Special Issue "Data Fusion for Urban Applications"

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

Deadline for manuscript submissions: 20 July 2022 | Viewed by 8636

Special Issue Editors

Dr. Stefan Auer
E-Mail Website
Guest Editor
German Aerospace Center (DLR), Remote Sensing Technology Institute, Muenchener Strasse 20, 82234 Wessling-Oberpfaffenhofen, Germany
Interests: remote sensing; image analysis; interpretation; data fusion; simulation
PD Dr. Michael Schmitt
E-Mail Website
Guest Editor
Signal Processing in Earth Observation, Technical University of Munich, 80333 Munich, Germany
Interests: remote sensing; data fusion; machine learning; geospatial data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote Sensing applications for urban areas are of major importance as the majority of human population is concentrated in these regions. Looking at reported literature, data from different sensors have been acquired, e.g., in order to characterize the nature of city areas, monitor urban development over time or detect changes after unexpected events. In this context, the unification of information from multimodal sensors in urban applications has always been very welcome, but so far hard to solve. On the one hand, finding appropriate strategies for combining the complementary information is difficult. On the other hand, the assignment of multi-modal information to entities of urban scenes is often not unambiguous. An increase of the spatial resolution of image data does not necessarily help but even tightens the conditions for useful solutions.

This Special Issue is devoted to strategies and methods for fusing multi-modal data in the context of urban remote sensing. As a general guideline, complementary sources should be combined in order to gain improved information about urban areas.   

Submitting authors are encouraged to address one of the following topics in the context of remote sensing data (not exclusively):

  • Enhancement of urban applications through exploitation of complementary information provided by data from multiple sensors, multiple sources and multi-temporal acquisitions;
  • Integration of external prior knowledge into urban remote sensing;
  • Fusion of information from remote sensing and non-typical Earth observation data sources (terrestrial data, data from social media, etc.) for improved understanding of urban problems;
  • 2-D, 3-D and multi-dimensional data fusion for urban analysis;
  • Multi-view fusion for exploiting different perspectives on urban elements;
  • Data fusion for urban tasks conducted on data level, feature level, or decision level;
  • Urban applications on different resolution levels (spatial, spectral, temporal).

Dr. Stefan Auer
PD Dr. Michael Schmitt
Dr. Naoto Yokoya
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 2500 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

  • Data fusion
  • Image fusion
  • Multi-sensor fusion
  • Multi-source fusion
  • Urban applications
  • City monitoring
  • Change detection
  • Multi-resolution data
  • Multi-temporal data
  • Multi-spectral data
  • Accuracy assessment

Published Papers (6 papers)

