scholarly journals DETECTION OF LANDSLIDES WITH SAR POLARYMETRY: CASE STUDY OF SOUTH-EASTERN COLOMBIA

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 177-184
Author(s):  
N. A. Correa-Muñoz ◽  
C. A. Murillo-Feo
Keyword(s):  
Principal Component ◽  
Unsupervised Classification ◽  
Bare Soil ◽  
Surface Scattering ◽  
Total Power ◽  
Detection Model ◽  
Radar Images ◽  
Dispersion Mechanism ◽  
Forested Areas

<p><strong>Abstract.</strong> SAR polarimetry (PolSAR) is a method that can be used to investigate landslides. Polarimetric scattering power decomposition allows to separate the total power received by the SAR antenna, which is divided in surface scattering power, double bounce scattering and volume scattering power. Polarimetric indices are expected to serve for landslide recognition, because landslides’ scattering properties are different from those of the surrounding forested areas. The surface scattering mechanism is mainly caused by rough surfaces like bare soil and agricultural fields, so we hope that this will be the predominant dispersion mechanism in landslides. In a study area located in south-western Colombia, we used dual-Pol provided by ESA’s Sentinel-1 satellites and quad-pol from NASA’s UAVSAR aerial platform. Using C-band and L-band radar images, we analysed the interaction between radar signals and landslides. First, with dual-pol we found backscatter calibrate coefficients over four GRD radar images acquired between 2015 and 2017. The analysis gave an average backscatter value of &amp;minus;14.47&amp;thinsp;dB for VH polarisation and &amp;minus;8.40&amp;thinsp;dB for VV polarisation. Then, using H-a decomposition for quad-pol data, we validated the high relationship between entropy and alpha parameter, which has the highest contribution to the first axis in a principal component analysis. These results were used to obtain an unsupervised classification of landslides, that separated the Colombian Geological Service landslide inventory in three classes characterized by the mechanism of dispersion. These results will be combined with InSAR parameters, morphometric parameters and optical spectral indexes to obtain a local detection model of landslides.</p>

scholarly journals Data Mining to Identify Anomalies in Public Procurement Rating Parameters

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2873
Author(s):  
Yeferson Torres-Berru ◽  
Vivian F. López Batista
Keyword(s):  
Supervised Learning ◽  
Public Procurement ◽  
Principal Component ◽  
Support Vector ◽  
Self Organizing Map ◽  
Detection Model ◽  
Process Qualification ◽  
Multi Phase ◽  
Direct Contracting

The awarding of public procurement processes is one of the main causes of corruption in governments, due to the fact that in many cases, contracts are awarded to previously agreed suppliers (favouritism); for this selection, the qualification parameters of a process play a fundamental role, seeing as due to their manipulation, bidders with high prices win, causing prejudice to the state. This study identifies processes with anomalies and generates a model for detecting possible corruption in the assignment of process qualification parameters in public procurement. A multi-phase model was used (the identification of anomalies and generation of the detection model), which uses different algorithms, such as clustering (K-Means), Self-Organizing map (SOM), Support Vector Machine (SVM) and Principal Component Analysis (PCA). SOM was used to determine the level of influence of each rating parameter, K-Means to create groups by clustering, semi-supervised learning with SVM and PCA to generate a model to detect anomalies in the processes. By means of a case study, four groups of processes were obtained, highlighting the presence of the group “null economic offer” where the values for the economic offer do not exceed 1%, and a greater weight is given to other qualification parameters, which include direct contracting. The processes in this cluster are considered anomalous. Following this methodology, a semi-supervised learning model is built for the detection of anomalies, which obtains an accuracy of 95%, allowing the detection of procedures where the aim is to benefit a particular supplier by means of the qualification assignment parameters.

scholarly journals Dealing with non-stationarity in sub-daily stochastic rainfall models

2018 ◽  
Vol 22 (11) ◽  
pp. 5919-5933 ◽  
Author(s):  
Lionel Benoit ◽  
Mathieu Vrac ◽  
Gregoire Mariethoz
Keyword(s):  
Real Data ◽  
Unsupervised Classification ◽  
Careful Examination ◽  
Model Parameters ◽  
Critical Importance ◽  
Radar Images ◽  
Weather Generators ◽  
Stochastic Weather Generators

