460 research outputs found
UAV-based high-resolution remote sensing and machine learning for risk management in hazard and disaster areas
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Detection of radioactive waste sites in the Chornobyl exclusion zone using UAV-based lidar data and multispectral imagery
No full textThe severe accident at the Chornobyl Nuclear Power Plant (ChNPP) in 1986 resulted in extraordinary contamination of the surrounding territory, which necessitated the creation of the Chornobyl Exclusion Zone (ChEZ). During the accident, liquidation materials contaminated by radioactive fallout (e.g., contaminated soil and trees) were buried in so-called Radioactive Waste Temporary Storage Places (RWTSPs). The exact locations of these burials were not always sufficiently documented. However, for safety management, including eventual remediation works, it is crucial to know their locations and rely on precise hazard maps. Over the past 34 years, most of these so-called trenches and clamps have been exposed to natural processes. In addition to settlement and erosion, they have been overgrown with dense vegetation. To date, more than 700 burials have been thoroughly investigated, but a large number of burial sites (approximately 300) are still unknown. In the past, numerous burials were identified based on settlement or elevation in the decimeter range, and vegetation anomalies that tend to appear in the immediate vicinity. Nevertheless, conventional detection methods are time-, effort- and radiation dose-intensive. Airborne gamma spectrometry and visual ground inspection of morphology and vegetation can provide useful complementary information, but it is insufficient for precisely localizing unknown burial sites in many cases. Therefore, sensor technologies, such as UAV-based lidar and multispectral imagery, have been identified as potential alternative solutions. This paper presents a novel method to detect radioactive waste sites based on a set of prominent features generated from high-resolution remote sensing data in combination with a random forest (RF) classifier. Initially, we generate a digital terrain model (DTM) and 3D vegetation map from the data and derive tree-based features, including tree density, tree height, and tree species. Feature subsets compiled from normalized DTM height, fast point feature histograms (FPFH), and lidar metrics are then incorporated. Next, an RF classifier is trained on reference areas defined by visual interpretation of the DTM grid. A backward feature selection strategy reduces the feature space significantly and avoids overfitting. Feature relevance assessment clearly demonstrates that the members of all feature subsets represent a final list of the most prominent features. For three representative study areas, the mean overall accuracy (OA) is 98.2% when using area-wide test data. Cohens’ kappa coefficient ranges from 0.609 to 0.758. Additionally, we demonstrate the transferability of a trained classifier to an adjacent study area (OA = 93.6%, = 0.452). As expected, when utilizing the classifier on geometrically incorrect and incomplete reference data, which were generated from old maps and orthophotos based on visual inspection, the OA decreases significantly to 65.1% ( = 0.481). Finally, detection is verified through 38 borings that successfully confirm the existence of previously unknown buried nuclear materials in classified areas. These results demonstrate that the proposed methodology is applicable to detecting area-wide unknown radioactive biomass burials in the ChEZ
A Multi-Dimensional Trust Model for Heterogeneous Contract Observations
No full textIn this paper we develop a novel probabilistic model of computational trust that allows agents to exchange and combine reputation reports over heterogeneous, correlated multi-dimensional contracts. We consider the specific case of an agent attempting to procure a bundle of services that are subject to correlated quality of service failures (e.g. due to use of shared resources or infrastructure), and where the direct experience of other agents within the system consists of contracts over different combinations of these services. To this end, we present a formalism based on the Kalman filter that represents trust as a vector estimate of the probability that each service will be successfully delivered, and a covariance matrix that describes the uncertainty and correlations between these probabilities. We describe how the agents’ direct experiences of contract outcomes can be represented and combined within this formalism, and we empirically demonstrate that our formalism provides significantly better trustworthiness estimates than the alternative of using separate single-dimensional trust models for each separate service (where information regarding the correlations between each estimate is lost)
Semantic labeling of als point clouds for tree species mapping using the deep neural network pointnet++
No full textMost methods for the mapping of tree species are based on the segmentation of single trees that are subsequently classified using a set of hand-crafted features and an appropriate classifier. The classification accuracy for coniferous and deciduous trees just using airborne laser scanning (ALS) data is only around 90% in case the geometric information of the point cloud is used. As deep neural networks (DNNs) have the ability to adaptively learn features from the underlying data, they have outperformed classic machine learning (ML) approaches on well-known benchmark datasets provided by the robotics, computer vision and remote sensing community. Though, tree species classification using deep learning (DL) procedures has been of minor research interest so far. Some studies have been conducted based on an extensive prior generation of images or voxels from the 3D raw data. Since innovative DNNs directly operate on irregular and unordered 3D point clouds on a large scale, the objective of this study is to exemplarily use PointNet++ for the semantic labeling of ALS point clouds to map deciduous and coniferous trees. The dataset for our experiments consists of ALS data from the Bavarian Forest National Park (366 trees/ha), only including spruces (coniferous) and beeches (deciduous). First, the training data were generated automatically using a classic feature-based Random Forest (RF) approach classifying coniferous trees (precision = 93%, recall = 80%) and deciduous trees (precision = 82%, recall = 92%). Second, PointNet++ was trained and subsequently evaluated using 80 randomly chosen test batches à 400 m2. The achieved per-point classification results after 163 training epochs for coniferous trees (precision = 90%, recall = 79%) and deciduous trees (precision = 81%, recall = 91%) are fairly high considering that only the geometry was included. Nevertheless, the classification results using PointNet++ are slightly lower than those of the baseline method using a RF classifier. Errors in the training data and occurring edge effects limited a better performance. Our first results demonstrate that the architecture of the 3D DNN PointNet++ can successfully be adapted to the semantic labeling of large ALS point clouds to map deciduous and coniferous trees. Future work will focus on the integration of additional features like i.e. the laser intensity, the surface normals and multispectral features into the DNN. Thus, a further improvement of the accuracy of the proposed approach is to be expected. Furthermore, the classification of numerous individual tree species based on pre-segmented single trees should be investigated
Evidence for a mixed mass composition at the ‘ankle’ in the cosmic-ray spectrum
No full textWe report a first measurement for ultrahigh energy cosmic rays of the correlation between the depth of shower maximum and the signal in the water Cherenkov stations of air-showers registered simultaneously by the fluorescence and the surface detectors of the Pierre Auger Observatory. Such a correlation measurement is a unique feature of a hybrid air-shower observatory with sensitivity to both the electromagnetic and muonic components. It allows an accurate determination of the spread of primary masses in the cosmic-ray flux. Up till now, constraints on the spread of primary masses have been dominated by systematic uncertainties. The present correlation measurement is not affected by systematics in the measurement of the depth of shower maximum or the signal in the water Cherenkov stations. The analysis relies on general characteristics of air showers and is thus robust also with respect to uncertainties in hadronic event generators. The observed correlation in the energy range around the ‘ankle’ at lg(E/eV)=18.5–19.0lg(E/eV)=18.5–19.0 differs significantly from expectations for pure primary cosmic-ray compositions. A light composition made up of proton and helium only is equally inconsistent with observations. The data are explained well by a mixed composition including nuclei with mass A>4A>4. Scenarios such as the proton dip model, with almost pure compositions, are thus disfavored as the sole explanation of the ultrahigh-energy cosmic-ray flux at Earth
Classification of tree species and standing dead trees by fusing UAV-based lidar data and multispectral imagery in the 3D deep neural network PointNet++
No full textKnowledge of tree species mapping and of dead wood in particular is fundamental to managing our forests. Although individual tree-based approaches using lidar can successfully distinguish between deciduous and coniferous trees, the classification of multiple tree species is still limited in accuracy. Moreover, the combined mapping of standing dead trees after pest infestation is becoming increasingly important. New deep learning methods outperform baseline machine learning approaches and promise a significant accuracy gain for tree mapping. In this study, we performed a classification of multiple tree species (pine, birch, alder) and standing dead trees with crowns using the 3D deep neural network (DNN) PointNet++ along with UAV-based lidar data and multispectral (MS) imagery. Aside from 3D geometry, we also integrated laser echo pulse width values and MS features into the classification process. In a preprocessing step, we generated the 3D segments of single trees using a 3D detection method. Our approach achieved an overall accuracy (OA) of 90.2% and was clearly superior to a baseline method using a random forest classifier and handcrafted features (OA = 85.3%). All in all, we demonstrate that the performance of the 3D DNN is highly promising for the classification of multiple tree species and standing dead trees in practice
A new bed for the river Inde: a case study with special view on the risk of depth erosion
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Numerical and experimental methods for the calibration of electronic instrumentation on BepiColombo Mission
No full textThe SIMBIO-SYS instrument suite of the BepiColombo ESA mission to Mercury is equipped with the Stereo Imaging Channel (STC) stereo telescope, based on an innovative and compact design, in which two independent optical channels oriented at ±20° with respect to Nadir collect light on a common detector. The stereo acquisition mode is based on the push frame concept, which has never been adopted on space missions before.
To demonstrate and characterize the capability of the instrument to reconstruct a tridimensional surface with the desired accuracy by means of the stereo push-frame concept, an innovative experimental setup has been realized. The problem of working at an essentially infinite object distance over hundreds km baselines has been overcome by means of a simple collimator lens and two precision rotation stages, scaling down the stereo reconstruction problem in terms of baseline and accuracy requirements. The stereo validation has been performed by comparing the shape of a target object accurately measured by laser scanning, with the shape reconstructed by applying classical stereo algorithms to the acquired image pairs.
The reconstruction of depth information from stereo images of an observed surface requires the characterization of the imaging system in terms of camera calibration process. The particular application required a preliminary analysis of the most suited camera model by verifying the calibration procedure performance. To this end, an innovative method has been developed for the simulation of the calibration data and for the evaluation of calibration algorithms.
A preliminary test of the stereo validation procedure has been performed with an evaluation model of STC. The obtained results show the goodness of this innovative technique, that will be applied also for validating the stereo capabilities of STC flight model
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