The evolution and spreading of data capturing methods ranging from simple GPS devices like smart-phones to large scale imaging equipment – including very high resolution and hyperspectral cameras and LiDAR – resulted in an exponential growth in the amount of spatial data maintained by companies and organizations. At the same time methods for extracting information from such data are often behind in efficiency. In this paper we analyse the possibilities for nation-wide change detection of massive airborne laser altimetry point clouds, based on digital elevation models generated from them. The proposed workflow distinguishes modifications in the built-up area from other changes and noise. Our methodology combines different area processing spatial algorithms: object detection, noise filtering, morphological operations and clustering. Our proposed method is designed to scale dynamically on extensive datasets by processing a spatially partitioned input dataset in an easily parallelized manner. Favourable visualizations and aggregated representations of the results are examined, followed by a discussion of feasible validation methods. As a demonstration we showcase the implemented distributed evaluation of our workflow on the full Dutch altimetry archive – a dataset exceeding several terabytes of storage space – using a high-performance computing environment. While the average execution time was 47 h on a desktop computer, our solution only took less than 2.4 h to complete. The output was validated against the building layer of the TOP10NL topographic dataset, proving a 70% accuracy nation-wide and over 90% for urban areas. As a result our analysis shows that The Netherlands experienced an aggregated building volume change of 912.33 km3 between the acquisition of AHN-2 and AHN-3.
|Number of pages||12|
|Journal||International Journal of Applied Earth Observation and Geoinformation|
|Publication status||Published - 2023|
- Big data
- Change detection
- Cloud computing
- Object recognition