Will Point Clouds Replace the Geodatabase?

Since attending the Spar3D conference, I've been pondering this question:

Will point clouds (eventually) replace the geodatabase?

What is a Point Cloud?

A point cloud is a collection of points which have an x, y and z value. I am now immersed in the world of digital reality, and point clouds form one of the key data sources for this 3D mapping environment. Point clouds can be generated by LiDAR or traditional remote sensing data.

LiDAR is at the centre of much of the ongoing research into autonomous vehicles. Money has been pouring into LiDAR technology. That has led to significant advances. The cost of LiDAR continues to fall.

LiDAR send and receive focused laser pulses. The time it takes for a beam to be sent and received is a measure of distance. Software can take that data and build a 3D scene. Today's LiDAR scanners send out up to 150,000 pulses per second. This allows us to reconstruct a 3D scene with centimeter accuracy. That might be the interior of a room, or house, The exterior of an industrial plant. A cell tower or city.

There are essentially 5 types of LiDAR data collection:

• High Level Aerial - Often collected using aeroplanes
• Low Level Aerial - Collected using drones
• Terrestrial - Stationary ground based tripods
• Mobile - Collected from a vehicle or walking
• Indoor - Both tripod and hand-held

LiDAR is extremely accurate and penetrative. This latter feature means thick vegetation, for example, is often not a barrier to LiDAR's ability to map the ground surface. Traditional imagery based point cloud data is more dense than LiDAR and provides a greater amount of metadata; colour for example. Often LiDAR and traditional imagery based methods are used to construct point clouds.

How do geodatabases work?

In its simplest form a geodatabase is a vector based representation of reality. Points, lines and polygons are the basic constructs. Geometry and attributes are the two core data elements stored by a geodatabase. Thus a cell tower might be represented by a point; its location (x,y), height, material and last inspection date, stored in the geodatabase. A river might be represented by a line, a park as a polygon.

Fundamentally a geodatabase allows an organization to store and view (often via a map) their assets. An organizations data usually needs to be converted from its native data format into a geodatabase. Thus a spreadsheet, taking a simple example, needs to undergo conversion before the data contained can be mapped.

Back to the question ..

To summarize: Point clouds are highly accurate representations of reality. A digital twin or 'bits to bytes'. Geodatabases are vector representations of reality. Accuracy can vary.

Public agencies have begun funding LiDAR data collection. Many provinces in Canada and States in the US have commissioned LiDAR aerial data collection. This data will eventually be made available for download. The USDA in the US has gone one step further and has begun an initiative to stream the LiDAR data it has been collecting. Departments of Transportation have begun scanning roadways using mobile LiDAR technology.

In short, we are generating increasing amounts of point cloud data.

Money is pouring into funding this current data collection/generation phase. The implications are staggering (and actually far beyond the original question posed here). Point clouds can allow us to view a city in 3D, both from above and at ground level. Artificial intelligence (AI) can help us extract features from a point cloud, and attach attributes to these features.

Think about GIS today. We pull data from a geodatabase and overlay that on a base map: "Show me all my water assets: pipes, valves etc".

Imagine instead using a digital reality platform. Starting with a 3D basemap, we make the same water assets request. That request is made to a point cloud source (and not geodatabase). What we see is a real world representation of these assets, at centimeter accuracy. This might be data collected weeks, even days ago.

With this flood of new data, one wonders the path geospatial will take. There is little doubt that 3D mapping platforms (digital reality) which can consume and provide access to this data will become increasingly more popular. Particularly those platforms which provide access to this data over the web.

Seeing and working in a world which is a mirror of reality, is compelling. That opens up a new wave of opportunities to solve problems. We live in fascinating times.

Read the follow up to this article here.

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