The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, reconstructing ancient buildings presents unique challenges, including the slow speed of 3D reconstruction using traditional methods, the complex textures of ancient structures and geometric issues caused by repeated textures. Additionally, there is a hash conflict problem when rendering outdoor scenes using neural radiation fields. To address these challenges, this paper proposes a 3D reconstruction method based on depth-supervised neural radiation fields. To enhance the representation of the geometric neural network, the addition of a truncated signed distance function (TSDF) supplements the existing signed distance function (SDF). Furthermore, the neural network’s training is supervised using depth information, leading to improved geometric accuracy in the reconstruction model through depth data obtained from sparse point clouds. This study also introduces a progressive training strategy to mitigate hash conflicts, allowing the hash table to express important details more effectively while reducing feature overlap. The experimental results demonstrate that our method, under the same number of iterations, produces images with clearer structural details, resulting in an average 15% increase in the Peak Signal-to-Noise Ratio (PSNR) value and a 10% increase in the Structural Similarity Index Measure (SSIM) value. Moreover, our reconstruction model produces higher-quality surface models, enabling the fast and highly geometrically accurate 3D reconstruction of ancient buildings.

Fast, high-precision texture maps and high-frame-rate level of detail (LOD) generation for realistic 3D models are foundational data infrastructures for smart cities. However, LOD generation faces three main issues: reduced model accuracy from mesh simplification, inefficient texture memory utilization, and browsing lag with detail loss. This paper proposed a novel LOD rendering method with multilevel structure-keeping mesh simplification and fast texture alignment for realistic 3D models. Firstly, a multilevel structure-keeping mesh simplification method with mesh segmentation and vertex classification was used to generate a simplified mesh with high-precision structure preservation. Secondly, a fast texture alignment method was proposed that uses segmentation information and LSCM parameterization to acquire texture blocks. The method integrated integral images and a precise multi-template strategy to align texture blocks to obtain texture maps with high completeness and high occupancy. Finally, by integrating these methods, an LOD generation method with fast multilevel pyramid construction and adaptive tree organization is proposed. This method achieved high-precision multilevel structure-keeping, along with a high occupancy rate of texture maps, facilitating a high frame rate for LOD model construction. Compared with QEF, QEM, low-poly, CGAL and Nvdiffrec, the proposed mesh simplification algorithm demonstrated average accuracy improvements of 12.1%, 24.2%, 57.1%, 17.9% and 3.2%, respectively. Compared with OpenMVE, ContextCapture and Xatlas, the proposed texture alignment algorithm achieved average occupancy improvements of 27.74%, 11.89% and 4.80%, respectively. Compared with the state-of-the-art ContextCapture and Smart3D, the proposed method for browsing large-scale realistic 3D models increased the frame rate by 17.3% and 14.4%, respectively.

The combined effects of climate change and socio-economic development have intensified the risk of water supply–demand imbalance in China. To project future trends, this study develops a multi-scenario coupled prediction framework integrating climate, socio-economic, and human activity drivers, combining data-driven and physically based modeling approaches to assess terrestrial water storage imbalance in nine major river basins under six representative SSP–RCP scenarios through the end of the 21st century. Using ISIMIP multi-model runoff outputs along with GDP and population projections, agricultural, industrial, and domestic water demands were estimated. A Water Conflict Index was proposed by integrating the Water Supply–Demand Stress Index and the Standardized Hydrological Runoff Index to identify high-risk basins. Results show that under high-emission scenarios, the WCI in the Yellow River, Hai River, and Northwest Rivers remains high, peaking during 2040–2069, while low-emission scenarios significantly alleviate stress in most basins. Water allocation inequity is mainly driven by insufficient supply in arid northern regions and limited redistribution capacity in resource-rich southern basins. Targeted strategies are recommended for different risk types, including inter-basin water transfer, optimization of water use structure and pricing policies, and the development of resilient management systems, providing scenario-based quantitative support for future water security and policy-making in China.

To enhance the overall quality and consistency of depth maps, Digital Surface Models (DSM), and True Digital Orthophoto Map (TDOM) in UAV image reconstruction, this paper proposes a multi-stage adaptive optimization generation method. First, to address the noise and outlier issues in depth maps, an adaptive joint bilateral filtering-based optimization method is introduced. This method repairs anomalous depth values using a four-directional filling strategy, incorporates image-guided joint bilateral filtering to enhance edge structure representation, effectively improving the accuracy and continuity of the depth map. Next, during the DSM generation stage, a method based on depth value voting space and elevation anomaly detection is proposed. A joint mechanism of elevation calculation and anomaly point detection is used to suppress noise and errors, while a height value completion strategy significantly enhances the geometric accuracy and integrity of the DSM. Finally, in the TDOM generation process, occlusion detection and gap-line generation techniques are introduced. Together with uniform lighting, color adjustment, and image gap optimization strategies, this improves texture stitching continuity and brightness consistency, effectively reducing artifacts caused by gaps, blurriness, and lighting differences. Experimental results show that the proposed method significantly improves depth map smoothness, DSM geometric accuracy, and TDOM visual consistency compared to traditional methods, providing a complete and efficient technical pathway for high-quality surface reconstruction.