BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-2026//talk//YDTHMJ BEGIN:VTIMEZONE TZID:JST BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1 TZNAME:JST TZOFFSETFROM:+0900 TZOFFSETTO:+0900 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-2026-YDTHMJ@talks.osgeo.org DTSTART;TZID=JST:20260903T140000 DTEND;TZID=JST:20260903T143000 DESCRIPTION:In recent years\, the widespread use of mobile devices equipped with LiDAR sensors has made it possible to easily capture 3D point clouds . However\, these devices suffer from cumulative self-localization errors\ , limiting accurate 3D measurements to relatively small areas. Existing ap proaches to wide-area georeferencing of mobile LiDAR point clouds typicall y rely on GNSS receivers\, direct 3D point-cloud matching\, or alignment t o pre-existing high-precision urban 3D models. These approaches can be cos tly\, computationally demanding\, or impractical where such reference data are unavailable.\n\nTherefore\, this study proposes a low-cost method for constructing a high-precision wide-area 3D map using only LiDAR-equipped mobile devices and open data. It is intended to help municipalities and ci tizen communities without expensive equipment or professional surveying sk ills develop local digital twin infrastructure. The resulting 3D maps can support 3D visualization of potential disaster impacts and community disas ter preparedness.\n\nThe proposed processing pipeline accepts 3D point clo uds from mobile devices and Japanese governmental open geospatial data as input and outputs open point-cloud formats such as LAS/LAZ for seamless us e in FOSS4G software such as QGIS. The workflow consists of two stages: re gistration of adjacent point clouds and georeferencing of the integrated p oint cloud within a global geodetic coordinate system. Rather than directl y estimating rigid transformations in full 3D space\, the proposed method separates spatial information into horizontal and vertical components and optimizes them sequentially\, improving registration stability for point c louds acquired by mobile devices. Geographic coordinates are assigned thro ugh image matching against open geospatial data instead of GNSS-based posi tioning. All processing\, except point cloud acquisition\, is implemented using open-source Python libraries widely used in the FOSS4G ecosystem. To support reproducibility\, we will release the source code\, processing wo rkflow\, example configuration files\, and links to the open input dataset s used in the experiment.\n\nFirst\, rough overlaps are extracted from the approximate positional information of each point cloud. CSF is then appli ed to separate each point cloud into ground and non-ground points. Height- based slices of the non-ground points are projected into bird’s-eye-view images representing wall surfaces. ORB feature-based image matching in Op enCV provides initial alignment\, followed by horizontal refinement on the XY plane using the Point-to-Point ICP algorithm in Open3D. Finally\, vert ical and tilt errors are corrected using the ground points.\n\nNext\, abso lute coordinates are assigned to the registered point clouds using Fundame ntal Geospatial Data provided by the Geospatial Information Authority of J apan. Although validation is performed with Japanese open data\, the metho d is applicable in other regions where road edge vector data and digital e levation models (DEMs) are openly available. For horizontal georeferencing \, road edge data is used. Using geopandas and shapely\, a rough region ar ound the point cloud is extracted and converted into a road edge image. Th e integrated point cloud is processed in the same manner to generate a wal l surface image. OpenCV’s normalized cross-correlation (NCC) is then use d to estimate the optimal translation and rotation parameters. For vertica l georeferencing\, a 5 m mesh DEM is referenced\, and elevations are extra cted with rasterio. A correction surface is generated by smoothing the ele vation differences between the ground points and the DEM\, and the resulti ng correction values are applied to the entire point cloud. This aligns ab solute elevation with the DEM while preserving local terrain variations.\n \nTo verify the effectiveness of the proposed method\, an experiment was c onducted in the Jinaimachi district of Tondabayashi City\, Osaka Prefectur e. Measurements were conducted using an iPad Pro (11-inch\, 4th generation )\, and the Scaniverse application. Five scan datasets were obtained from these measurements. In addition\, ground truth data were prepared using me asurements from a high-performance 3D scanner (Matterport Pro3) and a GNSS receiver (Drogger RZX.D). As evaluation metrics\, both the RMSE between f eature points of adjacent point clouds after registration (relative accura cy) and the RMSE between the final constructed 3D map and the ground truth data (absolute accuracy) were evaluated. The target accuracy was defined as an RMSE of within 0.1 m for relative accuracy. For absolute accuracy\, the horizontal accuracy was required to be within 1.75 m\, corresponding t o “Map Information Level 2500” and suitable as a base map for hazard m apping\, while the vertical accuracy was required to be within 0.30 m\, su itable for flood simulation. \n\nAs a result of the evaluation\, the avera ge relative accuracy achieved an RMSE of 0.048 m\, sufficiently satisfying the target value. Regarding absolute accuracy\, the horizontal RMSE was 0 .63 m and the vertical RMSE was 0.09 m\, demonstrating favorable results. The results satisfy the requirements for Map Information Level 2500 horizo ntally and Level 500 vertically.\n\nFor the FOSS4G community\, the propose d pipeline demonstrates how mobile LiDAR\, governmental open data\, and op en-source geospatial libraries can be combined into a reproducible workflo w for practical 3D hazard mapping. It also aligns with the conference’s emphasis on Asian geospatial initiatives by demonstrating a reproducible w orkflow based on Japanese governmental open data. Experimental results con firmed that the proposed method provides sufficient accuracy for 3D hazard maps that enable intuitive visualization of flood depths and landslide-af fected areas. This method facilitates 3D data development in municipalitie s and citizen communities with limited budgets and can contribute to regio nal digital transformation. Future work includes improving robustness in w ider and more diverse environments and developing a web system that integr ates data from multiple devices to generate 3D maps automatically. Unlike workflows that depend on GNSS receivers or pre-existing high-precision 3D city models\, the proposed method enables georeferenced wide-area 3D mappi ng using only mobile LiDAR\, open geospatial data\, and an open-source pro cessing stack. DTSTAMP:20260717T225746Z LOCATION:Cosmos2 SUMMARY:A Pipeline for Low-Cost Wide-Area 3D Mapping Using LiDAR-Equipped M obile Devices and Open Data - Ryosei Ueda URL:https://talks.osgeo.org/foss4g-2026/talk/YDTHMJ/ END:VEVENT END:VCALENDAR