Time-series monitoring of the land subsidence in the Yangtze River coastal area is crucial for maintaining river stability and early warning of disasters. This study employed PS-InSAR and SBAS-InSAR techniques to monitor the land subsidence along the Yangtze River in Nanjing, using a total of 42 Sentinel-1A images obtained between April 2015 and November 2021. The accuracy of both methods was compared and validated, while a comprehensive analysis was conducted to ascertain the spatial distribution characteristics and underlying causes of land subsidence. The maximum deviation between the two methods and six leveling point data did not exceed ±5 mm. Within the 5 km buffer zone on either side of the Yangtze River in Nanjing, four subsidence funnels were identified. Analysis of the factors contributing to land subsidence in this area indicates that underground engineering construction and operation, increasing ground building area, and geological condition all have certain correlations to the land subsidence. The results obtained through PS-InSAR and SBAS-InSAR technologies revealed a high degree of consistency in monitoring outcomes, and the latter method exhibited superior monitoring accuracy than the former one in this area. This study holds significant implications for guiding the scientific management of urban geohazards along the Yangtze River.

Achieving lightweight representations of building mesh models with accurate geometry and fine structural details is a key challenge in urban 3D modelling. Most existing mesh simplification methods focus on minimizing geometric error while neglecting the specific characteristics of building models in terms of geometric structure and semantic hierarchy, thus leading to structural degradation and semantic inconsistencies. To address this issue, this paper proposes a structure–semantic dual-constrained edge-collapse decimation method for simplifying dense building mesh models reconstructed from point clouds. Our core innovation lies in the joint enforcement of geometric structural constraints and building semantic constraints to effectively preserve both geometric structural features and component-level semantic structures of the models. By incorporating these two constraints, we adaptively assign higher collapse penalties to key structural edges and semantic boundaries, achieving lightweight building model simplification while maintaining fine-level structural details even under high compression ratios. Our method is extensively validated on several datasets of varying scales and complexities, including single-building models from Sketchfab, the large-scale urban datasets SUM and STPLS3D, and the ArCH cultural heritage dataset. Experimental results demonstrate that our method achieves superior or comparable performance compared to the existing methods across all the test datasets, consistently achieving lower or on-par geometric errors measured by RMSE and MAE. Furthermore, our simplified results can be semantically organized and stored under the CityGML paradigm, which provides a unified data support for sharing, semantic retrieval, downstream analysis, and other applications of lightweight building models.