Abstract. Understanding the stationarity properties of rainfall is critical when using stochastic weather generators. Rainfall stationarity means that the statistics being accounted for remain constant over a given period, which is required for both inferring model parameters and simulating synthetic rainfall. Despite its critical importance, the stationarity of precipitation statistics is often regarded as a subjective choice whose examination is left to the judgement of the modeller. It is therefore desirable to establish quantitative and objective criteria for defining stationary rain periods. To this end, we propose a methodology that automatically identifies rain types with homogeneous statistics. It is based on an unsupervised classification of the space–time–intensity structure of weather radar images. The transitions between rain types are interpreted as non-stationarities. Our method is particularly suited to deal with non-stationarity in the context of sub-daily stochastic rainfall models. Results of a synthetic case study show that the proposed approach is able to reliably identify synthetically generated rain types. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm. This highlights the need for a careful examination of the temporal stationarity of precipitation statistics when modelling rainfall at high resolution.

scholarly journals Dealing with non-stationarity in sub-daily stochastic rainfall models

2018 ◽  
Author(s):  
Lionel Benoit ◽  
Mathieu Vrac ◽  
Gregoire Mariethoz
Keyword(s):  
Real Data ◽  
Unsupervised Classification ◽  
Careful Examination ◽  
Model Parameters ◽  
Critical Importance ◽  
Radar Images ◽  
Weather Generators ◽  
Stochastic Weather Generators

Abstract. Understanding the stationarity properties of rainfall is critical when using stochastic weather generators. Rainfall stationarity means that the statistics being accounted for remain constant over a given period, which is required for both inferring model parameters and simulating synthetic rainfall. Despite its critical importance, the stationarity of precipitation statistics is often regarded as a subjective choice whose examination is left to the judgement of the modeler. It is therefore desirable to establish quantitative and objective criteria for defining stationary rain periods. To this end, we propose a methodology that automatically identifies rain types with homogeneous statistics. It is based on an unsupervised classification of the space–time–intensity structure of weather radar images. The transitions between rain types are interpreted as non-stationarities. Our method is particularly suited to deal with non-stationarity in the context of sub-daily stochastic rainfall models. Results of a synthetic case study show that the proposed approach is able to reliably identify synthetically generated rain types. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm. This highlights the need for a careful examination of the temporal stationarity of precipitation statistics when modelling rainfall at high resolution.

scholarly journals Evaluating Landscape Attractiveness with Geospatial Data, a Case Study in Flanders, Belgium

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 703
Author(s):  
Astrid Vannoppen ◽  
Jeroen Degerickx ◽  
Anne Gobin
Keyword(s):  
Ecosystem Services ◽  
Urban Areas ◽  
Policy Development ◽  
Bare Soil ◽  
Geospatial Data ◽  
Soil Conditions ◽  
Landscape Indicators ◽  
Landscape Characteristics ◽  
Landscape Quality

Attractive landscapes are diverse and resilient landscapes that provide a multitude of essential ecosystem services. The development of landscape policy to protect and improve landscape attractiveness, thereby ensuring the provision of ecosystem services, is ideally adapted to region specific landscape characteristics. In addition, trends in landscape attractiveness may be linked to certain policies, or the absence of policies over time. A spatial and temporal evaluation of landscape attractiveness is thus desirable for landscape policy development. In this paper, landscape attractiveness was spatially evaluated for Flanders (Belgium) using landscape indicators derived from geospatial data as a case study. Large local differences in landscape quality in (i) rural versus urban areas and (ii) between the seven agricultural regions in Flanders were found. This observed spatial variability in landscape attractiveness demonstrated that a localized approach, considering the geophysical characteristics of each individual region, would be required in the development of landscape policy to improve landscape quality in Flanders. Some trends in landscape attractiveness were related to agriculture in Flanders, e.g., a slight decrease in total agricultural area, decrease in dominance of grassland, maize and cereals, a decrease in crop diversity, sharp increase in the adoption of agri-environmental agreements (AEA) and a decrease in bare soil conditions in winter. The observed trends and spatial variation in landscape attractiveness can be used as a tool to support policy analysis, assess the potential effects of future policy plans, identify policy gaps and evaluate past landscape policy.

scholarly journals Multitemporal–Multispectral UAS Surveys for Archaeological Research: The Case Study of San Vincenzo Al Volturno (Molise, Italy)

