The collection and analysis of existing built environment data can help urban and rural planning departments to build a reasonable infrastructure distribution. How to collect and process data more accurately should be a key consideration. Lidar scanning as a high-precision data acquisition method can provide maintenance personnel with an effective digital copy of our real world. This project proposes a framework that uses point cloud segmentation algorithms to extract specific types of infrastructure and then uses GIS (geographic information system) to analyze the spatial distribution of infrastructure based on the extracted results to help inspectors efficiently check the rationality of infrastructure spatial distribution design. This study uses fire hydrants and buildings on the Purdue University campus as the object of study and attempts to automate the analysis to obtain the closest distance between the hydrants and the buildings. Point cloud data were collected using Trimble X7 and 43 hydrant stations were collected with the surrounding buildings and environment. Then, point cloud segmentation was used to extract point cloud instances of fire hydrants and buildings, and then the point clouds were converted into Raster data for import into GIS for further processing to achieve nearest distance estimation.