Putting people in the picture: Combining big location-based social media data and remote sensing imagery for enhanced contextual urban information in Shanghai

Jendryke, Michael; Balz, Timo; McClure, Stephen C.; Liao, Mingsheng

doi:10.1016/j.compenvurbsys.2016.10.004

Cited by 60 publications

(32 citation statements)

References 35 publications

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“…Unsupervised thresholding techniques for change-detection using the coherence and intensity characteristics of SAR imagery have been proposed in previous studies [33]. Jendryke et al [34] combined social media messages with SAR images to express human activities and urban changes in Shanghai. The coherence characteristics of SAR images identified urban areas and the changes occurring therein; linking these data to social media messages permitted the identification of human activity occurring in those areas.…”

Section: Sar Coherence and Change-detectionmentioning

confidence: 99%

“…A sudden change in a building, however, will result in a high standard deviation as this will show a huge shift from the norm; thus, indicating a change in coherence for buildings. Other types of polygons, for example, grids or hexagons, can be used for this purpose; however, street blocks are more appropriate in an urban set-up as they are relatable objects in the real world [34]. The same technique was applied to the classified polygons by joining them to the coherence point data and calculating the average coherence over time.…”

Section: Workflowmentioning

confidence: 99%

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Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas

2018

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Rapid, reliable, and continuous information is an essential component in disaster monitoring and management. Remote sensing data could be a solution, but often cannot provide continuous data due to an absence of global coverage and weather and daylight dependency. To overcome these challenges, this study makes use of weather and day/light independent Sentinel-1 data with a global coverage to monitor localized effects of different types of disasters using the Coherence Change-Detection (CCD) technique. Coherence maps were generated from Synthetic Aperture Radar (SAR) images and used to classify areas of change and no change in six study areas. These sites are located in Syria, Puerto Rico, California, and Iran. The study areas were divided into street blocks, and the standard deviation was calculated for the coherence images for each street block over entire image stacks. The study areas were classified by land-use type to reveal the spatial variation in coherence loss after a disaster. While temporal decorrelation exhibits a general loss in coherence over time, disaster occurrence, however, indicates a significant loss in coherence after an event. The variations of each street block from the average coherence for the entire image stack, as measured by a high standard deviation after a particular disaster, is an indication of disaster induced building damage.

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Section: Sar Coherence and Change-detectionmentioning

confidence: 99%

Section: Workflowmentioning

confidence: 99%

Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas

2018

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“…'s work in 2013 [38], future work may increase the resolution again, or combining with multi-spectral and multi-sourced remote sensing imagery data. Social sensing, such as using social media and user generated content as mining source [76], can also be combined or inter-calibrated. The NTL imagery dataset with high resolution can also be the input of many machine learning tasks of the real-world urban planning.…”

Section: Resultsmentioning

confidence: 99%

A Scientometric Visualization Analysis for Night-Time Light Remote Sensing Research from 1991 to 2016

Guan

et al. 2017

Remote Sensing

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Abstract:In this paper, we conducted a scientometric analysis based on the Night-Time Light (NTL) remote sensing related literature datasets retrieved from Science Citation Index Expanded and Social Science Citation Index in Web of Science core collection database. Using the methods of bibliometric and Social Network Analysis (SNA), we drew several conclusions: (1) NTL related studies have become a research hotspot, especially after 2011 when the second generation of NTL satellites, the Suomi National Polar-orbiting Partnership (S-NPP) Satellite with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor was on board. In the same period, the open-access policy of the long historical dataset of the first generation satellite Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) started. (2) Most related studies are conducted by authors from USA and China, and the USA takes the lead in the field. We identified the biggest research communities constructed by co-authorships and the related important authors and topics by SNA. (3) By the visualization and analysis of the topic evolution using the co-word and co-cited reference networks, we can clearly see that: the research topics change from hardware oriented studies to more real-world applications; and from the first generation of the satellite DMSP/OLS to the second generation of satellite S-NPP. Although the Day Night Band (DNB) of the S-NPP exhibits higher spatial and radiometric resolution and better calibration conditions than the first generation DMSP/OLS, the longer historical datasets in DMSP/OLS are still important in long-term and large-scale human activity analysis. (4) In line with the intuitive knowledge, the NTL remote sensing related studies display stronger connections (such as interpretive frame, context, and academic purpose) to the social sciences than the general remote sensing discipline. The citation trajectories are visualized based on the dual-maps, thus the research preferences for combining the environmental, ecological, economic, and political science disciplines are clearly exhibited. Overall, the picture of the NTL remote sensing research is presented from the scientist-level, topic-level, and discipline-level interactions. Based on these analyses, we also discuss the possible trends in the future work, such as combining NTL studies with social science research and social media data.

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“…Paid and free data from online social media sources and remote sensing cater near-real-time data. Such data have been increasingly used for a huge range of applications including human settlement mapping [9,10], land use and land cover mapping [11], understanding socio-economic conditions [12], tourism studies [13][14][15], etc. In recent years, many studies have proposed various ways to discover tourism destinations and activities.…”

Section: Introductionmentioning

confidence: 99%

Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

et al. 2019

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Easy, economical, and near-real-time identification of tourism areas of interest is useful for tourism planning and management. Numerous studies have been accomplished to analyze and evaluate the tourism conditions of a place using free and near-real-time data sources such as social media. This study demonstrates the potential of volunteered geographic information, mainly Twitter and OpenStreetMap, for discovering tourism areas of interest. Active tweet clusters generated using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm and building footprint information are used to identify touristic places that ensure the availability of basic essential facilities for travelers. Furthermore, an investigation is made to examine the usefulness of nighttime light remotely sensed data to recognize such tourism areas. The study successfully discovered important tourism areas in urban and remote regions in Nepal which have relatively low social media penetration. The effectiveness of the proposed framework is examined using the F1 measure. The accuracy assessment showed F1 score of 0.72 and 0.74 in the selected regions. Hence, the outcomes of this study can provide a valuable reference for various stakeholders such as tourism planners, urban planners, and so on.

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Putting people in the picture: Combining big location-based social media data and remote sensing imagery for enhanced contextual urban information in Shanghai

Cited by 60 publications

References 35 publications

Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas

Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas

A Scientometric Visualization Analysis for Night-Time Light Remote Sensing Research from 1991 to 2016

Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

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