Inference Overlay: Difference between revisions
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The Inference Overlay is a [[Grid Overlay]] which can spatially identify features using one or more Prequel Grids. Features are identified using a [[Neural Network]]. This | [[File:Treesmask.jpg|thumb|right|Trees detected with Inference Overlay.]] | ||
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 [[Stride fraction (Inference Overlay)|configurable stride]]. | |||
==Foliage Example== | |||
[[File:inference_foliage_animated.gif|frame|right|Animation of inference with a moving window and Bounding Box detection.]] | |||
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|WCS Overlay]] representing the [[DSM]], [[Combo Overlay]] to combine these and an optionally an iterative [[Avg & interpolation (overlay)|Max Overlay]] to enhance the foliage height. For more information, see this [[How to detect foliage using an Inference Overlay|how-to]]. | |||
<gallery widths="200"> | |||
foliage_inference_labels.jpg|[[Labels result type (Inference Overlay)|Labeled features]] on 0.1m satellite image | |||
foliage_inference_scores.jpg|[[Scores result type (Inference Overlay)|Label Scores]] | |||
foliage_inference_masks.jpg|[[Masks result type (Inference Overlay)|Pixel Masks]] | |||
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_iterative_max_5_iterations_d0_25m.jpg|Max neighboring height within 0.25m, iterated 5 times | |||
</gallery> | |||
{{article end | {{article end | ||
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* [[Neural Network]] | * [[Neural Network]] | ||
* [[ONNX]] | * [[ONNX]] | ||
* [[PyTorch]] | |||
|howtos= | |||
* [[How to detect foliage using an Inference Overlay]] | |||
* [[How to create foliage height based on an Inference Overlay]] | |||
* [[How to import trees based on an Inference Overlay]] | |||
* [[How to detect solar panels using an Inference Overlay]] | |||
* [[How to update Buildings's solar panel attribute based on an Inference Overlay]] | |||
}} | }} | ||
{{InferenceOverlay nav}} | |||
{{Overlay nav}} | {{Overlay nav}} | ||
Latest revision as of 16:29, 7 November 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
Max neighboring height within 0.25m, iterated 5 times
How-to's
- How to detect foliage using an Inference Overlay
- How to create foliage height based on an Inference Overlay
- How to import trees based on an Inference Overlay
- How to detect solar panels using an Inference Overlay
- How to update Buildings's solar panel attribute based on an Inference Overlay
See also
