Inference Overlay

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 that used a limited amount of proposal regions (RCNN). This Neural Network takes a subsection (window) of the input grid and either;

classifies the image as a whole
detect one or more objects in that image.

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 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.

Foliage Example

Animation of inference with a moving window and Bounding Box detection and a stride fraction of 0.5.

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.