The advanced technologies of laser scanning with their accuracy, speed and resolution, have revolutionized the field of Earth observation. The amount of information contained within 3D point clouds has introduced the recognition of geometric structure as the most important computational challenge of this decade. Developing new solutions requires coping with irregular point distribution, the lack of topology and their sheer size that often exceeds the capabilities of modern computer systems. Using the known concepts that were developed for pattern recognition in raster data leads to inefficient algorithms that require intensive user interaction and additional information about the geographical areas.

The proposed project’s intention is to research a new methodology for recognizing 3D geometrical structures, monitoring their dynamics, and detecting events within large point clouds, as acquired from scanning the Earth’s surface by applying contemporary findings of mathematical morphology. Although, mathematical morphology is considered to be a young mathematical theory, its quantitative arithmetic of shape description offers great expressional strength. Morphological operators are derived from the set theory and extended by using the concepts of geometry, topology, probability, and statistics, and are completely adapted for digital and parallel processing. The recently developed algebraic formalization of scanning morphology offer a spatially-dependent, selective, and completely automatic adaptation to the geometrical structures of input data. These theoretical foundations offer the possibility of developing an efficient pattern recognition methodology, where adaptation to the temporal domain would allow a quantitative presentation of events and a description of the dynamics. The efficiency of the developed method would be demonstrated with by two uses: (i) recognition of geomorphological process kinematics and (ii) monitoring tree development in Slovenia. For the purpose of recognizing the kinematics of geomorphological changes (such as landslides) it is intended to develop an automatic method for ground recognition within 3D point clouds, and the construction of a digital elevation model that would be more accurate and time efficient without the need for users to set parameters. Such a procedure would allow for the detection of changes in the terrain and evaluate the volume, mass and speed of moving earth masses over large geographical areas (whole of Slovenia) with high resolution (under 0.5m) and accuracy (over 90%). Similar accuracy can be expected regarding (ii) monitoring tree development, where a new method for recognizing single trees would be developed. This method would estimate the number of trees within a respected area and provide the geographical positions, heights and volumes of tree-crowns. It would measure growth of a single tree, wood biomass growth by cyclical data acquisition, and develop a predictive simulation of their development.

The precision of the proposed uses would be tested by on terrain measurements, while the construction of a digital elevation model of Slovenia will demonstrate their computational efficiencies. In this way, the national project for surface scanning of Slovenia with LiDAR technology would be supported directly. The results of our research will be published in the most distinguished international journals, and regularly presented to the Slovenian public by organizing symposiums and workshops. It will also promote our products abroad by attending international conferences.