scholarly journals The Relation of Fault Fracture Density with the Residual Gravity; case study in Muria

2021 ◽  
Vol 1 (2) ◽  
pp. 41-47
Author(s):  
Fajar Rizki Widiatmoko ◽  
Ratih Hardini Kusuma Putri ◽  
Huzaely Latief Sunan
Keyword(s):  
Analytical Method ◽  
Ground Truth ◽  
Fracture Density ◽  
Ground Truth Data ◽  
Reservoir Area ◽  
Residual Gravity ◽  
Magmatic Body ◽  
Preliminary Study

The usages of the FFD analytical method massively are utilized during the last decade, especially in the geothermal preliminary study that can show the prospect reservoir area. This article discusses the correlation of the FFD value with the residual gravity value that is assumed as an indication of the underneath magmatic body. The correlation of FFD value with residual gravity value is applied in Muria mountain. Muria is classified as the volcano body that contains the magmatic body, also exist Genuk volcano and Patiayam hill around Muria. The correlation shows that FFD value and residual gravity value have a relation, but especially for the uninfluenced by structural activity has a low value of FFD. The correlation of FFD and residual gravity is double-checked with the ground truth data, it showing the proof relation. This way of methodology may use for finding the underneath magmatic body, especially applied to the surface that has not been influenced by structural activity.

scholarly journals Simple Method for Land-Cover Mapping by Combining Multi-Temporal Landsat ETM+ Images and Systematically Sampled Ground Truth Data : A Case Study in Japan

2012 ◽  
Vol 18 (1) ◽  
pp. 77-85
Author(s):  
Shinya Tanaka ◽  
Tomoaki Takahashi ◽  
Hideki Saito ◽  
Yoshio Awaya ◽  
Toshiro Iehara ◽  
...  
Keyword(s):  
Land Cover ◽  
Ground Truth ◽  
Land Cover Mapping ◽  
Simple Method ◽  
Ground Truth Data ◽  
Multi Temporal

A Total Error Approach for Validating Event Data

2021 ◽  
pp. 000276422110216
Author(s):  
Scott Althaus ◽  
Buddy Peyton ◽  
Dan Shalmon
Keyword(s):  
Gold Standard ◽  
Total Error ◽  
Ground Truth ◽  
Event Extraction ◽  
Event Data ◽  
Actual Distribution ◽  
Ground Truth Data ◽  
Total Event ◽  
Conflict Research

Understanding how useful any particular set of event data might be for conflict research requires appropriate methods for assessing validity when ground truth data about the population of interest do not exist. We argue that a total error framework can provide better leverage on these critical questions than previous methods have been able to deliver. We first define a total event data error approach for identifying 19 types of error that can affect the validity of event data. We then address the challenge of applying a total error framework when authoritative ground truth about the actual distribution of relevant events is lacking. We argue that carefully constructed gold standard datasets can effectively benchmark validity problems even in the absence of ground truth data about event populations. To illustrate the limitations of conventional strategies for validating event data, we present a case study of Boko Haram activity in Nigeria over a 3-month offensive in 2015 that compares events generated by six prominent event extraction pipelines—ACLED, SCAD, ICEWS, GDELT, PETRARCH, and the Cline Center’s SPEED project. We conclude that conventional ways of assessing validity in event data using only published datasets offer little insight into potential sources of error or bias. Finally, we illustrate the benefits of validating event data using a total error approach by showing how the gold standard approach used to validate SPEED data offers a clear and robust method for detecting and evaluating the severity of temporal errors in event data.

Assessment of Forest Encroachment Using Remote Sensing Technique. Case Study: Mentigi Forest Reserve, Cameron Highlands

2015 ◽  
Vol 73 (5) ◽  
Author(s):  
Mohd Hayyi Mat Zin ◽  
Baharin Ahmad
Keyword(s):  
Remote Sensing ◽  
Open Space ◽  
Ground Truth ◽  
Forest Reserve ◽  
Ground Truth Data ◽  
Remote Sensing Technique ◽  
Cameron Highlands ◽  
Hybrid Classification ◽  
Spot 5

