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Remote Sensing Big Data for Improving the Urban Environment

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 5429

Special Issue Editors


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Guest Editor
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: urban remote sensing; high resolution remote sensing; remote sensing in environment; image processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Earth Observation Science, University of Twente, 7500 Enschede, The Netherlands
Interests: remote sensing; earth observation; geoinformatics; Environment; image processing
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Guest Editor
Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing of geological disasters; remote sensing of the environment; data mining in GIS applications; machine leaning and data mining in multi-platform remote sensing; InSAR technology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Mathematics Informatics and Computational Science, Solomon Mahlangu College of Science and Education, Sokoine University of Agriculture, P.O. Box 3038, Morogoro, Tanzania
Interests: GIS; earth observation; land use; land cover; remote sensing; computer science

Special Issue Information

Dear Colleagues,

Global urban areas have been rapidly expanding in recent decades, especially in developing countries. The urbanization rate has been projected to reach 60% by 2030. Urban expansion inevitably leads to conversion of natural and semi-natural ecosystems into impervious surfaces, and thus becomes the most widespread anthropogenic cause of increased urban environmental degradation. Rapid urbanization, along with manufacturing industries and the large number of vehicles, has resulted in serious environmental problems termed “urban diseases”, and include increased vulnerability to natural hazards, natural vegetation cover decline and arable land loss, urban heat islands, air pollution, hydrological circle alteration, and biotic homogenization. Although urban land covers only less than 3% of the global terrestrial surface, their marked effects on environmental conditions is becoming increasingly serious. Since urban ecosystems are strongly influenced by anthropogenic activities, a considerable amount of research has been conducted all around the world to understand the spatial patterns, driving forces, and ecological and social consequences of urbanization. This is crucial not only for characterizing the ecological consequences of urbanization but also for developing effective economic, social, and environmental policies in order to mitigate its adverse impacts.

Remote sensing has been widely used for investigating the urban environment and its associated drivers during the urbanization process, as it can quickly and frequently monitor large area surface change with lower cost compared to field survey or in situ measurements. Digital archives of remotely sensed data provide an excellent opportunity to study historical urban changes and to relate their spatiotemporal patterns to environmental and human factors. With the rapid development of Earth observation techniques, it has become convenient to obtain a large number of remotely sensed images over a certain area, taken at different times and from hundreds of Earth observation platforms. However, this brings challenges to researchers with regard to the timely processing of remote sensing big data as well as the rapid transformation of this data into information and knowledge.

In consideration of these points, this Special Issue of Sensors aims to present novel studies exploiting remote sensing big data to monitor and improve the urban environment, in addition to showing the potential of remote sensing in developing sustainable cities. This includes coverage of the following topics:

(1) Urban remote sensing big data

(2) Remote sensing information interpretation

(3) Urban expansion, land use/land cover dynamics, and associated environmental consequences

(4) Remote sensing of urban water quality

(5) Remote sensing of urban thermal environment

(6) Remote sensing of urban geological environment

Prof. Dr. Zhenfeng Shao
Prof. emer John L. van Genderen
Dr. Cheng Zhong
Dr. Neema S. Sumari
Guest Editors

Manuscript Submission Information

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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. Sensors 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 2600 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 remote sensing
  • urban big data
  • urban environment
  • urban expansion
  • urban water quality
  • urban thermal environment
  • urban geological environment

Published Papers (2 papers)

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20 pages, 5708 KiB  
Article
An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images
by Fei Sun, Fang Fang, Run Wang, Bo Wan, Qinghua Guo, Hong Li and Xincai Wu
Sensors 2020, 20(22), 6699; https://doi.org/10.3390/s20226699 - 23 Nov 2020
Cited by 10 | Viewed by 2546
Abstract
Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme [...] Read more.
Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing. Full article
(This article belongs to the Special Issue Remote Sensing Big Data for Improving the Urban Environment)
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18 pages, 73552 KiB  
Letter
Image Stitching Based on Nonrigid Warping for Urban Scene
by Lixia Deng, Xiuxiao Yuan, Cailong Deng, Jun Chen and Yang Cai
Sensors 2020, 20(24), 7050; https://doi.org/10.3390/s20247050 - 09 Dec 2020
Cited by 6 | Viewed by 2095
Abstract
Image stitching based on a global alignment model is widely used in computer vision. However, the resulting stitched image may look blurry or ghosted due to parallax. To solve this problem, we propose a parallax-tolerant image stitching method based on nonrigid warping in [...] Read more.
Image stitching based on a global alignment model is widely used in computer vision. However, the resulting stitched image may look blurry or ghosted due to parallax. To solve this problem, we propose a parallax-tolerant image stitching method based on nonrigid warping in this paper. Given a group of putative feature correspondences between overlapping images, we first use a semiparametric function fitting, which introduces a motion coherence constraint to remove outliers. Then, the input images are warped according to a nonrigid warp model based on Gaussian radial basis functions. The nonrigid warping is a kind of elastic deformation that is flexible and smooth enough to eliminate moderate parallax errors. This leads to high-precision alignment in the overlapped region. For the nonoverlapping region, we use a rigid similarity model to reduce distortion. Through effective transition, the nonrigid warping of the overlapped region and the rigid warping of the nonoverlapping region can be used jointly. Our method can obtain more accurate local alignment while maintaining the overall shape of the image. Experimental results on several challenging data sets for urban scene show that the proposed approach is better than state-of-the-art approaches in both qualitative and quantitative indicators. Full article
(This article belongs to the Special Issue Remote Sensing Big Data for Improving the Urban Environment)
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