|   | 
Details
   web
Records
Author Sun, L.; Tang, L.; Shao, G.; Qiu, Q.; Lan, T.; Shao, J.
Title (up) A Machine Learning-Based Classification System for Urban Built-Up Areas Using Multiple Classifiers and Data Sources Type Journal Article
Year 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 Salat, H.; Smoreda, Z.; Schlapfer, M.
Title (up) A method to estimate population densities and electricity consumption from mobile phone data in developing countries Type Journal Article
Year 2020 Publication PloS one Abbreviated Journal PLoS One
Volume 15 Issue 6 Pages e0235224
Keywords Remote Sensing
Abstract High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a popular proxy to evaluate the density, activity and social characteristics of a population. They offer additional advantages: they are updated in real-time, include mobility information and record visitors' activity. However, we show with the example of Senegal that the direct correlation between the average phone activity and both the population density and the nighttime lights intensity may be insufficiently high to provide an accurate representation of the situation. There are reasons to expect this, such as the heterogeneity of the market share or the particular granularity of the distribution of cell towers. In contrast, we present a method based on the daily, weekly and yearly phone activity curves and on the network characteristics of the mobile phone data, that allows to estimate more accurately such information without compromising people's privacy. This information can be vital for development and infrastructure planning. In particular, this method could help to reduce significantly the logistic costs of data collection in the particularly budget-constrained context of developing countries.
Address Future Cities Laboratory, Singapore-ETH Centre, ETH Zurich, Singapore, Singapore
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1932-6203 ISBN Medium
Area Expedition Conference
Notes PMID:32603345 Approved no
Call Number GFZ @ kyba @ Serial 3030
Permanent link to this record
 

 
Author Cho, M., Park, R., Yoon, J., Choi, Y., Jeong, J. I., Labzovskii, L., Fu, J. S., Huang, K., Jeong, S., & Kim, B.
Title (up) A missing component of Arctic warming: Black carbon from gas flaring Type Journal Article
Year 2019 Publication Environmental Research Letters Abbreviated Journal
Volume Issue Pages
Keywords Remote Sensing
Abstract Gas flaring during oil extraction over the Arctic region is the primary source of warming-inducing aerosols (e.g., black carbon (BC)) with a strong potential to affect regional climate change. Despite continual BC emissions near the Arctic Ocean via gas flaring, the climatic impacts of BC related to gas flaring remain uncertain. Here, we present simulations of potential gas flaring using an earth system model with comprehensive aerosol physics that to show that increases in BC from gas flaring can potentially explain a significant fraction of Arctic warming. BC emissions from gas flaring over high latitudes contribute to locally confined warming over the source region, especially during the Arctic spring through BC-induced local albedo reduction. This local warming invokes remote and temporally lagging sea-ice melting feedback processes over the Arctic Ocean during winter. Our findings imply that a regional change in anthropogenic aerosol forcing is capable of changing Arctic temperatures in regions far from the aerosol source via time-lagged, sea-ice-related Arctic physical processes. We suggest that both energy consumption and production processes can increase Arctic warming.
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 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number IDA @ intern @ Serial 2645
Permanent link to this record
 

 
Author Ma, X.; Li, C.; Tong, X.; Liu, S.
Title (up) A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data Type Journal Article
Year 2019 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 11 Issue 21 Pages 2516
Keywords Remote Sensing
Abstract Recent advances in the fusion technology of remotely sensed data have led to an increased availability of extracted urban information from multiple spatial resolutions and multi-temporal acquisitions. Despite the existing extraction methods, there remains the challenging task of fully exploiting the characteristics of multisource remote sensing data, each of which has its own advantages. In this paper, a new fusion approach for accurately extracting urban built-up areas based on the use of multisource remotely sensed data, i.e., the DMSP-OLS nighttime light data, the MODIS land cover product (MCD12Q1) and Landsat 7 ETM+ images, was proposed. The proposed method mainly consists of two components: (1) the multi-level data fusion, including the initial sample selection, unified pixel resolution and feature weighted calculation at the feature level, as well as pixel attribution determination at decision level; and (2) the optimized sample selection with multi-factor constraints, which indicates that an iterative optimization with the normalized difference vegetation index (NDVI), the modified normalized difference water index (MNDWI), and the bare soil index (BSI), along with the sample training of the support vector machine (SVM) and the extraction of urban built-up areas, produces results with high credibility. Nine Chinese provincial capitals along the Silk Road Economic Belt, such as Chengdu, Chongqing, Kunming, Xining, and Nanning, were selected to test the proposed method with data from 2001 to 2010. Compared with the results obtained by the traditional threshold dichotomy and the improved neighborhood focal statistics (NFS) method, the following could be concluded. (1) The proposed approach achieved high accuracy and eliminated natural elements to a great extent while obtaining extraction results very consistent to those of the more precise improved NFS approach at a fine scale. The average overall accuracy (OA) and average Kappa values of the extracted urban built-up areas were 95% and 0.83, respectively. (2) The proposed method not only identified the characteristics of the urban built-up area from the nighttime light data and other daylight images at the feature level but also optimized the samples of the urban built-up area category at the decision level, making it possible to provide valuable information for urban planning, construction, and management with high accuracy.
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 2731
Permanent link to this record
 

 
Author Ye, Y.; Xue, X.; Huang, L.; Gan, M.; Tong, C.; Wang, K.; Deng, J.
Title (up) A new perspective to map the supply and demand of artificial night light based on Loujia1-01 and urban big data Type Journal Article
Year 2020 Publication Journal of Cleaner Production Abbreviated Journal Journal of Cleaner Production
Volume 276 Issue Pages 123244
Keywords Remote Sensing
Abstract The notable increase in artificial night light (ANL) induced by the rapid urbanization process has been widely studied, but a deep understanding of the supply and demand status of ANL is still lacking. This paper attempts to map the supply and demand of ANL from the human perspective by using advanced Loujia1-01 nighttime imagery and social media derived population density (PD) data, which provides a new tool for light regulation in urban management. The bivariate clustering based k-means algorithm and template matching technique are integrated to delineate mismatch regions at the block scale to further analyze the underlying reason for unbalanced status. The results showed that the high supply but low demand (HSLD) ANL status was the leading component in the mismatch regions, occupying more than 650,000 ha and mainly occurring in the city center. The HSLD proportion was considerable in terms of public services (44%), commercial (40%), industrial (39%), transportation (56%), and green space areas (53%). Moreover, the HSLD area notably increased 946 ha over time from 18:00 to 22:00. The measurements for validation obtained by field investigation showed highly linear relationship with ANL (R2 = 0.75) and PD (R2 = 0.62), and the mapping results were consistent with the actual conditions. This study reveals the highly unbalanced ANL status, and appeals to planners for the establishment of optimal lighting regulations to alleviate disruptive effects.
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 0959-6526 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 3070
Permanent link to this record