Photogrammetry

Project Objective

The goal of this project is to demonstrate my skills & ability to generate actionable insights through aerial photogrammetry. I will present a portfolio of photogrammetry projects that showcase:

My commitment to safety protocols, mission planning, data organization, and efficient processing workflows to achieve the best possible results with the available resources and equipment.
My approach to developing a reality capture workflow and resulting data that is dependable, defensible, repeatable, consistent, and accurate.

I used a drone to capture data that is integrated into a reality capture workflow, creating orthomosaic maps, digital surface models (DSMs), digital terrain models (DTMs), point clouds (sparse and dense), and textured meshes.

The goal is not simply to create outputs but to interpret the findings to enhance business decisions in construction, design, architecture, or land-use business intelligence.

This project aims to show that implementing an intentional workflow, focusing on environmental conditions, camera control, mission planning, and data organization, allowed me to produce relatively accurate models, visually detailed progress photos, aggregate stockpile measurements, and general project verification.

Capture Methodology

In this project, I used a consumer-grade DJI Mini 4 Pro, which lacks RTK functionality or GNSS corrections. In addition, I did not have access to, nor did I employ, ground control points or checkpoints for coordinate correction and model alignment.

Without coordinate correction, it’s understood that true absolute accuracy is unattainable and only relative accuracy can be measured. To test the relative accuracy of my projects, I verified output accuracy using ground-based measurements to compare aerial photogrammetric results across missions with different capture methods and quantify deviations from reality.

All of these exercises and projects are intended for educational purposes and demonstration, and they are not intended for:

Engineering design
Construction layout
Survey-grade GIS analysis
Legal boundary documentation

Mission Planning Methodology

I used the DJI Fly app to create missions. The DJI Fly map allows you to create waypoint missions but lacks the enterprise-grade features needed for automated, complex mission planning. To achieve the desired ground sampling distance (GSD) and image quality, I manually calculated grid spacing, photo intervals, altitude, and flight speed, and physically created missions waypoint by waypoint to ensure sufficient coverage and detail.

I conducted a walk-through of each site prior to flying the mission. During the scout mission, I would inspect:

A safe and unobstructed launch zone that does not create conflict with traffic or any right-of-way
Emergency landing zones
Vertical obstacles like power lines and trees
Potential pedestrian and vehicle traffic, including homeless people
Surrounding elements that could present issues (nearby helipad, construction sites, bird nests)
Visual obstructions that could compromise VLOS
Airspace, weather, lighting, extraneous noise
Private property adjacent to the mapping site
Any upcoming special events in the area that conflict with the scheduled mission
Any local regulations that affect or prohibit the mapping mission

All elements discovered during the scouting mission are included in the mission plan, which is integrated with information obtained through:

DJI Fly
AirData UAV
FlightRadar24
Aloft AirControl
Windy

Processing Methodology

I used WebODM to process my image captures to create orthomosaic maps, digital surface models (DSMs), digital terrain models (DTMs), point clouds (sparse and dense), and textured meshes. Native 8064 × 6048-pixel images were resized to 6048 × 4536 pixels to facilitate processing. WebODM generated in-depth processing reports detailing reconstruction statistics, feature counts, reprojection error, dense point counts, and accuracy metrics.

Operational Safety Assessment

Risk Evaluation

Hazard	Mitigation
Airspace and TFRs	Confirmed TFRs and airspace restrictions prior to the mission using AirData UAV. Monitoring nearby or incoming manned aircraft during the mission with FlightRadar24
Pedestrians & Vehicle traffic	Selected a launch area away from pedestrian and vehicle traffic while maintaining VLOS and situational awareness throughout the mission. Maintained a 100’ buffer from the adjacent freeway.
Trees & Light Poles	Planned waypoint spacing during mission planning to maintain safe horizontal and vertical obstacle clearance. Verified obstacle clearance prior to the mission flight.
Weather	Weather monitored within 30 minutes prior to and during the mission
Birds	Maintained situational awareness with VLOS and prepared an emergency avoidance and landing procedure to evade birds and terminate the flight if necessary.

Operational Readiness

Conduct aircraft inspection
Battery health verified
GNSS lock established
Weather evaluated
Home point confirmed
Return-to-Home altitude verified
Emergency landing area identified
Conduct Hover Control Test prior to flight
Visual Line of Sight(VLOS) maintained throughout operation

Training Flights

This portfolio project will consist of 4 separate sites, each with 3-4 missions. The purpose of each mission is to test specific skills, variables, and/or settings applicable to that site.

Overall learning framework

Capture variables

Altitude
Overlap
Camera Angle
Flight Pattern
Lighting
Environmental Conditions
Repeatability

Processing outcomes

Alignment Quality
Orthomosaic Distortion
Point Cloud Density
Mesh Completeness
Façade Quality
Vegetation Noise
Edge Warping
Reconstruction Confidence

QA thinking

Checkpoint Comparisons
Repeatability
Visible Artifacts
Error Explanation
Corrective Action

Sites

Flight 3B: 150’ Altitude Nadir | 90º Crosshatch Grid

Flight 3C: Follow Terrain | 120’ Altitude

Lessons Learned from Project

Mission Planning
Mission & Waypoint Creation
Pre-flight Operational and Safety Check
On-site situational awareness (Safety)
Contingency plan (People traffic, birds, curious inquirers)
Organizational skills (Flight log data, imagery, mission requirements, model reconstruction, reporting)
Data translation
Anticipation of necessary outputs when planning and obtaining imagery
Memory settings needed to build reconstruction outputs
Relative and absolute accuracy
How to manually create a waypoint mission that would provide adequate coverage (it took 3 tries)
Flying the mission in a manner and time to avoid people (required 2 mission abortions and restarts)
Established fight planning, flight logging, and processing workflow
How to read and interpret WebODM results, models, and use measuring tools
Objective hazards that are present when mapping a mission (people on site, birds flying in the area)
The lack of vertical detail when only capturing nadir imagery
Absolute and relative inaccuracies when only using consumer GNSS
Computer memory and processing settings are needed to build a high-resolution reconstruction