When Good Policies Go Bad: Controlling Risks Posed By Flawed Incentive-Based Compensation

The recent Wells Fargo scandal revealed the harm that can result from flawed incentive-based compensation arrangements. Large financial institutions have both a legal and an ethical obligation to ensure that any incentive-based compensation arrangements that are in place will not encourage risky or fraudulent employee behavior. The continued existence of inappropriate and poorly structured arrangements demonstrates that existing regulations are inadequate to ensure compliance and protect consumers. Regulations should include increased penalties and should more evenly distribute the burden of oversight and compliance between the public and private sectors. In addition to regulatory reform, the government should prosecute culpable high-level executives more aggressively. Arguably, white-collar criminals are in a position to be more effectively deterred by the threat of incarceration than other types of criminals

Performance comparison of deep learning and machine learning methods in determining wetland water areas using EuroSAT dataset

Wetlands are critical to the ecology because they maintain biodiversity and provide home for a variety of species. Researching, mapping, and conservation of wetlands is a challenging and time-consuming process. Because they produce temporal and geographical information, remote sensing and photogrammetric approaches are useful tools for analyzing and managing wetlands. In this study, the water areas of five different wetlands obtained with Sentinel-2 images in Turkey were classified. Although obtaining large amounts of high-dimensional dataset labeled for various land types is costly, it is a significant advantage to use it after model training in a wide range of applications. In this paper, the EuroSAT dataset was used in the validation process. Proposed deep learning–based 1D convolutional neural networks (CNN) and traditional machine learning methods (i.e., support vector machine, linear discriminant analysis, K-nearest neighborhood, canonical correlation forests, and AdaBoost.M1) were compared quantitatively (i.e., accuracy, recall, precision, specificity, F-score, and image quality assessment metrics) and qualitatively. Finally, pairwise comparison was made with chi-square-based McNemar’s test. There is a statistical difference between 1D CNN and machine learning method (except the support vector machine vs linear discriminant analysis in Test 1 area). CNN models outperform machine learning algorithms in terms of non-linear function approximation and the ability to extract and articulate data features. Since 1D CNNs can process data in a highly complex and unique feature space, they are very successful in segmenting strongly related and highly correlated discrete signals. It also has advantages over machine learning methods for water body extraction in that it can be integrated with sophisticated image pre-processing and standardization tools, is less susceptible to low-level random noise, and provides shift in variations and contrast-invariant image local transforms.</p

Adaptive neighborhood size and effective geometric features selection for 3D scattered point cloud classification

Classification of 3D scatter and unorganized point cloud (PC) is an ongoing hard problem due to high redundancy, unbalanced sampling density, and large data structure of PC. Geometric and spectral features derived from the PC are generally used for classification. In this paper, an Omnivariance based adaptive neighborhood size selection method and a new feature set composed of 14 features are proposed for extraction of geometric features for each individual point within the local neighborhood. Performance of 8 modern classifiers with different strategies (i.e., boosting, ensemble, and deep learning etc.) were evaluated on the Oakland, Vaihingen, and ISPRS datasets. These 3 datasets are identified by 5, 9, and 2 distinct object classes, respectively. The results were compared with different neighborhood size selection methods (i.e., eigenentropy based, fixed number of the k-nearest neighbors) and feature set (i.e., 21 features). Only 3D local features were employed to classify datasets with varying characteristics and properties. The proposed optimum neighbor selection method and feature set provided the best statistical results with Auto-Encoder classifier (the overall accuracies are over 85%, 60% and, 90% the Oakland, Vaihingen, and ISPRS datasets, respectively). Especially for the ISPRS dataset, the Auto-Encoder obtained over 94%, 90%, and 93% precision, recall, and f-score, respectively. (C) 2021 Published by Elsevier B.V

A comprehensive framework based on GIS-AHP for the installation of solar PV farms in Kahramanmaraş, Turkey

© 2021Solar photovoltaic (PV) technologies receive investment, support, and incentives despite their apparent high costs. In terms of renewable energy policies and spatial planning, site selection for solar PV farms is a critical issue. A novel comprehensive framework for assessing the site suitability of solar PV farms is proposed, which considers the preservation of natural, ecological conservation, and cultural areas. The framework combines a Geographical Information System (GIS) with layers of satellite-derived data for energy resources as well as locally collected data, and the Multi Criteria Decision Making (MCDM) method based on the Analytic Hierarchy Process (AHP). In the GIS environment, maps belonging to fourteen sub-criteria of the three main criteria (Geography, Climate and Location) for Kahramanmaraş, Turkey were obtained, as well as suitability map derived from their weighted sum. The AHP method generates the appropriate weight for each input criteria using a pairwise comparison matrix. GHI, aspect, distance from power line network, land use/cover, annual average temperature, and others were found as the most necessary sub-criteria, respectively. According to the findings, 9.62% of study area very low, 20.15% is low, 22.51% is moderate, 23.98% is high and 23.74% is very high, and 26% is unsuitable for solar PV farms. Based on the results, the northern districts of the study region were determined to be the most suitable for the construction of solar PV farms. When existing five PV farms' location selection decisions were examined, it was observed that the investment outcomes were consistent with the results of the study. The proposed framework is projected to minimize the cost, time, and resources used on the construction of solar PV farms

Weighted differential evolution algorithm based pansharpening

Imaging Satellites acquire multispectral images, MIs, in low resolution, LR, and panchromatic images, PANs, in high resolution, HR, due to some advantages provided in satellite design. The pansharpening, PS, is a super resolution image synthesis method that is used to generate the pansharpened image, PI, by fusion of PAN and MI. The use of PS process is unavoidable in applications such as efficient use of communication-bandwidth of imaging satellites and fusion of images derived from various image sensors. The PS process is a multi-step process consisting of various complex image processing stages, such as registration, resampling, synthesis, and fusion. The Weighted Differential Evolution Algorithm-based PS method, WDEPS, has been proposed in this paper. The WDEPS uses WDE to synthesize the intensity image, which is the blended-image of MI -bands. WDE has been used to compute the relevant image-blending weights, efficiently. In the experiments, several satellite images (QuickBird-2, Ikonos-2, and GeoEye-1) with different spatial resolutions were used. The WDEPS's experimental results have been compared with 17 well-known PS methods by using 9 full-reference and 3 blind image quality assessment metrics. The experimental results exposed that WDEPS generates high-quality PIs than traditional PS methods used in the experimental aspect of qualitative and quantitative assessment

QUALITY ASSESSMENT OF PANSHARPENED RASAT AND LANDSAT-8 IMAGES USING SYNTHETIC SENTINEL-2 PANCHROMATIC IMAGE

Technical and physical limitations often do not allow images to be acquired with high spatial and spectral resolution. Pansharpened images obtained by fusing high spatial resolution panchromatic images and multi-spectral images are widely used in GIS applications. In this study, it is aimed to increase the spatial resolution of the RASAT and Landsat-8 multispectral satellite images with synthetic Sentinel-2 panchromatic data. Six different pansharpening methods were used to test the success of the synthetic panchromatic data generation method using dataset with two different land use/land cover properties. Seven full reference image quality assessment metrics and two referenceless image quality assessment metrics were used to perform quantitative comparison