|   | 
Details
   web
Records
Author Adrian, J.; Hue, D.; Porte, S.; Le Brun, J.
Title Validation of the driver ecological glare test Type Journal Article
Year (down) 2020 Publication Journal of Safety Research Abbreviated Journal Journal of Safety Research
Volume Issue Pages in press
Keywords Night vision
Abstract The present study proposes to validate the Driver Ecological Glare Test (DEGT), a test developed to measure the benefit of a headlight glare Advanced Driver Assistance System (ADAS), by comparing it to a laboratory glare test. Method: Twenty-four participants, aged from 55 to 70 years, were recruited to complete a visual examination, including monocular halo size measurement for both eyes using Vision Monitor device (MonCv3; Metrovision). An on-field evaluation took place at night at the UTAC CERAM test track to obtain disability glare measures using the DEGT. Results: A significant correlation was found between the two glare tests and Bland-Altman analysis reveals a good agreement with a bias of 73.7 arcmin between the halo size measurements obtained from the DEGT and Vision Monitor. The results of the present study demonstrate that the DEGT is a valid method to test halo size and is adapted to evaluate the benefits of an antiglare device for drivers in an ecological situation.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0022-4375 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2797
Permanent link to this record
 

 
Author Andrade-Núñez, M.J.; Aide, T.M.
Title The Socio-Economic and Environmental Variables Associated with Hotspots of Infrastructure Expansion in South America Type Journal Article
Year (down) 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 12 Issue 1 Pages 116
Keywords Remote Sensing
Abstract The built environment, defined as all human-made infrastructure, is increasing to fulfill the demand for human settlements, productive systems, mining, and industries. Due to the profound direct and indirect impacts that the built environment produces on natural ecosystems, it is considered a major driver of land change and biodiversity loss, and a major component of global environmental change. In South America, a global producer of minerals and agricultural commodities, and a region with many biodiversity hotspots, infrastructure expanded considerably between 2001 and 2011. This expansion occurred mainly in rural areas, towns, and sprawling suburban areas that were not previously developed. Herein, we characterized the areas of major infrastructure expansion between 2001 and 2011 in South America. We used nighttime light data, land use maps, and socio-economic and environmental variables to answer the following questions: (1) Where are the hotspots of infrastructure expansion located? and (2) What combination of socio-economic and environmental variables are associated with infrastructure expansion? Hotspots of infrastructure expansion encompass 70% (337,310 km2) of the total infrastructure expansion occurring between 2001 and 2011 across South America. Urban population and economic growth, mean elevation, and mean road density were the main variables associated with the hotspots, grouping them into eight clusters. Furthermore, within the hotspots, woody vegetation increased around various urban centers, and several areas showed a large increase in agriculture. Investments in large scale infrastructure projects, and the expansion and intensification of productive systems (e.g., agriculture and meat production) play a dominant role in the increase of infrastructure across South America. We expect that under the current trends of globalization and land changes, infrastructure will continue increasing and expanding into no-development areas and remote places. Therefore, to fully understand the direct and indirect impacts of land use change in natural ecosystems studies of infrastructure need to expand to areas beyond cities. This will provide better land management alternatives for the conservation of biodiversity as well as peri-urban areas across South America.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2798
Permanent link to this record
 

 
Author Sun, L.; Tang, L.; Shao, G.; Qiu, Q.; Lan, T.; Shao, J.
Title A Machine Learning-Based Classification System for Urban Built-Up Areas Using Multiple Classifiers and Data Sources Type Journal Article
Year (down) 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 12 Issue 1 Pages 91
Keywords Remote Sensing
Abstract Information about urban built-up areas is important for urban planning and management. However, obtaining accurate information about urban built-up areas is a challenge. This study developed a general-purpose built-up area intelligent classification (BAIC) system that supports various types of data and classifiers. All of the steps in the BAIC were implemented using Python modules including Numpy, Pandas, matplotlib, and scikit-learn. We used the BAIC to conduct a classification experiment that involved seven types of input data; namely, Point of Interest (POI), Road Network (RN), nighttime light (NTL), a combination of POI and RN data (POIRN), a combination of POI and NTL data (POINTL), a combination of RN and NTL data (RNNTL), and a combination of POI, RN, and NTL data (POIRNNTL), and five classifiers, namely, Logistic Regression (LR), Decision Tree (DT), Random Forests (RF), Gradient Boosted Decision Trees (GBDT), and AdaBoost. The results show the following: (1) among the 35 combinations of the five classifiers and seven types of input data, the overall accuracy (OA) ranged from 76 to 89%, F1 values ranged from 0.73 to 0.86, and the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.83 to 0.95. The largest F1 value and OA were obtained using the POIRNNTL data and AdaBoost, while the largest AUC was obtained using POIRNNTL and POINTL data against AdaBoost, LR, and RF; and (2) the advantages of the BAIC include its support for multi-source input data, its objective accuracy assessment, and its robust classifiers. The BAIC can quickly and efficiently realize the automatic classification of urban built-up areas at a reasonably low cost and can be readily applied to other urban areas in the world where any kind of POI, RN, or NTL data coverage is available. The results of this study are expected to provide timely and effective reference information for urban planning and urban management departments, and could also potentially be used to develop large-scale maps of urban built-up areas in the future.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2800
Permanent link to this record
 

