Real estate needs to improve its adoption of disruptive technologies to move from traditional to smart real estate (SRE). This study reviews the adoption of disruptive technologies in real estate. It covers the applications of nine such technologies, hereby referred to as the Big9. These are: drones, the internet of things (IoT), clouds, software as a service (SaaS), big data, 3D scanning, wearable technologies, virtual and augmented realities (VR and AR), and artificial intelligence (AI) and robotics. The Big9 are examined in terms of their application to real estate and how they can furnish consumers with the kind of information that can avert regrets. The review is based on 213 published articles. The compiled results show the state of each technology’s practice and usage in real estate. This review also surveys dissemination mechanisms, including smartphone technology, websites and social media-based online platforms, as well as the core components of SRE: sustainability, innovative technology and user centredness. It identifies four key real estate stakeholders—consumers, agents and associations, government and regulatory authorities, and complementary industries—and their needs, such as buying or selling property, profits, taxes, business and/or other factors. Interactions between these stakeholders are highlighted, and the specific needs that various technologies address are tabulated in the form of a what, who and how analysis to highlight the impact that the technologies have on key stakeholders. Finally, stakeholder needs as identified in the previous steps are matched theoretically with six extensions of the traditionally accepted technology adoption model (TAM), paving the way for a smoother transition to technology-based benefits for consumers. The findings pertinent to the Big9 technologies in the form of opportunities, potential losses and exploitation levels (OPLEL) analyses highlight the potential utilisation of each technology for addressing consumers’ needs and minimizing their regrets. Additionally, the tabulated findings in the form of what, how and who links the Big9 technologies to core consumers’ needs and provides a list of resources needed to ensure proper information dissemination to the stakeholders. Such high-quality information can bridge the gap between real estate consumers and other stakeholders and raise the state of the industry to a level where its consumers have fewer or no regrets. The study, being the first to explore real estate technologies, is limited by the number of research publications on the SRE technologies that has been compensated through incorporation of online reports.

Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91%. Disaster management organizations can use this model to assess the damages to critical city infrastructure and other assets worldwide to instigate proper disaster responses and minimize the damages. This can help with the smart governance of the cities where all emergent disasters are addressed promptly.

Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically to maintain its reliability and structural health. Visual signs of cracks and depressions indicate stress and wear and tear over time, leading to failure/collapse if these cracks are located at critical locations, such as in load-bearing joints. Manual inspection is carried out by experienced inspectors who require long inspection times and rely on their empirical and subjective knowledge. This lengthy process results in delays that further compromise the infrastructure’s structural integrity. To address this limitation, this study proposes a deep learning (DL)-based autonomous crack detection method using the convolutional neural network (CNN) technique. To improve the CNN classification performance for enhanced pixel segmentation, 40,000 RGB images were processed before training a pretrained VGG16 architecture to create different CNN models. The chosen methods (grayscale, thresholding, and edge detection) have been used in image processing (IP) for crack detection, but not in DL. The study found that the grayscale models (F1 score for 10 epochs: 99.331%, 20 epochs: 99.549%) had a similar performance to the RGB models (F1 score for 10 epochs: 99.432%, 20 epochs: 99.533%), with the performance increasing at a greater rate with more training (grayscale: +2 TP, +11 TN images; RGB: +2 TP, +4 TN images). The thresholding and edge-detection models had reduced performance compared to the RGB models (20-epoch F1 score to RGB: thresholding −0.723%, edge detection −0.402%). This suggests that DL crack detection does not rely on colour. Hence, the model has implications for the automated crack detection of concrete infrastructures and the enhanced reliability of the gathered information.