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With the acceleration of urbanization, subway construction has become a key part of urban development. However, shield construction often needs to cross existing railway lines, which requires accurate prediction of ground surface settlement of railway operation facilities to ensure railway safety and smooth construction. The existing Internet of Things (IOT) ground surface settlement prediction mainly relies on deep learning algorithms, but these algorithms are often difficult to take into account the extraction of local features and the capture of global features when processing long time sequence data, resulting in the prediction effect is affected when processing long-term dependent ground surface settlement data. In order to solve the above problems, this paper proposes a ground surface settlement prediction method based on T-BiGRU Mask Multi-Head Self-Attention Informer (TM-Informer). First, T-BiGRU is introduced into the model to effectively cover a wide range of time scales and extract the local feature of the settlement sequence data. Second, Mask Multi-Head Self-Attention (MMSA) is used to effectively solve the long-term dependence problem in long time sequence settlement data prediction and enhance the global feature capture ability of the model. In this paper, TM-Informer is compared with Informer, Transformer and LSTM by four evaluation indexes MSE, MAE, RMSE and MAPE. The experimental results show that the MSE, MAE, RMSE and MAPE of TM-Informer are 0.301, 0.432, 0.548 and 2.617, respectively, which are 0.08, 0.04, 0.06 and 0.26 higher than those of Informer, which proves the effectiveness of TM-Informer in railway ground surface settlement monitoring. [ABSTRACT FROM AUTHOR]

Wind comfort is an important factor when new buildings in existing urban areas are planned. It is common practice to use computational fluid dynamics (CFD) simulations to model wind comfort. These simulations are usually time-consuming, making it impossible to explore a high number of different design choices for a new urban development with wind simulations. Data-driven approaches based on simulations have shown great promise, and have recently been used to predict wind comfort in urban areas. These surrogate models could be used in generative design software and would enable the planner to explore a large number of options for a new design. In this paper, we propose a novel machine learning workflow (MLW) for direct wind comfort prediction. The MLW incorporates a regression and a classification U-Net, trained based on CFD simulations. Furthermore, we present an augmentation strategy focusing on generating more training data independent of the underlying wind statistics needed to calculate the wind comfort criterion. We train the models based on different sets of training data and compare the results. All trained models (regression and classification) yield an F 1 -score greater than 80% and can be combined with any wind rose statistic. [ABSTRACT FROM AUTHOR]

This paper introduces a deep learning (DL) tool capable of classifying cities and revealing the features that characterize each city from a visual perspective. The study utilizes city view data captured from satellites and employs a methodology involving DL-based classification for city identification, along with an Explainable Artificial Intelligence (AI) tool to unveil definitive features of each city considered in this study. The city identification model implemented using the ResNet architecture yielded an overall accuracy of 84%, featuring 45 cities worldwide with varied geographic locations, Human Development Index (HDI), and population sizes. The portraying attributes of urban locations have been investigated using an explanatory visualization tool named Relevance Class Activation Maps (CAM). The methodology and findings presented by the current study enable decision makers, city managers, and policymakers to identify similar cities through satellite data, understand the salient features of the cities, and make decisions based on similarity patterns that can lead to effective solutions in a wide range of objectives such as urban planning, crisis management, and economic policies. Analyzing city similarities is crucial for urban development, transportation strategies, zoning, improvement of living conditions, fostering economic success, shaping social justice policies, and providing data for indices and concepts such as sustainability and smart cities for urban zones sharing similar patterns. [ABSTRACT FROM AUTHOR]

A rich volume of research has detected urban growth by quantifying the land use/land cover (LU/LC) changes based on remote sensing technologies. However, the research has limitations in identifying various formats of urban growth, particularly small-scale urban growth, such as infill development or redevelopment in urban areas, prompted by smart growth and sustainable urban development. This paper aims to design a framework for the accurate detection of residential infill development in the City of Los Angeles by employing a deep-learning method that has been increasingly applied to analyze phenomena in cities. In order to do so, this paper develops six models that reflect the variations of image classification methods, deep-learning algorithms, and estimation types. The results from the models emphasize the potential of the deep-learning models for the detection of micro-urban growth at a city scale. However, there is room for the improvement of estimation accuracy in the cases that detect some new developments and replacements as not developed parcels. The findings suggest that the performance of the models depends primarily on the articulations of the training dataset rather than the types of deep-learning algorithms. Findings from the models provide the city with insights into land use and transportation planning decision-making based on a better understanding of the spatial distribution patterns of urban growth and development. • Remote sensing data and geospatial technologies are limited in identifying small-scale urban growth due to the low resolutions of satellite data. • Deep-learning methods can be used to detect infill development by automatically classifying and labeling longitudinal satellite images. • The model performance primarily depends on the articulations of the training dataset rather than the types of deep-learning algorithms. [ABSTRACT FROM AUTHOR]