2021 ◽  
Vol 13 (14) ◽  
pp. 2719
Author(s):  
Nicodemo Abate ◽  
Alessia Frisetti ◽  
Federico Marazzi ◽  
Nicola Masini ◽  
Rosa Lasaponara
Keyword(s):  
Middle Ages ◽  
Principal Component ◽  
Archaeological Site ◽  
Archaeological Sites ◽  
Medieval Art ◽  
Archaeological Data ◽  
History Of ◽  
Close Range ◽  
Low Altitude

Unmanned aerial vehicles are currently the most used solution for cultural heritage in the field of close range and low altitude acquisitions. This work shows data acquired by multitemporal and multispectral aerial surveys in the archaeological site of San Vincenzo al Volturno (Molise, Italy). The site is one of the most important medieval archaeological sites in the world. It is a monastic settlement that was particularly rich during the early Middle Ages, and is famous for its two full-frescoed crypts which represent a milestone in the history of medieval art. Thanks to the use of multispectral aerial photography at different times of the year, an area not accessible to archaeological excavation has been investigated. To avoid redundancy of information and reduce the number of data to be analysed, a method based on spectral and radiometric enhancement techniques combined with a selective principal component analysis was used for the identification of useful information. The combination of already published archaeological data and new remote sensing discoveries, has allowed to better define the situation of the abbey during the building phases of the 8th/9th century and 11th century, confirming and adding new data to the assumptions made by archaeologists.

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

scholarly journals Multi-objective parametrization of interatomic potentials for large deformation pathways and fracture of two-dimensional materials

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xu Zhang ◽  
Hoang Nguyen ◽  
Jeffrey T. Paci ◽  
Subramanian K. R. S. Sankaranarayanan ◽  
Jose L. Mendoza-Cortes ◽  
...  
Keyword(s):  
Large Deformation ◽  
Functional Form ◽  
Principal Component ◽  
Interatomic Potentials ◽  
Two Dimensional ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Two Dimensional Materials ◽  
Definition Of

AbstractThis investigation presents a generally applicable framework for parameterizing interatomic potentials to accurately capture large deformation pathways. It incorporates a multi-objective genetic algorithm, training and screening property sets, and correlation and principal component analyses. The framework enables iterative definition of properties in the training and screening sets, guided by correlation relationships between properties, aiming to achieve optimal parametrizations for properties of interest. Specifically, the performance of increasingly complex potentials, Buckingham, Stillinger-Weber, Tersoff, and modified reactive empirical bond-order potentials are compared. Using MoSe2 as a case study, we demonstrate good reproducibility of training/screening properties and superior transferability. For MoSe2, the best performance is achieved using the Tersoff potential, which is ascribed to its apparent higher flexibility embedded in its functional form. These results should facilitate the selection and parametrization of interatomic potentials for exploring mechanical and phononic properties of a large library of two-dimensional and bulk materials.

scholarly journals Development of Performance Evaluation Indicators for Table Grape Packaging Units

2021 ◽  
Vol 13 (4) ◽  
pp. 2177
Author(s):  
Edson Kogachi ◽  
Adonias Ferreira ◽  
Carlos Cavalcante ◽  
Marcelo Embiruçu
Keyword(s):  
Performance Evaluation ◽  
Standard Deviation ◽  
Process Management ◽  
Principal Component ◽  
Table Grape ◽  
Data Envelopment ◽  
Table Grapes ◽  
Speed Performance ◽  
The Cost

In order to improve the process management of table grape packaging, its performance should be evaluated. However, the literature on performance evaluation indicators is scarce. To address this research gap, we propose a method for the development of performance evaluation indicators for table grape packaging units, which are characterized by labor-intensive and highly seasonal production processes in the agro-economic sector. The stages include the following: contextualizing table grape packaging units, selecting the performance objectives, selecting techniques to be used in the development of the indicators, and applying the method to the packaging units of table grapes. The techniques adopted in the development of the indicators aimed at the cost, quality, flexibility, reliability, and speed performance objectives were data envelopment analysis, principal component analysis, quantification of the batch, compliance with the program within the established deadline, and measurement of the execution time of the batch, respectively. The results obtained in the case study demonstrate that the correlations between the performance indicators do not indicate the need to disregard any of them. Furthermore, the standard deviation values for each indicator are similar. Thus, both results of correlations and standard deviation confirm the importance of the indicators chosen for the performance evaluation of table grape packaging.

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