SpatialScale offers a range of data processing jobs to help you manipulate and analyze point cloud, raster, and vector data. Below is a detailed overview of the available jobs and their functionalities.
Point Clouds
Point Cloud Classification
This module uses deep learning to classify LiDAR point clouds into ground, building, and vegetation classes. If the point density is equal to or exceeds 20 points per square meter (ppm), the module will attempt to classify wire lines as well.
Input
Dataset Name: The name of the dataset containing the LiDAR point cloud data.
Output
Classified Point Cloud Dataset: The output dataset classifies points into noise, ground, and vegetation classes by default.
Change Distance
Conduct a change detection analysis between two point clouds using cloud-to-cloud distance. A new column is added to the result point cloud with the change distance. The distance of the reference (old) point cloud from the comparison (new) point cloud is negative and comparison (new) point cloud from the reference (old) point cloud is positive.
Colorize Points
Apply colors to point clouds using input values from a raster dataset.
Digital Elevation Model (DEM)
Create Digital Surface Model (DSM) or Digital Terrain Model (DTM) from points.
Tree Detection and Segmentation
Individual tree detection (ITD) is the process of spatially locating trees and extracting height information. Individual tree segmentation (ITS) is the process of individually delineating detected trees.
Inputs
Trees Dataset Name: The name of the dataset containing tree data.
Crowns Dataset Name: The name of the dataset containing crown data.
Minimum Tree Height: The minimum height threshold for tree detection.
Outputs
Trees: A vector dataset representing the detected individual trees.
Crowns: A vector dataset representing the delineated crowns of the detected trees
Vertical Differencing
This module allows users to analyze changes in elevation over time. It is particularly useful for detecting geomorphic changes and sediment movement.
Inputs
Current Dataset: The more recent point cloud.
Previous Dataset: The older point cloud.
Minimum Change Distance: The minimum distance for which changes should be considered.
Elevation Model Type: Choose between Digital Terrain Model (DTM) or Digital Surface Model (DSM).
Resolution: Specify the resolution for the analysis.
Output Dataset Name: The name of the dataset to be created.
Output
Differenced Raster Dataset: A raster dataset representing the elevation differences between the two source datasets.
Applications
Geomorphic Change Detection: Identify areas of erosion and deposition.
Sediment Volume Estimation: Calculate sediment movement volumes.
Flood Impact Analysis: Assess changes due to flooding events.
Rasters
Aspect
Generate an aspect raster with pixel values indicating azimuth from any DTM/DSM image.
Hillshade
Create shaded relief dataset from any DTM/DSM image.
Surface Roughness
Create surface roughness dataset from any DTM/DSM image.
Slope
Generate slope dataset from any DTM/DSM image.
Terrain Ruggedness Index (TRI)
Create a TRI raster dataset from any DTM/DSM image.
Topographic Position Index (TPI)
Generate TPI raster dataset from any DTM/DSM image.
Vertical Differencing
This module allows users to analyze changes in elevation over time. It is particularly useful for detecting geomorphic changes and sediment movement.
Inputs
Current Dataset: The more recent Digital Elevation Model (DEM).
Previous Dataset: The older Digital Elevation Model (DEM).
Minimum Change Distance: The minimum distance for which changes should be considered.
Output Dataset Name: The name of the dataset to be created.
Output
Differenced Raster Dataset: A raster dataset representing the elevation differences between the two DEMs.
Applications
Geomorphic Change Detection: Identify areas of erosion and deposition.
Sediment Volume Estimation: Calculate sediment movement volumes.
Flood Impact Analysis: Assess changes due to flooding events.
Vector
Point to Hex
Generate a hex grid based on point shapes.