Urban expansion and fossil fuel dependence have led to energy and environmental concerns, highlighting the need for sustainable solutions. Rooftop photovoltaic (RPV) systems offer a viable solution for urban energy transition by utilizing idle rooftop space and meeting decentralized energy needs. However, due to limited information on building function attributes, detailed assessments of RPV potential at the city scale are still complicated. This study introduces a cost-effective framework that combines big Earth data and deep learning to evaluate RPV potential for various investment entities. We introduced a sample construction strategy that considers built environment and location awareness to improve the effectiveness and generalizability of the framework. Applied to Shanghai, our building function recognition model achieved 88.67%, 88.51%, and 67.18% for accuracy, weighted-F1, and kappa, respectively. We identified a potential installed capacity of 42 GW with annual electricity generation of 17 TWh for industrial and commercial, 16 TWh for residential, and 10 TWh for public RPVs. The levelized cost of electricity ranges from 0.32 to 0.41 CNY/kWh, demonstrating that both user-side and plant-side grid parity was achieved. This study supports sustainable development by providing detailed urban energy assessments and guiding local energy planning. The methods and findings may offer insights for similar studies globally. [ABSTRACT FROM AUTHOR]

Urban space constitutes a complex system, the quality of which directly impacts the quality of life for residents. In high-density cities, factors such as the green coverage in street spaces, color richness, and accessibility of services are crucial elements affecting daily life. Moreover, the application of advanced technologies, such as deep learning combined with street view image analysis, has certain limitations, especially in the context of high-density urban streets. This study focuses on the street space quality within the urban fabric of the Macau Peninsula, exploring the characteristics of the street space quality within the context of high-density urban environments. By leveraging street view imagery and multi-source urban data, this research employs principal component analysis (PCA) and deep-learning techniques to conduct a comprehensive analysis and evaluation of the key indicators of street space quality. Utilizing semantic segmentation and ArcGIS technology, the study quantifies 16 street space quality indicators. The findings reveal significant variations in service-related indicators such as the DLS, ALS, DCE, and MFD, reflecting the uneven distribution of service facilities. The green coverage index and color richness index, along with other service-related indicators, are notably influenced by tourism and commercial activities. Correlation analysis indicates the presence of land-use conflicts between green spaces and service facilities in high-density urban settings. Principal component analysis uncovers the diversity and complexity of the indicators, with cluster analysis categorizing them into four distinct groups, representing different combinations of spatial quality characteristics. This study innovatively provides a quantitative assessment of street space quality, emphasizing the importance of considering multiple key factors to achieve coordinated urban development and enhance spatial quality. The results offer new perspectives and methodologies for the study of street space quality in high-density urban environments. [ABSTRACT FROM AUTHOR]

Street quality plays a crucial role in promoting urban development. There is still no consensus on how to quantify human street quality perception on a large scale or explore the relationship between street quality and street composition elements. This study investigates a new approach for evaluating and comparing street quality perception and accessibility in Shanghai and Chengdu, two megacities with distinct geographic characteristics, using street-view images, deep learning, and space syntax. The result indicates significant differences in street quality perception between Shanghai and Chengdu. In Chengdu, there is a curvilinear distribution of the highest positive perceptions along the riverfront space and a radioactive spatial distribution of the highest negative perceptions along the ring road and main roads. Shanghai displays a fragmented cross-aggregation and polycentric distribution of the streets with the highest positive and negative perceptions. Thus, it is reasonable to hypothesize that street quality perception closely correlates with the urban planning and construction process of streets. Moreover, we used multiple linear regression to explain the relationship between street quality perception and street elements. The results show that buildings in Shanghai and trees, pavement, and grass in Chengdu were positively associated with positive perceptions. Walls in both Shanghai and Chengdu show a consistent positive correlation with negative perceptions and a consistent negative correlation with other positive perceptions, and are most likely to contribute to the perception of low street quality. Ceilings were positively associated with negative perceptions in Shanghai but are not the major street elements in Chengdu, while the grass is the opposite of the above results. Our research can provide a cost-effective and rapid solution for large-scale, highly detailed urban street quality perception assessments to inform human-scale urban planning. [ABSTRACT FROM AUTHOR]