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Research

Article
Urban Sprawl and COVID-19 Impact Analysis by Integrating Deep Learning with Google Earth Engine
Remote Sens. 2022, 14(9), 2038; https://doi.org/10.3390/rs14092038 - 24 Apr 2022
Viewed by 500
Abstract
Timely information on land use, vegetation coverage, and air and water quality, are crucial for monitoring and managing territories, especially for areas in which there is dynamic urban expansion. However, getting accessible, accurate, and reliable information is not an easy task, since the [...] Read more.
Timely information on land use, vegetation coverage, and air and water quality, are crucial for monitoring and managing territories, especially for areas in which there is dynamic urban expansion. However, getting accessible, accurate, and reliable information is not an easy task, since the significant increase in remote sensing data volume poses challenges for the timely processing and analysis of the resulting massive data volume. From this perspective, classical methods for urban monitoring present some limitations and more innovative technologies, such as artificial-intelligence-based algorithms, must be exploited, together with performing cloud platforms and ad hoc pre-processing steps. To this end, this paper presents an approach to the use of cloud-enabled deep-learning technology for urban sprawl detection and monitoring, through the fusion of optical and synthetic aperture radar data, by integrating the Google Earth Engine cloud platform with deep-learning techniques through the use of the open-source TensorFlow library. The model, based on a U-Net architecture, was applied to evaluate urban changes in Phoenix, the second fastest-growing metropolitan area in the United States. The available ancillary information on newly built areas showed good agreement with the produced change detection maps. Moreover, the results were temporally related to the appearance of the SARS-CoV-2 (commonly known as COVID-19) pandemic, showing a decrease in urban expansion during the event. The proposed solution may be employed for the efficient management of dynamic urban areas, providing a decision support system to help policy makers in the measurement of changes in territories and to monitor their impact on phenomena related to urbanization growth and density. The reference data were manually derived by the authors over an area of approximately 216 km2, referring to 2019, based on the visual interpretation of high resolution images, and are openly available. Full article
(This article belongs to the Special Issue Data Fusion for Urban Applications)
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Article
Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier
Remote Sens. 2021, 13(15), 3040; https://doi.org/10.3390/rs13153040 - 03 Aug 2021
Cited by 4 | Viewed by 952
Abstract
Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have [...] Read more.
Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have identified the potential of remote sensing and multiple source information in impervious surface quantification. Therefore, this study aims to fuse datasets from the Sentinel 1 and 2 Satellites to map the impervious surfaces of nine Pakistani cities and estimate their growth rates from 2016 to 2020 utilizing the random forest algorithm. All bands in the optical and radar images were resampled to 10 m resolution, projected to same coordinate system and geometrically aligned to stack into a single product. The models were then trained, and classifications were validated with land cover samples from Google Earth’s high-resolution images. Overall accuracies of classified maps ranged from 85% to 98% with the resultant quantities showing a strong linear relationship (R-squared value of 0.998) with the Copernicus Global Land Services data. There was up to 9% increase in accuracy and up to 12 % increase in kappa coefficient from the fused data with respect to optical alone. A McNemar test confirmed the superiority of fused data. Finally, the cities had growth rates ranging from 0.5% to 2.5%, with an average of 1.8%. The information obtained can alert urban planners and environmentalists to assess impervious surface impacts in the cities. Full article
(This article belongs to the Special Issue Data Fusion for Urban Applications)
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Article
Remote Sensing-Based Analysis of Urban Landscape Change in the City of Bucharest, Romania
Remote Sens. 2021, 13(12), 2323; https://doi.org/10.3390/rs13122323 - 13 Jun 2021
Viewed by 1139
Abstract
The paper investigates the urban landscape changes for the last 50 years in Bucharest, the capital city of Romania. Bucharest shows a complex structural transformation driven by the socialist urban policy, followed by an intensive real-estate market development. Our analysis is based on [...] Read more.
