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Integrating Remote Sensing and Social Sensing

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 20423

Special Issue Editors

Department of Geography, Penn State University, State College, PA 16801, USA
Interests: spatio-temporal remote sensing; social media big data related to environmental hazards and renewable energy
Remote Sensing Group, Oak Ridge National Laboratory, P.O. Box 2008, 1 Bethel Valley, Road Oak Ridge, TN 37831-6134, USA
Interests: scaling and reproducibility; spatiotemporal modeling; geoifnormatics; risk assessment; infrastructure and community resilience; risk communication; spatial decision support system; remote sensing applications; data mining; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The volume of big data available via remote and citizen sensing has grown dramatically in recent years. These data sets available from disparate sources (e.g., satellite sensors, drones, aerial robotics, geotagged images from social media, GPS check-in records from vehicles, mobile phone records, etc.) provide unprecedented degrees of detail about regions, cities and human activities at varying spatial and temporal resolutions. The heterogenous structure of these data sets combined with their near real-time availability poses unique challenges and opportunities in integrating remotely sensed and socially sensed data for social science applications like characterizing human activity patterns and inferring urban land information.

Because of the availability of enormous volume of data, regional and urban analysis is focused on extracting information from these diverse data in a timely manner. Although existing urban and regional development theories are still used to generate information by fusing remotely and socially sensed data, new theoretical, technological and conceptual approaches and techniques need to be developed to address the challenges resulting from using the new generation of big data for urban analytics.

We solicit articles for a special issue in Remote Sensing that advance theories, methods, and/or applications to improve the integration of remote sensing and social sensing as well as to extract information from fused data to help with different urban applications. This special issue is scheduled to be published in conjunction with the 27th International Conference on Geoinformatics (2019 CPGIS Annual Conference) in July 1-3, 2019, Sydney, Australia, and International Conference on Urban Computing and Smart Planning in September 27-29, 2019, Nanjing, China.

Dr. Xinyue Ye
Dr. Changshan Wu
Dr. Guido Cervone
Dr. Bandana Kar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Remote sensing algorithms 
  • Spatial social network
  • Cross-domain data fusion 
  • Disaster response 
  • Remote sensing applications in urban environments

Published Papers (5 papers)

