Configure Landmark Detection

Landmark detection is a special type of object detection that specifically detects famous landmarks such as iconic architectures, historic artifacts, or natural structures.
The landmark detection block can return the detected landmark entity description, the bounding box, and the location coordinates.
Image credit: Nikolay Vorobyev on Unsplash (annotations added).
This block uses the Detect Landmarks Google Cloud Vision API. Responses are returned in Plainsight format. See the docs for more information:
Private cloud users must activate the Google Cloud Vision API in order to use this block.

Adding a Landmark Detection Block

To add a landmark detection block to your pipeline:
  1. 1.
    Navigate to the "Pipeline Blocks" tab.
  2. 2.
    Under the Prediction section, click on the Landmark Detection box to configure this processing block. You may need to click "Show All" or use the search box to find the block.
  3. 3.
    Enter the desired Result Limit between 1-50. This will limit the number of results returned per image for this block.
  4. 4.
    Click "Add to Pipeline" to add the block to your pipeline. This will bring you back to the "Pipeline Blocks" tab. From there you can add additional blocks and drag and drop to rearrange around other blocks as needed.
Image Sizing Tips
Recommended image size for landmark detection: 640 x 480
Vision API requires images to be a sufficient size so that important features within the request can be easily distinguished. Sizes smaller or larger than these recommended sizes may work. However, smaller sizes may result in lower accuracy, while larger sizes may increase processing time and bandwidth usage without providing comparable benefits in accuracy.
Image file size should not exceed 20 MB.

Example Output

"type": "rectangle",
"children": {
"description": {
"type": "text",
"children": {},
"data": "Saint Basil's Cathedral"
"latitude": {
"type": "regression",
"children": {},
"data": 55.752912
"longitude": {
"type": "regression",
"children": {},
"data": 37.622315883636475
"data": {
"min": [0.32075, 0.24206656346749225],
"max": [0.78, 0.6547987616099071]
"score": 0.8271462917327881