LiDAR point-cloud capture
ARKit captures dense spatial data while the device camera records overlapping site imagery.
Spatial Computing Case Study
WebGIS 3D Construction Site Scanning Platform
An iOS LiDAR, photogrammetry, GIS, and cloud platform for capturing construction sites and producing measurable 3D and geospatial outputs.
Construction and Civil EngineeringiOS, Cloud Backend and Web Dashboard
Project Overview
From operational challenge to working product
WebGIS turns a LiDAR-equipped iPhone into an integrated construction-site capture tool. Field engineers can collect point clouds, geotagged photographs, and GPS coordinates in one scanning session.
Captured assets are uploaded to a Python and Django backend, processed through photogrammetry pipelines, and delivered through a web dashboard as point clouds, USDZ and GLB models, GeoTIFF overlays, and measurement tools.
Business Challenge
The problems the product needed to solve
Replace slow, expensive, and fragmented site-documentation workflows with a mobile-first process.
Capture LiDAR point clouds and geotagged images simultaneously without frame drops, misalignment, or data loss.
Upload large scan packages reliably from construction sites with unstable connectivity.
Produce 3D, point-cloud, measurement, and GIS outputs through one connected platform.
Our Solution
A connected system designed for the workflow
The iOS application uses ARKit and Core Location for LiDAR depth, spatial tracking, and GPS metadata, while RealityKit and Metal maintain a responsive real-time point-cloud preview.
A Django REST Framework backend manages uploads and asynchronous AliceVision processing. The admin dashboard lets project teams review scans, measure distance, area, and volume, inspect 3D models, and export GIS-ready files.
Key Features
Capabilities delivered through the platform
Geotagged imagery
Core Location attaches coordinates, altitude, and heading to photographs captured during the scan.
Dual photogrammetry pipeline
AliceVision and Apple photogrammetry workflows produce detailed models in USDZ and GLB formats.
GeoTIFF generation
The backend creates georeferenced raster outputs for use in QGIS, ArcGIS, and related GIS tools.
Point-cloud measurements
Dashboard tools support distance, area, trench-depth, and excavation-volume analysis.
Offline capture and smart sync
Scan data remains on the device and uploads with retry behavior when connectivity becomes available.
Technologies Used
Product and engineering stack
- Swift
- UIKit
- ARKit
- RealityKit
- Metal
- Core Location
- Python
- Django REST Framework
- AliceVision
- USDZ
- GLB
- GeoTIFF
Results & Impact
What the work made possible
One connected field-to-dashboard workflow
Point clouds, images, GPS data, 3D models, GIS files, and measurements are managed through one platform.
Stable simultaneous capture
LiDAR and image capture were engineered to run together without degrading either data stream.
Up to 100 meters per scan
A single mobile session can cover construction features such as trenches and foundations across substantial site distances.
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