Special Issue "Big Data in Remote Sensing for Urban Mapping"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (29 February 2020).

Special Issue Editors

Dr. Marian-Daniel Iordache
Website
Guest Editor
Flemish Institute for Technological Research, Center for Remote Sensing and Earth Observation Processes (VITO-TAP), Boeretang 200, 2400 Mol, Belgium
Interests: remote sensing; image processing; spectral unmixing; machine learning; land cover/land use classification
Prof. Dr. Begüm Demir
Website
Guest Editor
Remote Sensing Image Analysis (RSiM) Group, Technische Universität Berlin, 10587 Berlin, Germany
Interests: remote sensing; big data processing and analysis; image processing; signal processing; machine learning; deep learning; image retrieval and classification
Special Issues and Collections in MDPI journals
Dr. Claudio Persello
Website
Guest Editor
Assistant Professor, Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, Department of Earth Observation Science, PO Box 217, 7500 AE Enschede, The Netherlands
Interests: Urban remote sensing; machine learning; deep learning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The current unprecedented technological developments in remote sensing are driving innovative research towards new challenges. On one hand, the high quality of modern remote sensing data, often available to users at low or no cost, open large opportunities for research. On the other hand, the abundance of these data implies significant efforts for data handling, (pre-)processing, and interpretation. The complementary sources of information also require new approaches to interconnected data handling and exploitation.

In the field of remote sensing, urban areas are of particular interest, as they imply large agglomerations of individuals in a limited area. The steep increase in the world population brings new challenges related to social, economic, and environmental actions. Remote sensing comes into the scene as a great companion, as it offers a plethora of possibilities to analyze the evolution of urban areas over time, jointly, with a clear view on their actual state. As the available data are plentiful, the focus moves towards improving the methods that exploit such data, in the field of applications as well as data processing.

In this Special Issue, submissions in a broad range of “Big Data in Remote Sensing for Urban Mapping” related research and applications are encouraged. Topics may include but are not limited to:

  • Urban big data processing, analysis and management;
  • Multisensor and multiresolution data analysis/validation for urban mapping;
  • Machine and deep learning for urban dynamics characterization;
  • Land-use and land-cover change monitoring for urban areas;
  • Large-scale 2D and 3D urban modeling;
  • Urban planning with big data evaluation and assessment;
  • Inference of environmental variables (e.g., socioeconomic indicators, population density, life-quality indicators, air pollution) for urban dynamics characterization;
  • Interactive platforms for public access to scientific results on urban mapping.

Dr. Marian-Daniel Iordache
Prof. Begüm Demir
Dr. Claudio Persello
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 papers will be 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 2200 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

  • Urban mapping 
  • Remote sensing 
  • Big data
  • Machine/deep learning
  • Data management and storage 
  • Cloud computing

Published Papers (3 papers)

