Foliage (Neural Network): Difference between revisions
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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: | ||
:Preferred [[grid cell size]]: 0. | :Preferred [[grid cell size]]: 0.25m | ||
:[[Inference mode (Inference Overlay)|Inference mode]]: [[Inference mode (Inference Overlay)|BBox Detection]] | :[[Inference mode (Inference Overlay)|Inference mode]]: [[Inference mode (Inference Overlay)|BBox Detection]] | ||
:[[Mask threshold (Inference Overlay)|Mask threshold]]: | :[[Mask threshold (Inference Overlay)|Mask threshold]]: 0.30 (30%) | ||
:[[Score threshold (Inference Overlay)|Score threshold]]: | :[[Score threshold (Inference Overlay)|Score threshold]]: 0.20 (20%) | ||
:[[Stride fraction (Inference Overlay)|Stride fraction]]: 0.50 (50%) | :[[Stride fraction (Inference Overlay)|Stride fraction]]: 0.50 (50%) | ||
Identifiable features: | Identifiable features: | ||
# | # Deciduous Tree | ||
# Pine Tree | |||
# Heath area | |||
# Hedge | |||
# Bush | |||
# Reed | |||
# Flower bed | |||
# Leafless Tree Area | |||
<gallery widths="200"> | <gallery widths="200"> | ||
foliage_inference_labels.jpg|[[Labels result type (Inference Overlay)|Labeled features]] on 0. | foliage_inference_labels.jpg|[[Labels result type (Inference Overlay)|Labeled features]] on 0.25m satellite image | ||
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]] | ||
| Line 19: | Line 26: | ||
{{article end | {{article end | ||
|notes=* | |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. For more detail, see the how-to's. | ||
|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]] | |||
|seealso=*[[Inference Overlay]] | |seealso=*[[Inference Overlay]] | ||
*[[Neural Network]] | |||
*[[ONNX]] | |||
*[[Foliage areas (Heat Overlay)]] | *[[Foliage areas (Heat Overlay)]] | ||
}} | }} | ||
{{InferenceOverlay nav}} | |||
Latest revision as of 12:59, 14 October 2025
The Foliage Neural Network is a Convolution Neural Network 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 -
-
-
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. For more detail, see the how-to's.
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
