Inference Overlay - Revision history

Maxim@tygron.com: Undo revision 63569 by Maxim@tygron.com (talk)

2025-10-17T15:09:44Z

Undo revision 63569 by Maxim@tygron.com (talk)

← Older revision		Revision as of 15:09, 17 October 2025
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	* [[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]]
	* [[How to train your own AI model for an Inference Overlay]]		* [[How to train your own AI model for an Inference Overlay]]
	* [[How to ~~detect Trees in private yards using an~~ Inference ~~Overlay~~]]		* [[How to select specific input data for AI Inference]]
	* [[How to create AI train data with QGIS]]		* [[How to create AI train data with QGIS]]
	}}		}}
	{{InferenceOverlay nav}}		{{InferenceOverlay nav}}
	{{Overlay nav}}		{{Overlay nav}}

Maxim@tygron.com at 15:08, 17 October 2025

2025-10-17T15:08:34Z

← Older revision		Revision as of 15:08, 17 October 2025
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	* [[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]]
	* [[How to train your own AI model for an Inference Overlay]]		* [[How to train your own AI model for an Inference Overlay]]
	* [[How to ~~select specific input data for AI~~ Inference]]		* [[How to detect Trees in private yards using an Inference Overlay]]
	* [[How to create AI train data with QGIS]]		* [[How to create AI train data with QGIS]]
	}}		}}
	{{InferenceOverlay nav}}		{{InferenceOverlay nav}}
	{{Overlay nav}}		{{Overlay nav}}

Maxim@tygron.com: Reverted edits by Maxim@tygron.com (talk) to last revision by Frank@tygron.nl

2025-10-17T15:07:46Z

Reverted edits by Maxim@tygron.com (talk) to last revision by Frank@tygron.nl

← Older revision		Revision as of 15:07, 17 October 2025
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	It is not possible to run the convolution network on the grid as a whole. Therefore, a marching window strategy is applied instead, where the window is moved over the input grid with a [[Stride fraction (Inference Overlay)\|configurable stride]]. This window takes the input values of one or more grids and puts these into tensors representing either floating point values or color channels (red, green and blue). Depending on the dataset used to train the neural network, the tensor values are normalized based on a particular value range. For example, a relative height input tensor for a foliage neural network trained on height data can be normalized using a range of 0 to 40 meters, when 40 meters is considered the largest tree height in the Netherlands.		It is not possible to run the convolution network on the grid as a whole. Therefore, a marching window strategy is applied instead, where the window is moved over the input grid with a [[Stride fraction (Inference Overlay)\|configurable stride]]. This window takes the input values of one or more grids and puts these into tensors representing either floating point values or color channels (red, green and blue). Depending on the dataset used to train the neural network, the tensor values are normalized based on a particular value range. For example, a relative height input tensor for a foliage neural network trained on height data can be normalized using a range of 0 to 40 meters, when 40 meters is considered the largest tree height in the Netherlands.

	~~The {{software}} contains several trained models, such as the foliage example explained below. However it is also possible to train your own model with the [[AI Suite]].~~

	==Foliage Example==		==Foliage Example==
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	{{article end		{{article end
	\|seealso=		\|seealso=
	* [[AI Suite\|AI Suite (for more information on training your own model)]]
	* [[Model attributes (Inference Overlay)]]		* [[Model attributes (Inference Overlay)]]
	* [[Neural Network]]		* [[Neural Network]]
Line 33:		Line 30:
	* [[How to import trees 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 detect solar panels using an Inference Overlay]]
	* [[How to ~~detect Trees in private yards~~ using an Inference Overlay]]		* [[How to create the input overlay for leafless trees detection using the Inference Overlay]]
			* [[How to train your own AI model for an Inference Overlay]]
	* [[How to select specific input data for AI Inference]]		* [[How to select specific input data for AI Inference]]
	* [[How to ~~evaluate an~~ AI ~~model~~]]		* [[How to create AI train data with QGIS]]
	}}		}}
	{{InferenceOverlay nav}}		{{InferenceOverlay nav}}
	{{Overlay nav}}		{{Overlay nav}}

Maxim@tygron.com at 15:05, 17 October 2025

2025-10-17T15:05:20Z

← Older revision		Revision as of 15:05, 17 October 2025
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	* [[How to import trees 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 detect solar panels using an Inference Overlay]]
	* [[How to ~~create the input overlay for leafless trees detection~~ using ~~the~~ Inference Overlay]]		* [[How to detect Trees in private yards using an Inference Overlay]]
	* [[How to select specific input data for AI Inference]]		* [[How to select specific input data for AI Inference]]
	* [[How to evaluate an AI model]]		* [[How to evaluate an AI model]]