With the increasing global population and escalating ecological and farmland degradation, challenges to the environment and livelihoods have become prominent. Coordinating urban development, food security, and ecological conservation is crucial for fostering sustainable development. This study focuses on assessing the "Ecology-Agriculture-Urban" (E-A-U) space in Yulin City, China, as a representative case. Following the framework proposed by Chinese named "environmental capacity and national space development suitability evaluation" (hereinafter referred to as "Double Evaluation"), we developed a Self-Attention Residual Neural Network (SARes-NET) model to assess the E-U-A space. Spatially, the northwest region is dominated by agriculture, while the southeast is characterized by urban and ecological areas, aligning with regional development patterns. Comparative validations with five other models, including Logistic Regression (LR), Naive Bayes (NB), Gradient Boosting Decision Trees (GBDT), Random Forest (RF) and Artificial Neural Network (ANN), reveal that the SARes-NET model exhibits superior simulation performance, highlighting it's ability to capture intricate non-linear relationships and reduce human errors in data processing. This study establishes deep learning-guided E-A-U spatial evaluation as an innovative approach for national spatial planning, holding broader implications for national-level territorial assessments. [ABSTRACT FROM AUTHOR]

During the process of urban development, there is large-scale laying of underground pipeline networks and coordinated operation of both new and old networks. The underground concrete drainage pipes have become a focus of operation and maintenance due to their strong concealment and serious corrosion. The current manual inspections for subterranean concrete drainage pipelines involve high workloads and risks, which makes meeting the diagnostic needs of intricate urban pipeline networks challenging. Through advanced information technology, it has reached a consensus to intelligently perceive, accurately identify, and precise prediction of the condition of urban subterranean drainage networks. The development process of detection and evaluation methods for underground concrete drainage pipe networks is the focus of this study. The study discusses common algorithms for classifying, locating, and quantifying pipeline defects by combining the principles of deep learning with typical application examples. The intelligent progression of information collection methods, image processing techniques, damage prediction models, and pipeline diagnostic systems is systematically elaborated upon. Lastly, prospects for future research of intelligent pipeline diagnosis are provided. [ABSTRACT FROM AUTHOR]

Road networks in the past are imperative for understanding evolution of transportation infrastructure, urban sprawl, and route planning, etc. Various approaches have been developed for road extraction from historical maps, among which deep learning techniques stand out as the most effective ones. However, little attention has been paid to investigating road vectorization and classification from historical maps. Moreover, road classification via machine learning methods usually requires large amounts of dedicated training data. To address these issues, this paper proposes a novel and comprehensive framework for road vectorization and classification on the basis of road segmentation from historical maps. First, deep learning is used to get pixel-wise raster road segmentation results, which are further skeletonized using morphological operations. Then, considering that each road class is represented with a certain symbol, a painting function is defined for each class able to paint the corresponding symbol. These painting functions are then used to draw road segments along the skeletons. Since the start and end points in each painting function are used to vectorise the segment, this method achieves vectorization and classification at the same time. Our method is validated on four Siegfried map sheets in Switzerland, and evaluated via both visual and quantitative assessments. The results indicate that the method is capable of classifying roads accurately. In particular, two evaluation metrics completeness and correctness achieve 90.69% and 72.71% respectively for road class 2 which accounts for the highest portion in the map. Moreover, the results of this method avoid the saw-toothed issue of vectorised road lines. This research is beneficial for creating complete vector road network datasets with class information to support decision-making in urban planning and transportation. • A novel framework is proposed to vectorise and classify roads from historical maps. • Deep CNNs are utilized to systematically and accurately extract roads. • Road classification and vectorization are achieved simultaneously by symbol painting. • Visual and quantitative assessments validate the effectiveness and robustness of the framework. [ABSTRACT FROM AUTHOR]