As a crucial component of cultural heritage, industrial heritage possesses considerable cultural, historical, and economic significance. The key challenge for industrial heritage sites is to foster eco-conscious actions among visitors while boosting tourism spending. Based on the S-O-R theory, we constructed a relationship model between authenticity, place attachment and environmental responsibility behavior through structural equation modeling and tested it with the survey data of tourists in Maoming open-pit mine ecological park. Findings reveal that both the object-related authenticity and the existential authenticity of the experience enhance the sense of reliance and belonging to the place, with the former also directly promoting visitors’ eco-conscious actions. In contrast, experiential authenticity does not exert a direct effect on eco-conscious actions. The sense of place reliance and identity are found to be significantly and positively linked to eco-conscious behavior. Furthermore, place reliance and identity act as intermediaries in the relationship between object authenticity, experiential authenticity, and eco-conscious behavior, serving as a sequential mediating factor. The study suggests strategies such as augmenting financial support to preserve the authenticity of the site, prioritizing the enhancement of infrastructure and the creation of cultural offerings to elicit emotional responses, and reinforcing emotional bonds to encourage eco-conscious actions.

Anxiety caused by the lack of social skills is the biggest problem faced by children with ASD. Playing can improve children’s social skills and relieve anxiety. This study aimed to explore the influence of urban built environments on ASD children’s play behavior. The participants in this study were 57 parents of children with ASD. An anonymous questionnaire was used to collect and analyze data. At the same time, retrospective semi-structured interviews with 31 parents of ASD children were performed to validate the data analysis results. The results showed that lower residential building density, higher residential greening and higher destination accessibility have positive effects on ASD children’s play behavior. Excellent transportation facilities and high NDVI vegetation coverage have positive effects on the play behavior of children with ASD. More recreational facilities and recreational playability have positive impacts on the play behavior of children with ASD. The population density and number of children in the destination, as well as public facilities, influence the play behavior of children with ASD. The research results can promote the integration of this group into urban life and further promote social equity. At the same time, with the social needs of autistic children as an intermediary, it is expected to further explore new directions for sustainable urban development. Finally, combined with the research results, parents of ASD children are given proposals for how to increase the likelihood of children’s play behavior by choosing appropriate urban built environments.

To resolve the problems of deep convolutional neural network models with many parameters and high memory resource consumption, a lightweight network-based algorithm for building detection of Minnan folk light synthetic aperture radar (SAR) images is proposed. Firstly, based on the rotating target detection algorithm R-centernet, the Ghost ResNet network is constructed to reduce the number of model parameters by replacing the traditional convolution in the backbone network with Ghost convolution. Secondly, a channel attention module integrating width and height information is proposed to enhance the network’s ability to accurately locate salient regions in folk light images. Content-aware reassembly of features (CARAFE) up-sampling is used to replace the deconvolution module in the network to fully incorporate feature map information during up-sampling to improve target detection. Finally, the constructed dataset of rotated and annotated light and shadow SAR images is trained and tested using the improved R-centernet algorithm. The experimental results show that the improved algorithm improves the accuracy by 3.8%, the recall by 1.2% and the detection speed by 12 frames/second compared with the original R-centernet algorithm.