Abstract
Landscape architectural heritage, comprising classical gardens, imperial complexes, and culturally significant cultivated landscapes, demands continuous fine-grained monitoring that conventional decadal surveys cannot deliver. This study proposes an integrated UAV remote sensing system that couples acquisition, interpretation, and change reasoning within a single conservation-oriented workflow. A multi-scale attention semantic segmentation network, built on a modified U-Net backbone with channel–spatial attention, atrous spatial pyramid pooling, and gated cross-level fusion, addresses the extreme scale disparity among garden elements. A temporal Siamese change detection algorithm with phenology-aware pseudo-change suppression distinguishes seasonal variation from genuine alteration. The two components are embedded in a six-layer architecture spanning data acquisition, preprocessing, intelligent interpretation, change detection, knowledge base, and visualization. Experiments on three heritage sites of contrasting typology—Humble Administrator’s Garden, Chengde Summer Resort, and Gulangyu villa cluster—show that the proposed model reaches 91.78% overall accuracy and 81.25% mIoU, outperforming FCN, SegNet, U-Net, DeepLabV3+ , and Swin-UNet. The change detection algorithm holds false alarm rate below 8% under cross-season pairings, against 15–34% for representative baselines. An eighteen-month deployment yielded mean early-warning lead time of 27.3 days and processing throughput of 1.93 ha/h, supporting the system’s practical value for preventive conservation, routine inspection, and post-disaster damage assessment of landscape heritage.
Abbreviations
- UAV:
Unmanned aerial vehicle
- GSD:
Ground sampling distance
- NDVI:
Normalized difference vegetation index
- NIR:
Near-infrared
- RTK:
Real-time kinematic
- GNSS:
Global navigation satellite system
- IMU:
Inertial measurement unit
- LiDAR:
Light detection and ranging
- CNN:
Convolutional neural network
- FCN:
Fully convolutional network
- ASPP:
Atrous spatial pyramid pooling
- CA:
Channel attention
- SA:
Spatial attention
- DW-Sep Conv:
Depth-wise separable convolution
- MMD:
Maximum mean discrepancy
- CVA:
Change vector analysis
- PCA:
Principal component analysis
- BIT:
Bitemporal image transformer
- FC-Siam-conc:
Fully convolutional siamese concatenation network
- OA:
Overall accuracy
- mIoU:
Mean intersection over union
- FAR:
False alarm rate
- MR:
Miss rate
- FPS:
Frames per second
- GIS:
Geographic information system
- GAP:
Global average pooling
- ReLU:
Rectified linear unit
- DP:
Douglas–Peucker
- IRB:
Institutional review board
Acknowledgements
The authors gratefully acknowledge the financial support provided by the National Key R&D Program of China under Grant (2024YFB3908900) and the Shaanxi Province Special Research Project of Philosophy and Social Sciences (2025QN0643) and the Basic Scientific Research Funding for Central Universities Project (300102415605).
Funding
This work was supported by the National Key R&D Program of China under Grant (2024YFB3908900) and the Shaanxi Province Special Research Project of Philosophy and Social Sciences (2025QN0643) and the Basic Scientific Research Funding for Central Universities Project (300102415605).
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Not Applicable. This study did not involve human participants, human tissue, or animal subjects. The research relied on UAV-acquired remote sensing imagery of publicly accessible landscape heritage sites and expert annotation by the research team; no personally identifiable information was collected or processed.
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Liu, Y., Qiu, C. & Yang, L. Construction of an intelligent interpretation and change detection system based on UAV remote sensing for landscape architectural heritage conservation.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-63132-8
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DOI: https://doi.org/10.1038/s41598-026-63132-8
Keywords
- UAV remote sensing
- Landscape architectural heritage
- Semantic segmentation
- Change detection
- Multi-scale attention
- Preventive conservation
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