Maxim@tygron.com at 10:33, 16 October 2025

2025-10-16T10:33:11Z

← Older revision		Revision as of 10:33, 16 October 2025
Line 5:		Line 5:

	It is not possible to run the convolution network on the grid as a whole. Therefore, a marching window strategy is applied instead, where the window is moved over the input grid with a [[Stride fraction (Inference Overlay)\|configurable stride]]. This window takes the input values of one or more grids and puts these into tensors representing either floating point values or color channels (red, green and blue). Depending on the dataset used to train the neural network, the tensor values are normalized based on a particular value range. For example, a relative height input tensor for a foliage neural network trained on height data can be normalized using a range of 0 to 40 meters, when 40 meters is considered the largest tree height in the Netherlands.		It is not possible to run the convolution network on the grid as a whole. Therefore, a marching window strategy is applied instead, where the window is moved over the input grid with a [[Stride fraction (Inference Overlay)\|configurable stride]]. This window takes the input values of one or more grids and puts these into tensors representing either floating point values or color channels (red, green and blue). Depending on the dataset used to train the neural network, the tensor values are normalized based on a particular value range. For example, a relative height input tensor for a foliage neural network trained on height data can be normalized using a range of 0 to 40 meters, when 40 meters is considered the largest tree height in the Netherlands.

			The {{software}} contains several trained models, such as the foliage example explained below. However it is also possible to train your own model with the [[AI Suite]].

	==Foliage Example==		==Foliage Example==

Maxim@tygron.com at 10:30, 16 October 2025

2025-10-16T10:30:22Z

← Older revision		Revision as of 10:30, 16 October 2025
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	{{article end		{{article end
	\|seealso=		\|seealso=
	* [[AI Suite\|\|AI Suite (for more information on training your own model)]]		* [[AI Suite\|AI Suite (for more information on training your own model)]]
	* [[Model attributes (Inference Overlay)]]		* [[Model attributes (Inference Overlay)]]
	* [[Neural Network]]		* [[Neural Network]]

Maxim@tygron.com at 10:30, 16 October 2025

2025-10-16T10:30:03Z

← Older revision		Revision as of 10:30, 16 October 2025
Line 20:		Line 20:
	{{article end		{{article end
	\|seealso=		\|seealso=
	* [[AI Suite]]		* [[AI Suite\|\|AI Suite (for more information on training your own model)]]
	* [[Model attributes (Inference Overlay)]]		* [[Model attributes (Inference Overlay)]]
	* [[Neural Network]]		* [[Neural Network]]
Line 32:		Line 32:
	* [[How to detect solar panels using an Inference Overlay]]		* [[How to detect solar panels using 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]]
	* [[How to train your own AI model for an Inference Overlay]]
	* [[How to select specific input data for AI Inference]]		* [[How to select specific input data for AI Inference]]
	* [[How to create AI train data with QGIS]]
	* [[How to evaluate an AI model]]		* [[How to evaluate an AI model]]
	}}		}}
	{{InferenceOverlay nav}}		{{InferenceOverlay nav}}
	{{Overlay nav}}		{{Overlay nav}}

Maxim@tygron.com at 08:48, 15 October 2025

2025-10-15T08:48:53Z

← Older revision		Revision as of 08:48, 15 October 2025
Line 20:		Line 20:
	{{article end		{{article end
	\|seealso=		\|seealso=
			* [[AI Suite]]
	* [[Model attributes (Inference Overlay)]]		* [[Model attributes (Inference Overlay)]]
	* [[Neural Network]]		* [[Neural Network]]

Maxim@tygron.com at 08:48, 15 October 2025

2025-10-15T08:48:00Z

← Older revision		Revision as of 08:48, 15 October 2025
Line 34:		Line 34:
	* [[How to select specific input data for AI Inference]]		* [[How to select specific input data for AI Inference]]
	* [[How to create AI train data with QGIS]]		* [[How to create AI train data with QGIS]]
	* [[How to ~~validate~~ an AI model]]		* [[How to evaluate an AI model]]
	}}		}}
	{{InferenceOverlay nav}}		{{InferenceOverlay nav}}
	{{Overlay nav}}		{{Overlay nav}}

Maxim@tygron.com at 08:16, 15 October 2025

2025-10-15T08:16:16Z

← Older revision		Revision as of 08:16, 15 October 2025
Line 34:		Line 34:
	* [[How to select specific input data for AI Inference]]		* [[How to select specific input data for AI Inference]]
	* [[How to create AI train data with QGIS]]		* [[How to create AI train data with QGIS]]
			* [[How to validate an AI model]]
	}}		}}
	{{InferenceOverlay nav}}		{{InferenceOverlay nav}}
	{{Overlay nav}}		{{Overlay nav}}