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.

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

• 

  Label Scores

• 

  Pixel Masks

• 

  Bounding Boxes

• 

  DSM subtracted by DTM on identified features

• 

  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

• Model attributes (Inference Overlay)
• Neural Network
• ONNX
• PyTorch

---

Inference Overlay

Inference Overlay

Tutorials

Overview

Theory

Benchmarks

Group

Model attributes

Inference

Neural Network • Input tensor • Output tensor

Model attributes

Box overlap • Inference mode • Mask threshold • Prioritize labels • Score threshold • Stride fraction

Prequels

A • B

Result types

Boxes • Labels • Masks • Scores • Debug gt instances • Center points

Neural Networks

Foliage • Solar panels

---

Overlays

Categorization

Grid • Parent • Result Child • Result Parent • Result type

Default

City

Administrative

Ownership • Ownership Grid • Zipcodes • Zoning • Neighborhoods • Municipalities • Vacancy

Environmental

Heat Stress • Traffic Density • Traffic Noise • Traffic NO2 • Livability

Water

Rainfall • Flooding • Groundwater • Subsidence • Watershed

Grid Calculation

Average • Combo • Distance • Sight Distance • Travel Distance • Inference • Iteration

Geo Data

GeoTIFF • WCS • WMS • Image • Area • Attribute • Function

Topography

Satellite • Digital Surface Model • Digital Terrain Model • Underground • Source

Network

Network Distance • Network Overview • Network Ownership