## 6 Conclusions  
This paper proposes an efficient automated method based on grid partitioning and connectivity constraints to address the challenges of memory overflow and block stitching due to the heterogeneous distribution of PS points in large-scale PSInSAR processing for high-density urban areas. Through grid partitioning and overlap region adjustment mechanisms, the method reduces memory usage while ensuring spatial consistency across the global deformation field. By combining graph theory-based connectivity analysis, it solves the problem of retaining information from discrete PS point clusters within sub-blocks, achieving the collaborative optimization of \"global continuity\" and \"local precision\".  
Experiments based on the TerraSAR-X data from Fuzhou's central urban area show that the method can process up to 14 million PS points on standard computational devices (32 GB memory), improving the processing efficiency by 25 times compared with traditional global methods. The deformation rate and elevation residual results are highly consistent with traditional methods (correlation coefficient \(\geq\) 0.98), and cross-validation with the Sentinel-1 data further confirmed its reliability. The innovation of this method lies in the construction of an integrated \"block-adjustment-fusion\" technical framework, significantly lowering the technical barriers for wide-area PSInSAR applications and providing a feasible path for operational deformation monitoring using high-resolution SAR data. Future research will focus on optimizing multi-scene image stitching and dynamic grid partitioning to extend its applicability to complex terrain and wide-area monitoring.  
Conceptualization, J.H.; methodology, C.S., J.H. and D.Z.; software, X.Z.; validation, C.S.; formal analysis, C.S. and D.Z.; resources, J.H., R.C. and J.P.; data curation, R.C. and J.P.; writing--original draft, C.S., D.Z. and H.H.; writing--review and editing, C.S., D.Z., X.Z. and H.H.; visualization, C.S.; supervision, R.C. and J.P.; project administration, J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Innovation Program of Fujian Province, grant number 2021Y3001; the National Natural Science Foundation of China, grant number 42474033; the Science and Technology Innovation Program of Hunan Province, grant number 2023 SK2012; and the Research and Innovation Project for Graduate Students at Central South University, grant number 1053320222945.  
The Sentinel-1 SAR data used in this study are publicly available through the Alaska Satellite Facility (ASF) at [[https://search.asf.alska.edu](https://search.asf.alska.edu)]([https://search.asf.alska.edu](https://search.asf.alska.edu)), and the Copernicus DEM GLO-30m can be accessed via the OpenTopography portal at [[https://portal.opentopography.org/datasetMetadata?otCollectionID=OT.032021.4326.1](https://portal.opentopography.org/datasetMetadata?otCollectionID=OT.032021.4326.1)]([https://portal.opentopography.org/datasetMetadata?otCollectionID=OT.032021.4326.1](https://portal.opentopography.org/datasetMetadata?otCollectionID=OT.032021.4326.1)). The TerraSAR-X data are not publicly available due to commercial licensing restrictions.  
The authors would like to thank the European Space Agency (ESA) for providing free Sentinel-1 data.  
Authors [PERSON] and [PERSON] were employed by Fuzhou Investigation and Surveying Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.