Synthetic Aperture Radar Interferometry (InSAR) is a powerful tool for monitoring ground deformation over large areas, with applications in geological disaster monitoring, inversion of groundwater status, building health analysis, earthquake parameter extraction, post-disaster relief, and more. However, existing digital earth platforms, such as Google Earth and ArcGIS, face challenges in supporting the exploration and querying of vast InSAR datasets, including slow processing speeds, unsupported data formats, and difficulties with secondary development. This study examines the challenges associated with online visualization of time-series point clouds and proposes principles for pre-processing, storage, exploration, and querying of such datasets. Challenges include slow graphics rendering on webpages, limited network bandwidth that hinders real-time updates during exploration, and large data sizes that can pose storage challenges. To overcome these challenges, we suggest separating position and colorc information from temporal information, partitioning data using an octree structure, and using various compression techniques. Based on these principles, we utilize Cesium.js, a JavaScript library that enables developers to create 3D globes and maps in a web browser with high performance and precision to develop a platform for the visualization and interpretation of InSAR point clouds, which we call WIMAP. This allows us to easily create interactive visualizations of geospatial data. To test the platform, we processed two SAR datasets covering a plain and a mountainous area, respectively, and obtained corresponding time-series point clouds. We tested the performance of the platform using these point clouds and demonstrated its ability to run smoothly under such conditions. Specifically, on a computer equipped with a mid-range graphics card, it was able to maintain a frame rate of over 30 FPS while browsing time-series point clouds containing tens of millions points. Additionally, compared to the original plain binary format, the size of binary data stored on server could be reduced to approximately one quarter using preprocessing tools provided by the platform. All deformation analysis tools, including single point time-series query, deformation rate along profile line query, and multi-temporal deformation along profile line query, work properly. Spatial profile analysis, which included spatial interpolation with a buffering radius of 200 meters, was performed on time-series point cloud datasets with over 100 epochs and took less than 20 seconds to complete. This performance is comparable to that of locally conducted queries based on Kd-tree on available computers. The WIMAP platform allows users to explore InSAR point clouds in their browser and facilitates the distribution of InSAR results. Individual organizations and research institutions can upload their processed InSAR results to the platform's server, providing geoscientific information for users in various industries. With the visualization and interpretation tools bundled, users can analyze InSAR multi-temporal observations, combined with three-dimensional terrains and optical images, to gain insights into various geological phenomena. This may accelerate the research progress in several areas, such as landslide studies, earthquake monitoring, volcanic deformation analysis, and coastal erosion monitoring.

InSAR;point cloud processing;data visualization;WebGL;deformation data