Agriculture is one of the biggest and profitable activities in Cameron Highlands, Malaysia. High quality plantation products such as tea, vegetable, fruits and flower have high demand in Malaysia. These profitable activities however have caused illegal agriculture and farming. Farmers tend to extent their farm by encroaching government lands and take advantage on any open space for illegal farming. These encroachment activities have affected forest reserve area including Mentigi Forest Reserve (MFR). This study is to identify and evaluate the encroachment activities within MFR area using multiple remote sensing datasets (SPOT 5 and IKONOS). Cadastral parcel map was used to delineate the MFR area and also provide the actual size of MFR area. Hybrid classification method was used on remote sensing image to classify the land-cover in the study area. Ground truth data from field observation were used to assess the accuracy of the classification. Results of this study showed the technique used was able to identify encroachment activities such as agriculture and development. The total encroachment area in MFR was about 2.8 ha in 2001 and has increased to about 7.3 ha in 2010. These encroachment areas represent 0.39% and 1.46% respectively. This area might be small but it may affect the forest ecosystem which can lead to hazardous natural disaster if not well monitored and managed.

Graph Convolutional Network-based Model for Incident-related Congestion Prediction: A Case Study of Shanghai Expressways

2021 ◽  
Vol 12 (3) ◽  
pp. 1-22
Author(s):  
Xi Wang ◽  
Yibo Chai ◽  
Hui Li ◽  
Wenbin Wang ◽  
Weishan Sun
Keyword(s):  
Traffic Control ◽  
Traffic Congestion ◽  
Local Governments ◽  
Ground Truth ◽  
Small Scale ◽  
Convolutional Network ◽  
Ground Truth Data ◽  
Real Time Traffic ◽  
Management And Development

Traffic congestion has become a significant obstacle to the development of mega cities in China. Although local governments have used many resources in constructing road infrastructure, it is still insufficient for the increasing traffic demands. As a first step toward optimizing real-time traffic control, this study uses Shanghai Expressways as a case study to predict incident-related congestions. Our study proposes a graph convolutional network-based model to identify correlations in multi-dimensional sensor-detected data, while simultaneously taking into account environmental, spatiotemporal, and network features in predicting traffic conditions immediately after a traffic incident. The average accuracy, average AUC, and average F-1 score of the predictive model are 92.78%, 95.98%, and 88.78%, respectively, on small-scale ground-truth data. Furthermore, we improve the predictive model’s performance using semi-supervised learning by including more unlabeled data instances. As a result, the accuracy, AUC, and F-1 score of the model increase by 2.69%, 1.25%, and 4.72%, respectively. The findings of this article have important implications that can be used to improve the management and development of Expressways in Shanghai, as well as other metropolitan areas in China.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

scholarly journals Evaluation of ESV Change under Urban Expansion Based on Ecological Sensitivity: A Case Study of Three Gorges Reservoir Area in China

2021 ◽  
Vol 13 (15) ◽  
pp. 8490
Author(s):  
Hongjie Peng ◽  
Lei Hua ◽  
Xuesong Zhang ◽  
Xuying Yuan ◽  
Jianhao Li
Keyword(s):  
Three Gorges Reservoir ◽  
Urban Expansion ◽  
Urban Land ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Ecological Sensitivity ◽  
Reservoir Area ◽  
Green Development ◽  
Source Data

In recent years, ecosystem service values (ESV) have attracted much attention. However, studies that use ecological sensitivity methods as a basis for predicting future urban expansion and thus analyzing spatial-temporal change of ESV are scarce in the region. In this study, we used the CA-Markov model to predict the 2030 urban expansion under ecological sensitivity in the Three Gorges reservoir area based on multi-source data, estimations of ESV from 2000 to 2018 and predictions of ESV losses from 2018 to 2030. Research results: (i) In the concept of green development, the ecological sensitive zone has been identified in Three Gorges reservoir area; it accounts for about 35.86% of the study area. (ii) It is predicted that the 2030 urban land will reach 211,412.51 ha by overlaying the ecological sensitive zone. (iii) The total ESV of Three Gorges Reservoir area showed an increasing trend from 2000 to 2018 with growth values of about USD 3644.26 million, but the ESVs of 16 districts were decreasing, with Dadukou and Jiangbei having the highest reductions. (iv) New urban land increases by 80,026.02 ha from 2018 to 2030. The overall ESV losses are about USD 268.75 million. Jiulongpo, Banan and Shapingba had the highest ESV losses.

