GIS Engineer

Challenge

Processing raw LiDAR data with varying point densities and inconsistent classification across multiple flight missions

Strategy

• Developed MicroStation macros to automate data preprocessing and cleaning.
• Implementation of custom classification routines in TerraScan to standardize point classification (ground, vegetation, buildings) across all flights.
• Developed Python scripts using laspy to automate splitting, validation, and merging of LAS files

Results

• Reduced processing time by 15%
• Achieved 95% classification accuracy
• Delivered high-resolution DTM/DSM products
• Established new standards for company LiDAR workflows

Challenge

The municipality needed a centralized GIS database to support several departments. It required handling complex data relationships, enforcing robust validation rules, and enabling a multi-user editing environment with proper conflict management

Strategy

• Designed a normalized geodatabase schema with approximately 100 feature classes
• Implemented coded domains for standardized data entry
• Created Arcade attribute rules for automated validation
• Developed Python toolbox for data migration and ETL processes
• Built versioned editing workflows with conflict resolution

Results

• Migrated 10+ years of legacy data without data loss
• Reduced data entry errors by 90% with validation rules
• Enabled collaboration across departments
• Automated manual workflows saving hours of daily work

Challenge

The client requested to manage more than 10,000 km of fiber optic network and infrastructure while ensuring compliance with AutoCAD plans and project requirements

Strategy

• Coordinated with cross-functional teams to align fiber optic and infrastructure data with project requirements and AutoCAD plans
• Participation in project follow-up meetings to ensure progress and resolve technical issues
• Collaborated with clients to define technical requirements and ensure data compliance with expectations

Results

• Managed 500,000+ infrastructure connections (fibre optique, cables electrique et poteaux) with 98% accuracy
• Enhanced data quality by cross-validating with AutoCAD plans and technical specifications
• Consolidated and harmonized fiber optic, electrical cable, and pole data into a unified GIS database, improving accessibility and planning efficiency

Challenge

Engineering firm required rapid digitization of cadastral maps over drone images, ensuring high accuracy and seamless integration with existing GIS workflows

Strategy

• Performed accurate digitization from drone images, applying rigorous quality control (QA/QC) procedures
• Developed a QGIS plugin that applies multiple rule-based conditions simultaneously to bulk-update attributes, significantly improving data editing efficiency and accuracy for large spatial datasets
• Integrated Python scripts for batch processing of vector layers

Results

• Digitized over 1000+ municipalities
• Reduced processing time by 20% compared to fully manual workflows
• Achieved sub-meter spatial accuracy consistently
• Delivered a reusable and adaptable plugin to support future projects