 
Author Chang, Y.; Wang, S.; Zhou, Y.; Wang, L.; Wang, F.
Title A Novel Method of Evaluating Highway Traffic Prosperity Based on Nighttime Light Remote Sensing Type Journal Article
Year (down) 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 12 Issue 1 Pages 102
Keywords Remote Sensing
Abstract As the backbone and arteries of a comprehensive transportation network, highways play an important role in improving people’s living standards and promoting economic growth. However, globally, there is limited quantifiable data evaluating the highway traffic state, characteristics, and performance. From the 1960s to the present, remote sensing has been regarded as the most effective technology for long-term and large-scale monitoring of surface information. However, how to reflect the dynamic “flow” information of traffic with a static remote sensing image has always been a difficult problem that is hard to solve in the field. This study aims to construct a method of evaluating highway traffic prosperity using nighttime remote sensing. First, based on nighttime light data that indicate social and economic activities, a highway-oriented method was proposed to extract highway nighttime light data from 2015 annual nighttime light data of the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (SNPP-VIIRS). Subsequently, Pearson correlation analysis was used to fit the relationship between freeway traffic flow volume and freeway nighttime light at the provincial level. The results showed that Pearson Correlation Coefficient of freeway nighttime light and freeway traffic flow volume for coach and truck are 0.905 and 0.731, respectively, which are higher than between freeway traffic flow volume for coach and truck and total nighttime light (0.593 and 0.516, respectively). A new index—Highway Nighttime Traffic Prosperity Index (HNTPI)—was proposed to evaluate highway traffic across China. The results showed that HNTPI has a strong correspondence with socio-economic parameters. The Pearson Correlation Coefficient of HNTPI and gross domestic product (GDP) per capita, consumption per capita, and population are 0.772, 0.895, and 0.968, respectively. There is a huge spatial heterogeneity in China nighttime traffic, the prosperity degree of highway traffic in developed coastal areas is obviously higher than that inland. The national general highway is the most prosperous highway at night and the national general highway nighttime prosperity of Shanghai reached 22.34%. This research provides basic data for the long-term monitoring and evaluation of regional traffic operation at night and research on the correlation between regional highway construction and the economy.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2801
Permanent link to this record
 

 
Author Ogando-Martínez, A.; Troncoso-Pastoriza, F.; Eguía-Oller, P.; Granada-Álvarez, E.; Erkoreka, A.
Title Model Calibration Methodology to Assess the Actual Lighting Conditions of a Road Infrastructure Type Journal Article
Year (down) 2020 Publication Infrastructures Abbreviated Journal Infrastructures
Volume 5 Issue 1 Pages 2
Keywords Lighting
Abstract Street lighting plays an important role in the comfort and safety of drivers and pedestrians, so the control and management of the lighting systems operation and consumption is an essential service for a city. In this document, a methodology is presented to calibrate lighting models in order to assess the lighting performance through simulation techniques. The objective of this calibration is to identify the maintenance factor of the street lamps, determine the real average luminance coefficient of the road pavement and adapt the reflection properties of the road material. The method is applied in three stages and is based on the use of Radiance and GenOpt software suits for the modeling, simulation, and calibration of lighting scenes. The proposed methodology achieves errors as low as 13% for the calculation of illuminance and luminance, evincing its potential to assess the actual lighting conditions of a road.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2412-3811 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2802
Permanent link to this record