This study highlights the need to strengthen the conservation and management of World Heritage properties by redefining their boundaries for sustainable cities and communities, in accordance with the Sustainable Development Goals (SDGs). The boundaries of World Heritage properties are defined at the time of inscription and serve as the target area for protection, management, and use. However, many sites inscribed in the 1990s were designated without clear boundary definitions, which may pose a threat to sustainability due to boundary uncertainty in urban areas under development pressure. Therefore, this study discusses the essential measures for the sustainable protection and management of heritage sites by redefining the boundary of the Hwaseong Fortress, a World Heritage site. For this purpose, a qualitative content analysis using MAXQDA and an overlay analysis using ArcGIS pro were conducted to reach the following conclusions. The analysis of the Hwaseong Fortress attributes identified urban components, in addition to walls and facilities, as attributes that convey the Outstanding Universal Value (OUV) of this property. However, the boundary at the time of inscription does not consider the inner and outer urban components as heritage, and the boundary used in Korea also does not include them, so it was confirmed that the attributes other than the fortress are not managed as World Heritage. Therefore, it is necessary to change the perspective and consider the Hwaseong Fortress as an urban heritage site. By strengthening the Hwaseong Fortress as an urban heritage site and giving it the protection of the World Heritage designation it deserves, it will ultimately be able to contribute to the SDGs. To achieve this, it is suggested that the boundary be redefined to include all attributes of the property based on the OUV attributes of the Hwaseong Fortress, similar to the approach used in this study.

Abstract Background Accurately representing the spatial distribution of organisms is essential for understanding ecological systems and managing natural environments. Conventional surveys for estimating macroinvertebrate abundance in tidal flats need labor-intensive burrow counting. Although model-based and remote sensing approaches have attempted to improve abundance accuracy, they do not fully reflect actual abundance. Results We introduce an advanced approach for estimating the abundance of multiple macroinvertebrate species using drone-based high-resolution orthophotos combined with artificial intelligence (AI)-driven object detection to identify their burrow openings. We constructed training and validation datasets for three macroinvertebrate species ( Laomedia sp., Uca arcuata , and Macrophthalmus japonicus ) using high-resolution drone orthophotos of biogenic features, including burrow openings and feeding or excretion traces of each species. The datasets were then used to train the YOLO network, and detection accuracy was evaluated through the multi-class identification of the burrow openings of each species in the validation datasets. The multi-class detection accuracy for Laomedia sp., U. arcuata , and M. japonicus averaged 75%, with variation attributed to species-specific burrow characteristics. Notably, the multi-class detection accuracy for the three species was comparable in two regions, with and without pre-established training datasets, suggesting its potential broad applicability and scalability beyond the study area. Conclusions This study represents the first attempt to estimate the abundance of multiple macroinvertebrate species using drones and AI, indicating the potential to develop a method that derives spatial information based on precise measurements reflecting actual abundance.