Forensic Spatial Auditor

ADDON

📖6 min read

Latest Version: 1.4.0

Introduction

The Forensic Spatial Auditor is a professional metrology and forensic tool for Blender designed to quantify the Uncertainty Budget of spatial measurements derived from aerial satellite imagery.

Unlike standard measuring tools that provide a single absolute value, this auditor uses the Root Sum Square (RSS) method to calculate a statistically rigorous measurement result. It combines user-induced measurement variance with sensor-specific uncertainty (Ground Sample Distance) to provide a final audited result with clear confidence intervals. It serves investigators who report spatial measurements as evidence and must state how certain those measurements are.

Key Takeaways

Quantified Uncertainty

Moves beyond simple measurements to scientific results with confidence intervals (e.g. 10.04m ± 0.02m).

Standardized Data

Built-in tiers for Aerial Photography, Commercial Satellite, and Landsat/Sentinel data sources.

Methodology Export

One-click generation of research-ready text for technical reports and affidavits.

Installation

Download forensic_spatial_auditor.zip from the GitHub repo.
Open Blender and go to Edit > Preferences > Extensions.
Click the arrow icon next to the search bar and select Install from Disk...
Select forensic_spatial_auditor.zip and enable the addon.

Requires Blender 4.2 or later; Blender 5.0 is supported.

Usage

The workflow runs four steps in the Spatial Uncertainty Auditor panel (press N in the 3D Viewport → View tab).

Primary Observation Data· input

Click Add Trial to create input fields. Input multiple measurements of the same feature (e.g. the length of a skid mark). The tool automatically calculates the User Induced Uncertainty ( $u_o$ ) from your variance.

Sensor-Induced Uncertainty· source

Select your Source Data system: Aerial Photography (15–30cm GSD), Commercial Satellite (30–60cm), Older Satellite (1.0–2.5m), or Landsat/Sentinel (15–30m). Then choose a Conservatism Profile: Optimistic (best-case GSD), Balanced (average), or Defensive (worst-case; recommended for forensic auditing).

Combined Uncertainty Budget· calculate

Set your Coverage Factor ( $k$ ). The forensic standard is $k=2$ (95.4% confidence); $k=1$ (68.2%) and $k=3$ (99.7%) are also supported. Review the Audit Result: your mean measurement plus/minus the expanded uncertainty.

Export for Documentation· report

Click Copy for Methodology and paste the result directly into your research paper, affidavit, or technical report.

How It Works: The Statistical Logic

The Forensic Spatial Auditor measures a line and calculates the limits of what can be known about it based on the quality of your evidence.

1. Establishing the Bracket (Resolution Limits)

Every digital image is a grid of pixels. In satellite imagery, the size of these pixels on the ground is the Ground Sample Distance (GSD). If a resolution range is "15–30cm", every pixel represents an area between 15cm and 30cm wide.

This creates a "fog of uncertainty." When you click a point, you aren't clicking an exact coordinate; you are clicking somewhere within that pixel's area. You must decide how conservative to be about this "fog":

Optimistic (15cm GSD): Assumes the best quality. Point error ≈ 7.5cm.
Balanced (22.5cm GSD): Assumes average quality. Point error ≈ 11.25cm.
Defensive (30cm GSD): Assumes worst quality. Point error ≈ 15cm.

Total Image Uncertainty ( $\sigma_i$ ) is calculated using RSS on the start and end points of your line. For a defensive profile (30cm), independent errors at both ends result in a total image uncertainty of ±21.2cm.

2. User Variability (The Human Factor)

Even with a perfect image, human clicking is inconsistent. If you measure a wall five times, you will get five slightly different numbers. This is your User Induced Uncertainty ( $\sigma_{user}$ ).

The tool calculates the standard deviation of your trials. Because sample sizes in investigations are small (e.g. 5 measurements), it applies a Bessel Correction (dividing by $n-1$ instead of $n$ ). This slightly increases the uncertainty margin to account for the fact that a small sample size might miss extreme measurement errors, keeping the result "honest" and statistically defensible.

3. Root Sum Square (The Combination)

Finally, the tool combines the Image Uncertainty (Systematic) and User Uncertainty (Random). Because your shaky hand is independent of the satellite's resolution, adding them together would overestimate the error.

