Inference Overlay: Difference between revisions
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foliage_inference_scores.jpg|[[Scores result type (Inference Overlay)|Label Scores]] | foliage_inference_scores.jpg|[[Scores result type (Inference Overlay)|Label Scores]] | ||
foliage_inference_masks.jpg|[[Masks result type (Inference Overlay)|Pixel Masks]] | foliage_inference_masks.jpg|[[Masks result type (Inference Overlay)|Pixel Masks]] | ||
foliage_inference_boxes.jpg|[[ | foliage_inference_boxes.jpg|[[Boxes result type (Inference Overlay)|Bounding Boxes]] | ||
foliage_inference_foliage_height.jpg|[[DSM]] subtracted by [[DTM]] on identified features | foliage_inference_foliage_height.jpg|[[DSM]] subtracted by [[DTM]] on identified features | ||
foliage_inference_iterative_max_5_iterations_d0_25m.jpg|Max neighboring height within 0.25m, iterated 5 times | foliage_inference_iterative_max_5_iterations_d0_25m.jpg|Max neighboring height within 0.25m, iterated 5 times |
Revision as of 08:34, 16 October 2024
The AI Inference Overlay is a Grid Overlay which can spatially identify features using one or more Prequel Grids. Features are identified using a Convolution Neural Network. This Neural Network takes a subsection (window) of the input grid and either classifies or detects one or more objects in that window. The window of detection marches over the input grid with a configurable stride.
Foliage Example
Using a Satellite Overlay of 0.1m detail, foliage features can be identified using an Inference Overlay and enhanced with a Digital Terrain Model Overlay (DTM), a WCS Overlay representing the DSM, Combo Overlay to combine these and an optionally an iterative Max Overlay to enhance the foliage height. For more information, see this how-to.
Labeled features on 0.1m satellite image
How-to's
- How to detect foliage using an Inference Overlay
- How to create foliage height areas based on an Inference Overlay
- How to import trees based on an Inference Overlay
- How to use the Inference Overlay to detect and add solar panels to buildings based on a Satellite Overlay
See also