SchemasΒΆ

LIDAR sensors quickly produce millions of points with large numbers of variables measured on each point. The challenge for a point cloud database extension is efficiently storing this data while allowing high fidelity access to the many variables stored.

Much of the complexity in handling LIDAR comes from the need to deal with multiple variables per point. The variables captured by LIDAR sensors vary by sensor and capture process. Some data sets might contain only X/Y/Z values. Others will contain dozens of variables: X, Y, Z; intensity and return number; red, green, and blue values; return times; and many more. There is no consistency in how variables are stored: intensity might be stored in a 4-byte integer, or in a single byte; X/Y/Z might be doubles, or they might be scaled 4-byte integers.

Point Cloud deals with all this variability by using a “schema document” to describe the contents of any particular LIDAR point. Each point contains a number of dimensions, and each dimension can be of any data type, with scaling and/or offsets applied to move between the actual value and the value stored in the database. The schema document format used by Point Cloud is the same one used by the PDAL library.

Here is a simple 4-dimensional schema document you can insert into a database called pointcloud_formats to work with the examples below:

INSERT INTO pointcloud_formats (pcid, srid, schema) VALUES (1, 4326,
'<?xml version="1.0" encoding="UTF-8"?>
<pc:PointCloudSchema xmlns:pc="http://pointcloud.org/schemas/PC/1.1"
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <pc:dimension>
    <pc:position>1</pc:position>
    <pc:size>4</pc:size>
    <pc:description>X coordinate as a long integer. You must use the
                    scale and offset information of the header to
                    determine the double value.</pc:description>
    <pc:name>X</pc:name>
    <pc:interpretation>int32_t</pc:interpretation>
    <pc:scale>0.01</pc:scale>
  </pc:dimension>
  <pc:dimension>
    <pc:position>2</pc:position>
    <pc:size>4</pc:size>
    <pc:description>Y coordinate as a long integer. You must use the
                    scale and offset information of the header to
                    determine the double value.</pc:description>
    <pc:name>Y</pc:name>
    <pc:interpretation>int32_t</pc:interpretation>
    <pc:scale>0.01</pc:scale>
  </pc:dimension>
  <pc:dimension>
    <pc:position>3</pc:position>
    <pc:size>4</pc:size>
    <pc:description>Z coordinate as a long integer. You must use the
                    scale and offset information of the header to
                    determine the double value.</pc:description>
    <pc:name>Z</pc:name>
    <pc:interpretation>int32_t</pc:interpretation>
    <pc:scale>0.01</pc:scale>
  </pc:dimension>
  <pc:dimension>
    <pc:position>4</pc:position>
    <pc:size>2</pc:size>
    <pc:description>The intensity value is the integer representation
                    of the pulse return magnitude. This value is optional
                    and system specific. However, it should always be
                    included if available.</pc:description>
    <pc:name>Intensity</pc:name>
    <pc:interpretation>uint16_t</pc:interpretation>
    <pc:scale>1</pc:scale>
  </pc:dimension>
  <pc:metadata>
    <Metadata name="compression">dimensional</Metadata>
  </pc:metadata>
</pc:PointCloudSchema>');

Schema documents are stored in the pointcloud_formats table, along with a pcid or “point cloud identifier”. Rather than store the whole schema information with each database object, each object just has a pcid, which serves as a key to find the schema in pointcloud_formats. This is similar to the way the srid is resolved for spatial reference system support in PostGIS.

The central role of the schema document in interpreting the contents of a point cloud object means that care must be taken to ensure that the right pcid reference is being used in objects, and that it references a valid schema document in the pointcloud_formats table.