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Research

18 pages, 7600 KiB  
Article
Integration of Crowdsourced Images, USGS Networks, Remote Sensing, and a Model to Assess Flood Depth during Hurricane Florence
Remote Sens. 2020, 12(5), 834; https://doi.org/10.3390/rs12050834 - 05 Mar 2020
Cited by 13 | Viewed by 4179
Abstract
Crowdsourced environmental data have the potential to augment traditional data sources during disasters. Traditional sensor networks, satellite remote sensing imagery, and models are all faced with limitations in observational inputs, forecasts, and resolution. This study integrates flood depth derived from crowdsourced images with [...] Read more.
Crowdsourced environmental data have the potential to augment traditional data sources during disasters. Traditional sensor networks, satellite remote sensing imagery, and models are all faced with limitations in observational inputs, forecasts, and resolution. This study integrates flood depth derived from crowdsourced images with U.S. Geological Survey (USGS) ground-based observation networks, a remote sensing product, and a model during Hurricane Florence. The data sources are compared using cross-sections to assess flood depth in areas impacted by Hurricane Florence. Automated methods can be used for each source to classify flooded regions and fuse the dataset over common grids to identify areas of flooding. Crowdsourced data can play a major role when there are overlaps of sources that can be used for validation as well providing improved coverage and resolution. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Social Sensing)
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26 pages, 6075 KiB  
Article
Knowledge and Data-Driven Mapping of Environmental Status Indicators from Remote Sensing and VGI
Remote Sens. 2020, 12(3), 495; https://doi.org/10.3390/rs12030495 - 04 Feb 2020
Cited by 8 | Viewed by 3385
Abstract
The paper proposes a transparent approach for mapping the status of environmental phenomena from multisource information based on both soft computing and machine learning. It is transparent, intended as human understandable as far as the employed criteria, and both knowledge and data-driven. It [...] Read more.
The paper proposes a transparent approach for mapping the status of environmental phenomena from multisource information based on both soft computing and machine learning. It is transparent, intended as human understandable as far as the employed criteria, and both knowledge and data-driven. It exploits remote sensing experts’ interpretations to define the contributing factors from which partial evidence of the environmental status are computed by processing multispectral images. Furthermore, it computes an environmental status indicator (ESI) map by aggregating the partial evidence degrees through a learning mechanism, exploiting volunteered geographic information (VGI). The approach is capable of capturing the specificities of local context, as well as to cope with the subjectivity of experts’ interpretations. The proposal is applied to map the status of standing water areas (i.e., water bodies and rivers and human-driven or natural hazard flooding) using multispectral optical images by ESA Sentinel-2 sources. VGI comprises georeferenced observations created both in situ by agronomists using a mobile application and by photointerpreters interacting with a geographic information system (GIS) using several information layers. Results of the validation experiments were performed in three areas of Northern Italy characterized by distinct ecosystems. The proposal showed better performances than traditional methods based on single spectral indexes. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Social Sensing)
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15 pages, 10254 KiB  
Article
The Exploration of Urban Material Anabolism Based on RS and GIS Methods: Case Study in Jinchang, China
Remote Sens. 2020, 12(3), 370; https://doi.org/10.3390/rs12030370 - 23 Jan 2020
Cited by 4 | Viewed by 3016 | Retraction
Abstract
As an open artificial ecosystem, the development of a city requires the continuous input and output of material and energy, which is called urban metabolism, and includes catabolic (material-flow) and anabolic (material-accumulation) processes. Previous studies have focused on the catabolic and ignored the [...] Read more.
As an open artificial ecosystem, the development of a city requires the continuous input and output of material and energy, which is called urban metabolism, and includes catabolic (material-flow) and anabolic (material-accumulation) processes. Previous studies have focused on the catabolic and ignored the anabolic process due to data and technology problems. The combination of remote-sensing technology and high-resolution satellite images facilitates the estimation of cumulative material amounts in urban systems. This study focused on persistent accumulation, which is the metabolic response of urban land use/urban land expansion, building stock, and road stock to land-use changes. Building stock is an extremely cost-intensive and long-lived component of cumulative metabolism. The study measured building stocks of Jinchang, China’s nickel capital by using remote-sensing images and field-research data. The development of the built environment could be analyzed by comparing the stock of buildings on maps representing different time periods. The results indicated that material anabolism in Jinchang is a distance-dependent function, where the amounts and rates of material anabolism decrease with changes in distance to the central business district (CBD) and city administration center (CAC). The cumulative metabolic rate and cumulative total metabolism were observed to be increasing, however, the growth rate has decreased. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Social Sensing)
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20 pages, 11707 KiB  
Article
Integrating Activity-Based Geographic Information and Long-Term Remote Sensing to Characterize Urban Land Use Change
Remote Sens. 2019, 11(24), 2965; https://doi.org/10.3390/rs11242965 - 11 Dec 2019
Cited by 4 | Viewed by 2611
Abstract
The land use structure is a key component to understand the complexity of urban systems because it provides a snapshot of urban dynamics and how people use space. This paper integrates socially sensed activity data with a remotely sensed land cover product in [...] Read more.
The land use structure is a key component to understand the complexity of urban systems because it provides a snapshot of urban dynamics and how people use space. This paper integrates socially sensed activity data with a remotely sensed land cover product in order to infer urban land use and its changes over time. We conducted a case study in the Washington D.C.–Baltimore metropolitan area to identify the pattern of land use change from undeveloped to developed land, including residential and non-residential uses for a period covering 1986–2008. The proposed approach modeled physical and behavioral features of land parcels from a satellite-based impervious surface cover change product and georeferenced Tweets, respectively. A model assessment with random forests classifiers showed that the proposed classification workflow could classify residential and non-residential land uses at an accuracy of 81%, 4% better than modeling the same land uses from physical features alone. Using the timestamps of the impervious surface cover change product, the study also reconstructed the timeline of the identified land uses. The results indicated that the proposed approach was capable of mapping detailed land use and change in an urban region, and represents a new and viable way forward for urban land use surveying that could be especially useful for surveying and tracking changes in cities where traditional approaches and mapping products (i.e., from remote sensing products) may have a limited capacity to capture change. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Social Sensing)
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20 pages, 2322 KiB  
Article
Model Fusion for Building Type Classification from Aerial and Street View Images
Remote Sens. 2019, 11(11), 1259; https://doi.org/10.3390/rs11111259 - 28 May 2019
Cited by 61 | Viewed by 5823
Abstract
This article addresses the question of mapping building functions jointly using both aerial and street view images via deep learning techniques. One of the central challenges here is determining a data fusion strategy that can cope with heterogeneous image modalities. We demonstrate that [...] Read more.
This article addresses the question of mapping building functions jointly using both aerial and street view images via deep learning techniques. One of the central challenges here is determining a data fusion strategy that can cope with heterogeneous image modalities. We demonstrate that geometric combinations of the features of such two types of images, especially in an early stage of the convolutional layers, often lead to a destructive effect due to the spatial misalignment of the features. Therefore, we address this problem through a decision-level fusion of a diverse ensemble of models trained from each image type independently. In this way, the significant differences in appearance of aerial and street view images are taken into account. Compared to the common multi-stream end-to-end fusion approaches proposed in the literature, we are able to increase the precision scores from 68% to 76%. Another challenge is that sophisticated classification schemes needed for real applications are highly overlapping and not very well defined without sharp boundaries. As a consequence, classification using machine learning becomes significantly harder. In this work, we choose a highly compact classification scheme with four classes, commercial, residential, public, and industrial because such a classification has a very high value to urban geography being correlated with socio-demographic parameters such as population density and income. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Social Sensing)
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