Foliage (Neural Network): Difference between revisions
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The Foliage Neural Network is a | The Foliage Neural Network is a [[Region-based Convolutional Neural Network (Inference Overlay)|Region-based Convolutional Neural Network (RCNN)]] that identifies foliage of individual trees and bushes, mainly for gardens and private property. This Neural Network is not suited for identifying individual trees within forested areas. | ||
An [[Inference Overlay]] can be configured with this Neural Network. Its default settings are: | An [[Inference Overlay]] can be configured with this Neural Network. Its default settings are: | ||
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{{article end | {{article end | ||
|notes=*In order to use the identified foliage areas as input for a Heat Overlay, additional steps have to be taken to obtain an actual foliage height | |notes= | ||
*In order to use the identified foliage areas as input for a Heat Overlay, [[How to create foliage height areas based on an Inference Overlay|additional steps have to be taken to obtain an actual foliage height]]. | |||
|howtos= | |howtos= | ||
*[[How to detect foliage using an Inference Overlay]] | *[[How to detect foliage using an Inference Overlay]] | ||
*[[How to create foliage height areas based on an Inference Overlay]] | *[[How to create foliage height areas based on an Inference Overlay]] | ||
*[[How to create the input overlay for leafless trees detection using the Inference Overlay]] | *[[How to create the input overlay for leafless trees detection using the Inference Overlay]] | ||
|seealso=*[[Inference Overlay]] | |seealso= | ||
*[[Neural Network]] | *[[Inference Overlay]] | ||
*[[Region-based Convolutional Neural Network (Inference Overlay)|RCNN]] | |||
*[[ONNX]] | *[[ONNX]] | ||
*[[Foliage areas (Heat Overlay)]] | *[[Foliage areas (Heat Overlay)]] | ||
}} | }} | ||
{{InferenceOverlay nav}} | {{InferenceOverlay nav}} | ||
Latest revision as of 07:30, 20 October 2026
The Foliage Neural Network is a Region-based Convolutional Neural Network (RCNN) that identifies foliage of individual trees and bushes, mainly for gardens and private property. This Neural Network is not suited for identifying individual trees within forested areas.
An Inference Overlay can be configured with this Neural Network. Its default settings are:
- Preferred grid cell size: 0.25m
- Inference mode: BBox Detection
- Mask threshold: 0.30 (30%)
- Score threshold: 0.20 (20%)
- Stride fraction: 0.50 (50%)
Identifiable features:
- Deciduous Tree
- Pine Tree
- Heath area
- Hedge
- Bush
- Reed
- Flower bed
- Leafless Tree Area
-
Labeled features on 0.25m satellite image -
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-
Notes
- In order to use the identified foliage areas as input for a Heat Overlay, additional steps have to be taken to obtain an actual foliage height.
How-to's
- How to detect foliage using an Inference Overlay
- How to create foliage height areas based on an Inference Overlay
- How to create the input overlay for leafless trees detection using the Inference Overlay