Currently, heritage is challenged in the Indian city of Surat due to diverse pressures, including rapid urbanization, increasing housing demand, and socio-cultural and climate changes. Where rapid demographic growth of urban areas is happening, heritage is disappearing at an alarming rate. Despite some efforts from the local government, urban cultural heritage is being neglected and historic buildings keep being replaced by ordinary concrete buildings at a worryingly rapid pace. Discussions of challenges and issues of Surat’s urban area is supported by a qualitative dataset, including in-depth semi-structured interviews and focus groups with local policy makers, planners, and heritage experts, triangulated by observation and a photo-survey of two historic areas. Findings from this study reveal a myriad of challenges such as: inadequacy of urban conservation management policies and processes focused on heritage, absence of skills, training, and resources amongst decision makers and persistent conflict and competition between heritage conservation needs and developers’ interests. Furthermore, the values and significance of Surat’s tangible and intangible heritage is not fully recognized by its citizens and heritage stakeholders. A crucial opportunity exists for Surat to maximize the potential of heritage and reinforce urban identity for its present and future generations. Surat’s context is representative of general trends and conservation challenges and therefore recommendations developed in this study hold the potential to offer interesting insights to the wider planners and conservationists’ international community. This paper recommends thoughtful integration of sustainable heritage urban conservation into local urban development frameworks and the establishment of approaches that recognize the plurality of heritage values.

Purpose: Heritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approach: The artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”Findings: This study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implications: The study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/value: For ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

Purpose: Across the architecture, engineering and construction industry, the application of building information modelling (BIM) as a digital technology for architectural heritage is becoming increasingly relevant. However, scholarly research on the application of BIM for architectural heritage is still in its infancy stage and the research gaps and future directions of this research area are still unclear. This paper therefore aims to fill this gap by using a systematic and robust review using a mixed-method approach to show the gaps of research for BIM and architectural heritage and point to new directions for future works.Design/methodology/approach: The study includes a quantitative scientometric analysis and mapping and a qualitative study. A total of 354 articles related to BIM and architectural heritage were analysed using a combination of quantitative and qualitative tools. The qualitative study consists of a systematic literature review supported by data collected from stakeholder and training workshops on digital technologies and BIM for heritage.Findings: Findings from this study reveal the gaps in the field of heritage and BIM research according to retrieved articles from different countries and journals. Furthermore, emerging domains for future research were identified and these included: as-built modelling and 3D reconstruction; conservation, preservation and management; documentation; maintenance and restoration; virtual technology and simulation.Originality/value: The common research challenges were identified as an integrated outcome of the findings revealed from the scientometric mapping and qualitative review and resulted in key BIM and heritage research priority recommendations relevant to researchers within this field.