Training on LiDAR for Advanced Spatial Analysis: Data Processing and 3D Modeling

Module 1: Introduction to LiDAR Technology

• Principles of LiDAR and remote sensing

• LiDAR sensors, platforms (airborne, terrestrial, UAV-based)

• Data characteristics and applications

• Case study: Urban growth monitoring with airborne LiDAR

Module 2: LiDAR Data Acquisition and Formats

• Acquisition techniques and system calibration

• LAS/LAZ formats and metadata

• Understanding point cloud structure

• Case study: UAV-based LiDAR for infrastructure mapping

Module 3: Preprocessing of LiDAR Data

• Noise filtering and outlier removal

• Point cloud registration and alignment

• Tools: LAStools, PDAL, CloudCompare

• Case study: Preparing LiDAR datasets for terrain analysis

Module 4: Point Cloud Classification

• Ground vs. non-ground classification

• Identifying vegetation, buildings, and water bodies

• Automated vs. manual classification methods

• Case study: Forest canopy height modeling

Module 5: Digital Elevation Models from LiDAR

• DEM, DSM, and DTM generation

• Accuracy assessment and validation

• Tools: ArcGIS Pro, QGIS, Global Mapper

• Case study: Flood risk modeling using LiDAR-derived DEMs

Module 6: Feature Extraction and Object Detection

• Building footprint extraction

• Power lines, roads, and infrastructure mapping

• Vegetation metrics from LiDAR

• Case study: 3D building mapping for urban planning

Module 7: 3D Modeling and Visualization

• Creating 3D city and terrain models

• Visualization in ArcGIS Pro, Blender, and CloudCompare

• Integration with BIM (Building Information Modeling)

• Case study: Smart city development using LiDAR-based 3D models

Module 8: Advanced LiDAR Applications

• Forestry: biomass estimation, canopy height, forest structure

• Hydrology: watershed delineation, floodplain mapping

• Mining: volumetric analysis, terrain deformation monitoring

• Case study: Forest carbon stock assessment with LiDAR

Module 9: LiDAR Data Integration and Automation

• Fusion with multispectral, hyperspectral, and SAR data

• Machine learning applications in LiDAR classification

• Python scripting for LiDAR processing (PDAL, PyLAS)

• Case study: Landslide susceptibility mapping with integrated datasets

Module 10: Emerging Trends and Project Development

• UAV-based LiDAR advancements

• Cloud processing of LiDAR data (AWS, GEE)

• Future of 3D geospatial analytics

• Group project: Designing a LiDAR-based workflow for a chosen sector (urban, forestry, mining, disaster management, or climate)