SPECTRAL ANALYSIS OF IMAGES OF PLANTS UNDER STRESS USING A CLOSE-RANGE CAMERA

Plants signal their health in a broader spectrum than we can see with our eyes. We compared sunlight reflectance on plants at spectral wavelengths ranging from 430 nm to 870 nm in our study. These are based on multispectral images captured at a distance of 2 m. Indoor plants were observed over a period of 18 days and stressed due to a lack of sunlight or water. Wild sedge photographed on the forest floor at close range and with a difficult capture setup produced results comparable to published multispectral signatures derived from aerial imagery. Changes of leaf reflectance were noticed in spectral signatures and in vegetation indices. When calculating vegetation indices, our results show that comparing red and red edge reflectance values is superior to comparing red and NIR reflectance values

ON THE DEVELOPMENT OF A DATASET PUBLICATION GUIDELINE: DATA REPOSITORIES AND KEYWORD ANALYSIS IN ISPRS DOMAIN

The FAIR principle (find, access, interoperability, reuse) forms a sustainable resource for scientific exchange between researchers. Currently, the implementation of this principle is an important process for future research projects. To support this process in the ISPRS community, the usage of data repositories for dataset publication has the potential to bring closer the achievement of the FAIR principle. Therefore, we (1) analysed available data repositories, (2) identified common keywords in ISPRS publications and (3) developed a tool for searching appropriate repositories. Thus, infrastructures from the field of geosciences, that can already be used, become more accessible

CURRENT STATUS OF THE BENCHMARK DATABASE BEMEDA

Open science is an important attribute for developing new approaches. Especially, the data component plays a significant role. The FAIR principle provides a good orientation towards open data. One part of FAIR is findability. Thus, domain specific dataset search platforms were developed: the Earth Observation Database and our Benchmark Metadata Database (BeMeDa). In addition to the search itself, the datasets found by this platforms can be compared with each other with regard to their interoperability. We compare these two platforms and present an update of our platform BeMeDa. This update includes additional location information about the datasets and a new frontend design with improved usability. We rely on user feedback for further improvements and enhancements

Potential of Mobile Mapping to Create Digital Twins of Forests

Forests are irreplaceable and are being studied extensively. Better forest inventory and understanding necessitate effective mapping, modeling, and automatic analysis. As a result, considerable research effort is being devoted to digitizing forest environments. Recently, digital twins have come to the attention of the geospatial community as a virtual representation of the Earth’s surface linked to its corresponding physical asset. This concept is applicable to forests and has been studied in the literature. This requires initial input data obtained through reality capture. Among mapping techniques, laser scanning has emerged as a state-of-the-art technology for vegetation modeling. In this paper, we look into the potential of mobile laser scanning for forest digital twinning. While most studies concentrate on single tree detection, modeling, and estimation of dendrometric parameters, we also include lower vegetation in our investigations. To accomplish this, we first detect single trees and then investigate different vegetation densities and levels using geometric metrics. We also demonstrate how to model the underlying layers of vegetation in a digital twin. We perform the tests on data from mobile laser scanning (MLS) and compare the results to those from airborne laser scanning (ALS).We show that single tree detection based on crown separation using MLS data works similarly to or slightly better than ALS data. Furthermore, we demonstrate that MLS data allows for more detailed analysis of understory vegetation taking into account different height levels and a multi-level representation, whereas ALS data only allows for rough analysis of the lower parts of forest vegetation

SEMANTIC LABELING OF STRUCTURAL ELEMENTS IN BUILDINGS BY FUSING RGB AND DEPTH IMAGES IN AN ENCODER-DECODER CNN FRAMEWORK

In the last decade, we have observed an increasing demand for indoor scene modeling in various applications, such as mobility inside buildings, emergency and rescue operations, and maintenance. Automatically distinguishing between structural elements of buildings, such as walls, ceilings, floors, windows, doors etc., and typical objects in buildings, such as chairs, tables and shelves, is particularly important for many reasons, such as 3D building modeling or navigation. This information can be generally retrieved through semantic labeling. In the past few years, convolutional neural networks (CNN) have become the preferred method for semantic labeling. Furthermore, there is ongoing research on fusing RGB and depth images in CNN frameworks. For pixel-level labeling, encoder-decoder CNN frameworks have been shown to be the most effective. In this study, we adopt an encoder-decoder CNN architecture to label structural elements in buildings and investigate the influence of using depth information on the detection of typical objects in buildings. For this purpose, we have introduced an approach to combine depth map with RGB images by changing the color space of the original image to HSV and then substitute the V channel with the depth information (D) and use it utilize it in the CNN architecture. As further variation of this approach, we also transform back the HSD images to RGB color space and use them within the CNN. This approach allows for using a CNN, designed for three-channel image input, and directly comparing our results with RGB-based labeling within the same network. We perform our tests using the Stanford 2D-3D-Semantics Dataset (2D-3D-S), a widely used indoor dataset. Furthermore, we compare our approach with results when using four-channel input created by stacking RGB and depth (RGBD). Our investigation shows that fusing RGB and depth improves results on semantic labeling; particularly, on structural elements of buildings. On the 2D- 3D-S dataset, we achieve up to 92.1 % global accuracy, compared to 90.9 % using RGB only and 93.6 % using RGBD. Moreover, the scores of Intersection over Union metric have improved using depth, which shows that it gives better labeling results at the boundaries

Temporal changes in the pattern of invasive angiography use and its outcome in suspected coronary artery disease : implications for patient management and healthcare resource utilization

Introduction: Invasive coronary angiography (CAG), the ‘gold standard’ in coronary artery disease (CAD) diagnosis, requires hospitalization, is not risk-free, and engages considerable healthcare resources. Aim: To assess recent (throught out 10 years) evolution of ‘significant’ (≥ 50% stenosis(es)) CAD prevalence in subjects undergoing CAG for CAD diagnosis in a high-volume tertiary referral center. Material and methods: Anonymized medical records were compared from the last vs. the first 2-years of the decade (June 2007 to May 2018). Referrals for suspected CAD were 2067 of 4522 hospitalizations (45.7%) and 1755 of 5196 (33.8%) respectively (p < 0.001). Results: The median patient age (64 vs. 68 years) and the prevalence of heart failure (24.1% vs. 42.2%) increased significantly (p < 0.001). The CAG atherosclerotic lesions, for all stenosis categories (< 50%; ≥ 50%; ≥ 70%; occlusion(s)), were significantly more prevalent in men. The proportion of subjects with any atherosclerosis on CAG increased (80.7% vs. 77.6%, p = 0.015). However, in the absence of any gross change in, for instance, the fraction of women (40.4% vs. 41.8%), the proportion of CAGs with significant CAD (lesion(s) ≥ 50%) decreased from 55.2% in 2007/2008 to below 1 in every 2 angiograms (48.9%) in 2017/2018 (p < 0.001). This unexpected finding occurred consistently across nearly all CAG referral categories. Conclusions: Despite more advanced age and a higher proportion of subjects with ‘any’ coronary atherosclerosis on CAG, the likelihood of a ‘negative’ angiogram (lesion(s) < 50%; no further evaluation/intervention) has increased significantly over the last decade. The exact nature of this phenomenon requires further investigation, particularly as a reverse trend would be expected with the growing role (and current high penetration) of contemporary non-invasive diagnostic tools to rule out significant CAD