scholarly journals Changes of Walking Activity During the First Cycle Phases of COVID-19 Pandemic: A Case Study of Seoul, Korea

2021 ◽  
pp. 193758672110265
Author(s):  
Youngjun Park ◽  
Haekwon Chung ◽  
Sohyun Park
Keyword(s):  
Public Health ◽  
Physical Inactivity ◽  
Voluntary Participation ◽  
Walking Activity ◽  
Significant Difference ◽  
Before And After ◽  
Acceleration And Deceleration ◽  
Preliminary Study ◽  
The Government

Aim: This study explores the changes in regular walking activities during the phases of the pandemic. Background: With the spread of COVID-19 transmission, people are refraining from going out, reducing their physical activity. In South Korea, COVID-19 broke out in the 4th week of 2020 and experienced the first cycle phases of the pandemic, such as outbreak, widespread, and decline. In response to the pandemic, the government encouraged voluntary participation in social distancing campaigns, and people reduced their outside activities. Methods: This article examines the decrease and increase of the Prevalence of Regular Walking (≥30 min of moderate walking a day, on ≥5 days a week) by the COVID-19 phases. This study is based on weekly walking data for 15 weeks in 2020, via the smartphone healthcare app, which is managed by 25 public health offices of the Seoul government. Results: According to the findings, the level of prevalence of regular walking (PRW) has a significant difference before and after the outbreak, and every interval of the four-stage COVID-19 phases, that is, pre-pandemic, initiation, acceleration, and deceleration. The level of PRW sharply decreased during initiation and acceleration intervals. In the deceleration interval of COVID-19, the PRW kept increasing, but it has not yet reached the same level as the previous year when the COVID-19 did not exist. Conclusions: As a preliminary study, this study explains empirically how COVID-19 changed PRW in Seoul. It would be helpful to enhance our understanding of the changes in physical inactivity in the pandemic period.

scholarly journals Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

2020 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Wen-Hao Su ◽  
Jiajing Zhang ◽  
Ce Yang ◽  
Rae Page ◽  
Tamas Szinyei ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Ground Truth ◽  
Wheat Breeding ◽  
Head Blight ◽  
Detection Rates ◽  
Ground Truth Data ◽  
Resistant Cultivars ◽  
Feature Pyramid ◽  
Rater Error ◽  
Wheat Lines

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

scholarly journals Classification of Cattle Behaviours Using Neck-Mounted Accelerometer-Equipped Collars and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4050
Author(s):  
Dejan Pavlovic ◽  
Christopher Davison ◽  
Andrew Hamilton ◽  
Oskar Marko ◽  
Robert Atkinson ◽  
...  
Keyword(s):  
Neural Network ◽  
Model Performance ◽  
Ground Truth ◽  
Practical Implementation ◽  
Ground Truth Data ◽  
Battery Lifetime ◽  
Implementation Challenges ◽  
Memory Footprint ◽  
Commercial Farms ◽  
Using Data

Monitoring cattle behaviour is core to the early detection of health and welfare issues and to optimise the fertility of large herds. Accelerometer-based sensor systems that provide activity profiles are now used extensively on commercial farms and have evolved to identify behaviours such as the time spent ruminating and eating at an individual animal level. Acquiring this information at scale is central to informing on-farm management decisions. The paper presents the development of a Convolutional Neural Network (CNN) that classifies cattle behavioural states (`rumination’, `eating’ and `other’) using data generated from neck-mounted accelerometer collars. During three farm trials in the United Kingdom (Easter Howgate Farm, Edinburgh, UK), 18 steers were monitored to provide raw acceleration measurements, with ground truth data provided by muzzle-mounted pressure sensor halters. A range of neural network architectures are explored and rigorous hyper-parameter searches are performed to optimise the network. The computational complexity and memory footprint of CNN models are not readily compatible with deployment on low-power processors which are both memory and energy constrained. Thus, progressive reductions of the CNN were executed with minimal loss of performance in order to address the practical implementation challenges, defining the trade-off between model performance versus computation complexity and memory footprint to permit deployment on micro-controller architectures. The proposed methodology achieves a compression of 14.30 compared to the unpruned architecture but is nevertheless able to accurately classify cattle behaviours with an overall F1 score of 0.82 for both FP32 and FP16 precision while achieving a reasonable battery lifetime in excess of 5.7 years.

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