5. Detection Workflows

AeroMegh Intelligence spatial analytics software enables you to extract insights from aerial images using powerful AI-based detection workflows. These workflows help automate complex geospatial tasks such as object detection, land classification, and change analysis across time.

This chapter explains how to prepare your data, apply different detection workflows, view results, and export reports. Whether you’re working on a single image or a large project, the system guides you step-by-step for accurate, scalable, and repeatable results.

1. Universal Steps

Before running any detection workflow, ensure the following preparation steps are complete. These actions are shared across all workflow types.

For step-by-step instructions, refer to the linked chapters below.

Universal Preparation Checklist

1. Upload TIFF Images

    • Add .tiff format images to the project via the ➕ Add Image button.
      📖 See: Uploading Images (from “Project Workspace > Images Section” – add TIFF images)

2. Upload DSM/DTM Files (Optional)

    • Useful for terrain-aware analysis.
      📖 See: Uploading DSM/DTM (from “Measurement Tools” – add DSM and DTM files in Image View)

3. Draw Detection Area

  • Mark the region of interest using the Detection Area tool.
    📖 See: Detection Area Tool (from “Image View Tools” – define area for detection)


4. Create Annotation Classes

  • Define one or more classes (e.g., Tree, Road, Building).
    📖 See: Managing Classes (from “Image View Tools” – create, rename, delete classes)

5. Add Annotations
Annotate objects using tools like Polygon, Rectangle, Circle, or Polyline.

  • At least 3 annotations must be added per Training and Accuracy area for object detection on gis.
  • If using an Object Detection detector, you must annotate at least 20 objects in total across Training, Testing, and Accuracy areas combined.
  • If using a Classification detector, you must annotate at least 10 regions across Training, Testing, and Accuracy areas combined.

📖 See: Drawing Annotations (from “Image View Tools” – using annotation tools for detection training)

6. Assign and Train a Detector
Assign an Object, Classification, or Change Detection model to the image/project.

  • Detector status must be Completed to run detection.
    📖 See: Assigning & Training Detectors (from “Training Detectors” – assign, train, and validate detectors)

💡 Once these steps are complete, you’re ready to run detection workflows.

2. Object Detection Workflow

Use Object Detection to automatically identify and locate specific objects in the detection area, such as trees, pits, or electric poles.

How to Run Object Detection

  1. Open the project and navigate to the Images Section for object detection on gis.
  2. In the row of your image, click Detect under the Actions column.
  3. Choose a trained Object Detection detector.
  4. Click Run Detection.

The system processes the image and displays overlaid object detections.

Viewing Object Detection Results

  • Click the Reports link in the Actions column for the image.
  • This opens the result view showing:
    • Colour-coded detection overlays
    • Count of detected objects by class
    • Accuracy (if trained with evaluation zones)
    • Export options

Available Export Formats:

  • GeoJSON
  • KML
  • SHP

📖 See: Reports Link in Image Row (from “Project Workspace > Images Section” – access detection reports)

3. Classification Workflow

Classification for spatial analysis arcmap assigns sub-classes to detected objects, such as different vegetation types, road materials, or utility components.

You must run Object Detection first before Classification.

 How to Run Classification

  1. Go to the Images Section.
  2. Click Detect for the image where Object Detection has already run.
  3. Select a trained Classification Detector.
  4. Click Run Classification.

The system updates the detected objects with sub-class predictions.

 Viewing Classification Results

  • Click the Reports link in the image row.
  • The Object Detection report is updated with sub-class labels.

 No separate report is created for classification.

📖 See: Reports Link in Image Row (from “Project Workspace > Images Section” – access updated results)

4. Change Detection Workflow

Use this spatial analytics software workflow to compare two time-separated aerial images of the same location and highlight what has changed — e.g., new construction, vegetation loss.

 Requirements

  • Two TIFF images of the same area:
    ➤ One designated as Base (earlier)
    ➤ One as Secondary (later)
  • The Secondary image must be annotated and trained.
  • Change Detection is triggered from the Secondary image, but assigned to the Base.

How to Run Change Detection

  1. Open the Secondary image in Image View.
  2. Click the Change Detection tool from the left toolbar.
  3. Select the Base image.
  4. Choose a trained Change Detection Detector.
  5. Click Run Change Detection.

 Viewing Change Detection Results

  • Results appear in a split view within the Secondary image.
  • Visual overlays show:
    • Red zones = Removed features
    • Green zones = Added features

⚠️ Change Detection results for object detection on gis are visual only — no downloadable report is generated.

📖 See: Change Detection Interface (from “Image View Tools” – view time-based changes using Base & Secondary pairing)

5. Viewing Detection Results and Reports

Detection results for spatial analysis arcmap are accessed from the Images Section, not a Reports tab.

 How to Access Reports

  1. Navigate to your Project > Images Section.
  2. In the relevant image row, click the Reports link under the Actions column.

This opens a report view with:

  • Detected classes and object count
  • Confidence metrics (if available)
  • Export options

Formats:

  • GeoJSON
  • KML
  • SHP

📖 See: Reports Link in Image Row (from “Project Workspace > Images Section” – access image-specific results)

6. Best Practices for Detection

Best Practice Description
Use high-quality TIFFs Enhances accuracy and model learning
Keep annotations clear Avoid overlaps, duplications, and unclear shapes
Follow minimum annotation rule At least 3 per area, 20+ total recommended
Use correct detector type Match Object, Classification, or Change use case
Complete training before use Detector status must be “Completed”

6.Troubleshooting Common Issues

IssuePossible CauseRecommended Fix
Detect button disabledDetector not assigned/trainedAssign and train the correct model
No detection resultsToo few or invalid annotationsEnsure valid annotations and class assignment
Change results blankImages misaligned or unmatchedUse consistent resolution/coverage
Missing report linkDetection not completedRun detection and refresh the project view
7. Workflow Checklist
 
Task Status
Uploaded TIFF image ☐
Drew detection area ☐
Created annotation classes ☐
Added required annotations ☐
Assigned and trained detector ☐
Ran detection on image ☐
Viewed/exported detection report ☐

Related Sections

  • Uploading Images (from “Project Workspace > Images Section” – TIFF image upload process)
  • Drawing Detection Area (from “Image View Tools” – marking the analysis region)
  • Assigning & Training Detectors (from “Training Detectors” – manage and complete model training)
  • Exporting Results (from “Reports View” – download GeoJSON, KML, SHP)
  • Change Detection Interface (from “Image View” – side-by-side visual change comparison)
Table of Contents
whatsapp
Scroll to Top