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Contemporary cities are the subject of new forms of visualization that are not only changing how we see the urban world but how it operates as a social environment. This chapter explores Google's Street View database and the Google Maps platform as sites for the production of distinctive new streams of visual data about cities around the world. I argue that this kind of digital infrastructure presents urban researchers with both new opportunities and new challenges, raising complex questions about the role of visual images in the context of the ongoing transition to a digital, computational, and networked image world.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.

Current methods for the preparation of composite powder feedstock for selective laser melting (SLM) rely on costly nanoparticles or yield inconsistent powder morphology. This study aims to develop a cost-effective Ti6Al4V-carbon feedstock, which preserves the parent Ti6Al4V particle’s flowability, and produces in situ TiC-reinforced Ti6Al4V composites with superior traits.

Ti6Al4V particles were directly mixed with graphite flakes in a planetary ball mill. This composite powder feedstock was used to manufacture in situ TiC-Ti6Al4V composites using various energy densities. Relative porosity, microstructure and hardness of the composites were evaluated for different SLM processing parameters.

Homogeneously carbon-coated Ti6Al4V particles were produced by direct mixing. After SLM processing, in situ grown 100–500 nm size TiC nanoparticles were distributed within the α-martensite Ti6Al4V matrix. The formation of TiC particles refines the Ti6Al4V β grain size. Relative density varied between 96.4% and 99.5% depending on the processing parameters. Hatch distance, exposure time and point distance were all effective on relative porosity change, whereas only exposure time and point distance were effective on hardness change.

This work introduces a novel, cost-effective powder feedstock preparation method for SLM manufacture of Ti6Al4V-TiC composites. The in situ SLM composites achieved in this study have high relative density values, well-dispersed TiC nanoparticles and increased hardness. In addition, the feedstock preparation method can be readily adapted for various matrix and reinforcement materials in future studies.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

Fire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety. The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.

This paper presents a flame smoke detection algorithm based on YOLOv5. The target regression loss function (CIoU) is used to improve the missed detection and false detection in target detection and improve the model detection performance. The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm. Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.

Based on the actual situation of flame smoke, the loss function and activation function of YOLOv5 model are improved. Based on the improved YOLOv5 model, a flame smoke detection algorithm with generalization performance is established. The improved model is compared with SSD and YOLOv4-tiny. The accuracy of the improved YOLOv5 model can reach 99.5%, which achieves a more accurate detection effect on flame smoke. The improved network model is superior to the existing methods in running time and accuracy.

Aiming at the actual particularity of flame smoke detection, an improved flame smoke detection network model based on YOLOv5 is established. The purpose of optimizing the model is achieved by improving the loss function, and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network. This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition.

Achieving tenure security is a global challenge impacting cities of the global south. The purpose of this paper is to evaluate the role of technology-enabled solutions as an enabler for the tenure rights of slum dwellers.

In this paper, we adopted a case study approach to analyze the use cases for technologies aiding India’s securitization of land tenure. The flagship state mission of Odisha, named the Jaga Mission, and that of Punjab, named BASERA – the Chief Minister’s Slum Development Program – were used as cases for this paper.

It was found that technologies like drone imagery and digital surveys fast-tracked the data collection and helped in mapping the slums with accuracy, mitigating human errors arising during measurement – a necessary condition for ensuring de jure tenure security. The adoption of a technology-based solution, along with a suitable policy and legal framework, has helped in the distribution of secure land titles to the slum dwellers in these states.

Odisha’s and Punjab’s journey in using technology to enable tenure security for its urban poor residents can serve as a model for the cities of the global south, dealing with the challenges of providing secure tenure and property rights.

The paper aims to provide a comprehensive analysis of artificial intelligence’s (AI) transformative impact on the real estate industry. By examining various AI applications, from property recommendations to compliance automation, this study highlights potential benefits such as increased accuracy and efficiency. At the same time, this study critically discusses potential drawbacks, like privacy concerns and job displacement. The paper's goal is to offer valuable insights to industry professionals and policy makers, aiding strategic decision-making as AI continues to reshape the landscape of the real estate sector.