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Research

Open AccessArticle
A New GPU Implementation of Support Vector Machines for Fast Hyperspectral Image Classification
Remote Sens. 2020, 12(8), 1257; https://doi.org/10.3390/rs12081257 - 16 Apr 2020
Abstract
The storage and processing of remotely sensed hyperspectral images (HSIs) is facing important challenges due to the computational requirements involved in the analysis of these images, characterized by continuous and narrow spectral channels. Although HSIs offer many opportunities for accurately modeling and mapping [...] Read more.
The storage and processing of remotely sensed hyperspectral images (HSIs) is facing important challenges due to the computational requirements involved in the analysis of these images, characterized by continuous and narrow spectral channels. Although HSIs offer many opportunities for accurately modeling and mapping the surface of the Earth in a wide range of applications, they comprise massive data cubes. These huge amounts of data impose important requirements from the storage and processing points of view. The support vector machine (SVM) has been one of the most powerful machine learning classifiers, able to process HSI data without applying previous feature extraction steps, exhibiting a robust behaviour with high dimensional data and obtaining high classification accuracies. Nevertheless, the training and prediction stages of this supervised classifier are very time-consuming, especially for large and complex problems that require an intensive use of memory and computational resources. This paper develops a new, highly efficient implementation of SVMs that exploits the high computational power of graphics processing units (GPUs) to reduce the execution time by massively parallelizing the operations of the algorithm while performing efficient memory management during data-reading and writing instructions. Our experiments, conducted over different HSI benchmarks, demonstrate the efficiency of our GPU implementation. Full article
(This article belongs to the Special Issue Big Data in Remote Sensing for Urban Mapping)
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Open AccessArticle
Urban Nighttime Leisure Space Mapping with Nighttime Light Images and POI Data
Remote Sens. 2020, 12(3), 541; https://doi.org/10.3390/rs12030541 - 06 Feb 2020
Cited by 1
Abstract
Urban nighttime leisure spaces (UNLSs), important urban sites of nighttime economic activity, have created enormous economic and social benefits. Both the physical features (e.g., location, shape, and area) and the social functions (e.g., commercial streets, office buildings, and entertainment venues) of UNLSs are [...] Read more.
Urban nighttime leisure spaces (UNLSs), important urban sites of nighttime economic activity, have created enormous economic and social benefits. Both the physical features (e.g., location, shape, and area) and the social functions (e.g., commercial streets, office buildings, and entertainment venues) of UNLSs are important in UNLS mapping. However, most studies rely solely on census data or nighttime light (NTL) images to map the physical features of UNLSs, which limits UNLS mapping, and few studies perform UNLS mapping from a social function perspective. Point-of-interest (POI) data, which can reflect social activity functions, are needed. As a result, a novel methodological UNLS mapping framework, that integrates NTL images and POI data is required. Consequently, we first extracted high-NTL intensity and high-POI density areas from composite data as areas with high nightlife activity levels. Then, the POI data were analyzed to identify the social functions of leisure spaces revealing that nighttime leisure activities are not abundant in Beijing overall, the total UNLS area in Beijing is 31.08 km2, which accounts for only 0.2% of the total area of Beijing. In addition, the nightlife activities in the central urban area are more abundant than those in the suburbs. The main urban area has the largest UNLS area. Compared with the nightlife landmarks in Beijing established by the government, our results provide more details on the spatial pattern of nighttime leisure activities throughout the city. Our study aims to provide new insights into how multisource data can be leveraged for UNLS mapping to enable researchers to broaden their study scope. This investigation can also help government departments better understand the local nightlife situation to rationally formulate planning and adjustment measures. Full article
(This article belongs to the Special Issue Big Data in Remote Sensing for Urban Mapping)
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Open AccessArticle
High-Resolution Urban Land Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine
Remote Sens. 2019, 11(7), 752; https://doi.org/10.3390/rs11070752 - 28 Mar 2019
Cited by 13
Abstract
Accurate and timely urban land mapping is fundamental to supporting large area environmental and socio-economic research. Most of the available large-area urban land products are limited to a spatial resolution of 30 m. The fusion of optical and synthetic aperture radar (SAR) data [...] Read more.
Accurate and timely urban land mapping is fundamental to supporting large area environmental and socio-economic research. Most of the available large-area urban land products are limited to a spatial resolution of 30 m. The fusion of optical and synthetic aperture radar (SAR) data for large-area high-resolution urban land mapping has not yet been widely explored. In this study, we propose a fast and effective urban land extraction method using ascending/descending orbits of Sentinel-1A SAR data and Sentinel-2 MSI (MultiSpectral Instrument, Level 1C) optical data acquired from 1 January 2015 to 30 June 2016. Potential urban land (PUL) was identified first through logical operations on yearly mean and standard deviation composites from a time series of ascending/descending orbits of SAR data. A Yearly Normalized Difference Vegetation Index (NDVI) maximum and modified Normalized Difference Water Index (MNDWI) mean composite were generated from Sentinel-2 imagery. The slope image derived from SRTM DEM data was used to mask mountain pixels and reduce the false positives in SAR data over these regions. We applied a region-specific threshold on PUL to extract the target urban land (TUL) and a global threshold on the MNDWI mean, and slope image to extract water bodies and high-slope regions. A majority filter with a three by three window was applied on previously extracted results and the main processing was carried out on the Google Earth Engine (GEE) platform. China was chosen as the testing region to validate the accuracy and robustness of our proposed method through 224,000 validation points randomly selected from high-resolution Google Earth imagery. Additionally, a total of 735 blocks with a size of 900 × 900 m were randomly selected and used to compare our product’s accuracy with the global human settlement layer (GHSL, 2014), GlobeLand30 (2010), and Liu (2015) products. Our method demonstrated the effectiveness of using a fusion of optical and SAR data for large area urban land extraction especially in areas where optical data fail to distinguish urban land from spectrally similar objects. Results show that the average overall, producer’s and user’s accuracies are 88.03%, 94.50% and 82.22%, respectively. Full article
(This article belongs to the Special Issue Big Data in Remote Sensing for Urban Mapping)
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