Instead, we use the Root Sum Square (RSS) formula:

$\sigma_{total} = \sqrt{\sigma_{image}^2 + \sigma_{user}^2}$

Often, you will find that human error adds very little to the total. If the image uncertainty is 21.2cm and you add 4cm of human error, the total only rises to ~21.6cm. This reveals the "physical floor" of the evidence.

4. Confidence Intervals (Sigmas)

The final result is reported with a confidence level:

1 Sigma ( $1\sigma$ ): ~68% confidence. "We are 68% sure the true value is in this range."
2 Sigma ( $2\sigma$ ): ~95.4% confidence. The forensic gold standard. "We are 95% sure..."
3 Sigma ( $3\sigma$ ): ~99.7% confidence. Nearly absolute certainty.

Technical Details

The tool employs the Root Sum Square (RSS) method to derive the final uncertainty ( $U$ ).

U = k \cdot \sqrt{u_{user}^2 + u_{sensor}^2}

Where:

$k$ is the coverage factor (Sigma level).
$u_{user}$ is the standard deviation derived from multiple user measurement trials.
$u_{sensor}$ is the error derived from the pixel pitch (GSD) or empirical Harrington rates.

Built-in Empirical Rates:

The tool includes Harrington Empirical Rates for specific forensic scenarios:

On-road analysis: 1.45% uncertainty rate.
Off-road analysis: 1.61% uncertainty rate.

In an Investigation

What to document when using Forensic Spatial Auditor

In Your Method Section

The Forensic Spatial Auditor aims to standardize mathematical reporting. When documenting your process, you should explicitly state the parameters used in the calculation:

Source Data Tier: (e.g. "Commercial Satellite (30-60cm GSD)")
Conservatism Profile: (e.g. "Defensive")
Coverage Factor: (e.g. " $k=2$ (95.4% confidence)")
Number of Trials: The number of independent measurements taken.

In Your Decision Log

Record the following:

Why a specific Source Data tier was chosen (e.g. "Based on Maxar imagery metadata identifying 50cm resolution").
The variance observed between measurement trials (User Induced Uncertainty).
Date performed.

Verification

To verify the tool's output, manually calculate the RSS formula from the displayed $u_{user}$ and $u_{sensor}$ values (see Technical Details).

Example Methodology Statement

"Spatial measurements were audited using the Forensic Spatial Auditor tool in Blender 4.2. Uncertainty was calculated using the Root Sum Square (RSS) method ( $k=2$ , 95.4% confidence interval). Based on commercial satellite imagery constraints (Defensive profile, approx. 60cm GSD) and user measurement variance across 5 trials, the final audited length is 14.2m ± 0.6m."

Limitations

Results depend on the source imagery resolution (GSD) and the conservatism profile you select. A defensive profile is recommended when the exact GSD is unknown.
User variability is only as good as your number of trials. Small sample sizes widen the uncertainty (Bessel-corrected).
The built-in GSD tiers are defaults. When your imagery metadata states the actual GSD, use that value instead of a tier.

Resources

Measurements, Scale & Dimension, the measurement technique this addon operationalizes
Precision Modeling, a related precision workflow

Sources

Forensic Spatial Auditor GitHub, source + download
Evaluating the Uncertainty in Various Measurement Tasks Common to Accident Reconstruction (SAE 2002), a related accident-reconstruction uncertainty study
Modeling and Visualizing the Positional Uncertainty of Remote Sensing Image (ISPRS Archives), the sensor-side positional-uncertainty theory
Forensic Photogrammetry: Measuring Images (ForensicSpot)

Introduction​

Key Takeaways​

Quantified Uncertainty

Standardized Data

Methodology Export

Installation​

Usage​

How It Works: The Statistical Logic​

1. Establishing the Bracket (Resolution Limits)​

2. User Variability (The Human Factor)​

3. Root Sum Square (The Combination)​

4. Confidence Intervals (Sigmas)​

Technical Details​

In an Investigation​

In Your Method Section​

In Your Decision Log​

Verification​

Example Methodology Statement​

Limitations​

Resources​

Sources​