The emergence of LiDAR-equipped mobile devices has enabled low-cost 3D point cloud acquisition. However, accurate 3D model reconstruction using such devices remains confined to individual, locally captured scans. This limitation makes wide-area coverage infeasible without additional correction. Existing methods for wide-area mapping require specialized equipment or high-accuracy base maps, which poses significant barriers in terms of cost and data infrastructure availability. This study proposes a low-cost pipeline for constructing wide-area 3D maps using only LiDAR-equipped mobile devices and open data. The proposed approach automatically registers multiple locally captured point clouds and assigns absolute coordinates to each by referencing open geospatial datasets. A key feature of the method is the decomposition of 3D spatial information into horizontal and vertical components. These components are processed independently to reduce errors and improve computational efficiency. Validation experiments in Tondabayashi City, Osaka, Japan confirmed a horizontal RMSE of 1.75 m or less and a vertical RMSE of 0.10 m or less. Both values meet the accuracy requirements for 1:2,500-scale disaster prevention base maps. The vertical accuracy in particular exceeds the stricter standard required for flood hazard mapping applications. These results demonstrate that combining mobile devices with open data enables non-experts to construct high-accuracy 3D maps suitable for disaster risk management applications.