is used. The experimental results indicate that the system can achieve a maximum pixel accuracy index, mean intersection over union (mIoU), and mean pixel accuracy (MPA) of 98.98%, 93.78%, and 97.26%, respectively. These results verify the feasibility of extracting construction waste covered by a dust-proof net from UAV remote sensing images and can provide reference information for the automatic extraction of construction waste.

Waterlogging happens almost in every rainstorm in city roads and the concave type of overpasses in Beijing. It causes to serious effect for the security of people’s daily travel. For resolving this kind of problem, this paper designed a monitoring network by water level sensors and other technologies of internet of things, and developed a monitoring and early warning system for detecting the real-time information of waterlogging conditions in urban roads. This system can capture the real time information of waterlogging depth, make a fast query statistics for certain road or area, and also provide the short message service for the public. It is very convenient for the managers working in department of flood control and command center to collect the real-time information of waterlogging status around urban roads. This is helpful for the local government to perform the correspondingly emergency plan and issue waterlogging information for the public, so as to reduce the impact on the security of people’s travel caused by the suddenly bad weather.