Our world is changing constantly, and keeping track of these changes is vital for making solid business decisions and planning for future events. From a small local business to broad federal initiatives, understanding change and determining its impact can be a challenge for any organization.
Let’s look at a typical municipal government example: monitoring urban property change. Using personnel to manually cover large areas to detect changes in property is becoming more costly. In fact in many cases, building permit regulations are being relaxed due to budget shortfalls. The result is a loss of visibility for land use changes, and ultimately a huge dent in the ability to provide up-to-date information for management and planning purposes.
Using Point Clouds for 3D Change Detection
What is required is an automated means of change detection. Traditionally, change detection was done using 2D imagery of the same area from two periods in time, and performing a pixel-by-pixel change calculation. This works, but puts very specific requirements on the imagery in terms of comparability. Aspects like building lean, absence of vegetation and comparability in spatial resolution are key for a satisfactory result.
Using 3D change detection , which employs high density point clouds, many of these restrictive requirements are overcome. Due to almost negligible radiometric differences, 3D change detection can reveal change that is undetectable in the traditional 2D space. Furthermore, when using high density point clouds, only those changes that result in height differences are shown in the analysis. This creates a very clean change image, and allows the end user to focus only on the important changes, and not be distracted by marginal changes.
The sophisticated algorithms in ERDAS IMAGINE’s AutoDTM module enables us to create these high density point clouds from any set of stereo images available, even when using historical sets. In this way, even historical trends in terrain movement will surface.
In this on-demand webcast, we demonstrate how both 3D and 4D change detection using high density point clouds greatly benefits municipalities in urban business processes. We cite real world examples for property change management, and also highlight water management organizations who examine changes in terrain to predict water flow and prevent flooding.