This paper employs an extensive literature review, combined with a qualitative analysis of case studies. Various AI applications in the real estate industry are examined, including machine learning for property recommendations and valuation, VR/AR property tours, AI automation for contract and regulatory compliance, and chatbots for customer service. The study also delves into the optimisation potential of AI in building management, lead generation, and risk assessment, whilst critically discussing potential challenges such as data privacy, algorithmic bias, and job displacement. The outcomes aim to inform strategic decisions for industry professionals and policy makers.

The study finds that AI has significant potential to revolutionise the real estate industry through enhanced accuracy in property valuation, efficient automation and immersive AR/VR experiences. AI-driven chatbots and optimisation in building management also hold promise. However, this study also uncovers potential challenges, including data privacy issues, algorithmic biases, and possible job displacement due to increased automation. The insights gleaned from this study underscore the importance of strategic decision-making in harnessing the benefits of AI while mitigating potential drawbacks in the real estate sector.

The paper's practical implications extend to industry professionals, policy makers, and technology developers. Professionals gain insights into how AI can enhance efficiency and accuracy in the real estate sector, guiding strategic decision-making. For policy makers, understanding potential challenges like data privacy and job displacement informs regulatory measures. Technology developers can also benefit from understanding the sector-specific applications and concerns raised. Additionally, highlighting the need for addressing algorithmic bias and privacy concerns in AI systems may foster better design practices. Therefore, the paper's findings could significantly shape the future trajectory of AI integration in real estate.

The paper provides original value by offering a comprehensive analysis of the transformative impact of AI in the real estate industry. Its multi-faceted examination of AI applications, coupled with a critical discussion on potential challenges, provides a balanced perspective. The paper's focus on informing strategic decisions for professionals and policy makers makes it a valuable resource. Moreover, by considering both benefits and drawbacks, this study contributes to the discourse on AI's broader societal implications. In the context of rapid technological change, such comprehensive studies are rare, adding to the paper's originality.

High rate of accidents continue to plague the construction industry. The advancements in safety technologies can ameliorate construction health and safety (H&S). This paper aims to explore the use of emerging technologies as an effective solution for improving safety in construction projects.

Following a detailed literature review, a questionnaire survey was developed encompassing ten technologies for safety management and ten safety enablers using technologies in construction. A total of 133 responses were gathered from Malaysian construction practitioners. The collected quantitative data were subjected to descriptive and inferential statistical analyses to determine the meaningful relationships between the variables.

Findings revealed that the most effective emerging technologies for safety management are: building information modelling (BIM), wearable safety technologies and robotics and automation (R&A). The leading safety enablers are related to improve hazard identification, reinforce safety planning, enhance safety inspection, enhance safety monitoring and supervision and raise safety awareness.

Safety is immensely essential in transforming the construction industry into a robustly developed industry with high safety and quality standards. The adoption of safety technologies in construction projects can drive the industry towards the path of Construction 4.0.

The construction industry has historically been slow to adopt new technology. This study contributes to advancing the body of knowledge in the area of incorporating emerging technologies to further construction safety science and management in the context of the developing world. By taking cognisance of the pertinent emerging technologies for safety management and the safety enablers involved, construction safety can be enhanced using integrated technological solutions.

Selective laser sintering (SLS) is an essential technology in the field of additive manufacturing. However, SLS technology is limited by the traditional point-laser sintering method and has reached the bottleneck of efficiency improvement. This study aims to develop an image-shaped laser sintering (ISLS) system based on a digital micromirror device (DMD) to address this problem. The ISLS system uses an image-shaped laser light source with a size of 16 mm × 25.6 mm instead of the traditional SLS point-laser light source.

The ISLS system achieves large-area image-shaped sintering of polymer powder materials by moving the laser light source continuously in the x-direction and updating the sintering pattern synchronously, as well as by overlapping the splicing of adjacent sintering areas in the y-direction. A low-cost composite powder suitable for the ISLS system was prepared using polyether sulfone (PES), pinewood and carbon black (CB) powders as raw materials. Large-sized samples were fabricated using composite powder, and the microstructure, dimensional accuracy, geometric deviation, density, mechanical properties and feasible feature sizes were evaluated.

The experimental results demonstrate that the ISLS system is feasible and can print large-sized parts with good dimensional accuracy, acceptable geometric deviations, specific small-scale features and certain density and mechanical properties.

This study has achieved the transition from traditional point sintering mode to image-shaped surface sintering mode. It has provided a new approach to enhance the system performance of traditional SLS.