A Textured Silicon Calorimetric Light Detector

We apply the standard photovoltaic technique of texturing to reduce the reflectivity of silicon cryogenic calorimetric light detectors. In the case of photons with random incidence angles, absorption is compatible with the increase in surface area. For the geometrically thin detectors studied, energy resolution from athermal phonons, dominated by position dependence, is proportional to the surface-to-volume ratio. With the CaWO4 scintillating crystal used as light source, the time constants of the calorimeter should be adapted to the relatively slow light-emission times.Comment: Submitted to Journal of Applied Physic

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

Detection of building regions using airborne LiDAR : a new combination of raster and point cloud based GIS methods

In this paper, a new GIS workflow for fully automated building detection from airborne LiDAR data is introduced. The strengths of both raster and point cloud based methods are combined, in order to derive reliable building candidate regions serving as input for 3D building outline extraction and modeling algorithms. Input data are a normalized Digital Surface Model (nDSM) and a slope-adaptive echo ratio raster, which is a significant parameter for solid objects with low surface roughness, such as buildings. In contrast, high vegetation exhibits a local vertical distribution of laser echoes leading to a low echo ratio value. Potential building areas are detected in the raster domain using standard tools provided by GRASS GIS. Seed regions are identified by using a threshold on (i) object height >2.0 m and (ii) echo ratio >75%. The following growing of the seed regions provides that building walls, overhanging roof parts, and areas obstructed by high vegetation are included. Finally, non-building regions are removed by an object-based classification using a threshold on average laser point surface roughness. The presented candidate region detection achieves high completeness (>97%) with already moderate correctness (>70%). By applying an existing 3D building outline extraction and modeling algorithm, the applicability of the derived candidate building regions is demonstrated

UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (bare earth, grassland, trees), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672 m3 is estimated for the test site (48 ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland