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Airborne laser scans present an optimal tool to describe geomorphological features in natural environments. However, a challenge\udarises in the detection of such phenomena, as they are embedded in the topography, tend to blend into their surroundings and leave only\uda subtle signature within the data. Most object-recognition studies address mainly urban environments and follow a general pipeline\udwhere the data are partitioned into segments with uniform properties. These approaches are restricted to man-made domain and are\udcapable to handle limited features that answer a well-defined geometric form. As natural environments present a more complex set of\udfeatures, the common interpretation of the data is still manual at large. In this paper, we propose a data-aware detection scheme, unbound\udto specific domains or shapes. We define the recognition question as an energy optimization problem, solved by variational means. Our\udapproach, based on the level-set method, characterizes geometrically local surfaces within the data, and uses these characteristics as\udpotential field for minimization. The main advantage here is that it allows topological changes of the evolving curves, such as merging\udand breaking. We demonstrate the proposed methodology on the detection of collapse sinkholes

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

AN ENERGY-BASED APPROACH FOR DETECTION AND CHARACTERIZATION OF SUBTLE ENTITIES WITHIN LASER SCANNING POINT-CLOUDS