The paper investigates the urban landscape changes for the last 50 years in Bucharest, the capital city of Romania. Bucharest shows a complex structural transformation driven by the socialist urban policy, followed by an intensive real-estate market development. Our analysis is based on a diachronic set of high-resolution satellite imagery: declassified CORONA KH-4B from 1968, SPOT-1 from 1989, and multisensor stacked layers from Sentinel-1 SAR together with Sentinel-2MSI from 2018. Three different datasets of land cover/use are extracted for the reference years. Each dataset reveals its own urban structure pattern. The first one illustrates a radiography of the city in the second part of the 20th century, where rural patterns meet the modern ones, while the second one reveals the frame of a city in a full process of transformation with multiple constructions sites, based on the socialist model. The third one presents an image of a cosmopolitan city during an expansion process, with a high degree of landscape heterogeneity. All the datasets are included in a built-up change analysis in order to map and assess the spatial transformations of the city pattern over 5 decades. In order to quantify and map the changes, the Built-up Change Index (BCI) is introduced. The results highlight a particular situation linked to the policy development visions for each decade, with major changes of about 50% for different built-up classes. The GIS analysis illustrates two major landscape transformations: from the old semirural structures with houses surrounded by gardens from 1968, to a compact pattern with large districts of blocks of flats in 1989, and a contemporary city defined by an uncontrolled urban sprawl process in 2018. Full article
(This article belongs to the Special Issue Data Fusion for Urban Applications)
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Article
An Improved Index for Urban Population Distribution Mapping Based on Nighttime Lights (DMSP-OLS) Data: An Experiment in Riyadh Province, Saudi Arabia
Remote Sens. 2021, 13(6), 1171; https://doi.org/10.3390/rs13061171 - 19 Mar 2021
Cited by 5 | Viewed by 1112
Abstract
Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the [...] Read more.
Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the well-reported challenges of pixel overglow and saturation influence the applicability of the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) for accurate population mapping. This paper integrates three remotely sensed information sources, DMSP-OLS, vegetation, and bare land areas, to develop a novel index called the Vegetation-Bare Adjusted NTL Index (VBANTLI) to overcome the uncertainties in the DMSP-OLS data. The VBANTLI was applied to Riyadh province to downscale governorate-level census population for 2004 and 2010 to a gridded surface of 1 km resolution. The experimental results confirmed that the VBANTLI significantly reduced the overglow and saturation effects compared to widely applied indices such as the Human Settlement Index (HSI), Vegetation Adjusted Normalized Urban Index (VANUI), and radiance-calibrated NTL (RCNTL). The correlation coefficient between the census population and the RCNTL (R = 0.99) and VBANTLI (R = 0.98) was larger than for the HSI (R = 0.14) and VANUI (R = 0.81) products. In addition, Model 5 (VBANTLI) was the most accurate model with R2 and mean relative error (MRE) values of 0.95% and 37%, respectively. Full article
(This article belongs to the Special Issue Data Fusion for Urban Applications)
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Article
An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition
Remote Sens. 2020, 12(16), 2582; https://doi.org/10.3390/rs12162582 - 11 Aug 2020
Cited by 2 | Viewed by 1118
Abstract
The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as [...] Read more.
The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as a third-order tensor. The InSAR tensor can be filtered by data fusion, i.e., tensor decomposition, and these filters keep balance in the noise elimination and the fringe details preservation, especially with abrupt fringe change, e.g., the edge of urban structures. However, tensor decomposition based on batch processing cannot deal with few newly acquired interferograms filtering directly. The filtering of dynamic InSAR tensor is the inevitable challenge when processing InSAR stack data, where dynamic InSAR tensor denotes the size of InSAR tensor increases continuously due to the acquisition of new interferograms. Therefore, based on the online CANDECAMP/PARAFAC (CP) decomposition, we propose an online filter to fuse data and process the dynamic InSAR tensor, named OLCP-InSAR, which performs well especially for the urban area. In this method, CP rank is utilized to measure the tensor sparsity, which can maintain the structural features of the InSAR tensor. Additionally, CP rank estimation is applied as an important step to improve the robustness of Online CP decomposition - InSAR(OLCP-InSAR). Importing CP rank and outlier’s position as prior information, the filter fuses the noisy interferograms and decomposes the InSAR tensor to acquire the low rank information, i.e., filtered result. Moreover, this method can not only operate on tensor model, but also efficiently filter the new acquired interferogram as matrix model with the assistance of chosen low rank information. Compared with other tensor-based filters, e.g., high order robust principal component analysis (HoRPCA) and Kronecker-basis-representation multi-pass SAR interferometry (KBR-InSAR), and the widespread traditional filters operating on a single interferometric pair, e.g., Goldstein, non-local synthetic aperture radar (NL-SAR), non-local InSAR (NL-InSAR), and InSAR nonlocal block-matching 3-D (InSAR-BM3D), the effectiveness and robustness of OLCP-InSAR are proved in simulated and real InSAR stack data. Especially, OLCP-InSAR can maintain the fringe details at the regular building top with high noise intensity and high outlier ratio. Full article
(This article belongs to the Special Issue Data Fusion for Urban Applications)
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Article
A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios
Remote Sens. 2020, 12(15), 2460; https://doi.org/10.3390/rs12152460 - 31 Jul 2020
Cited by 25 | Viewed by 2738
Abstract
Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation [...] Read more.
Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation of urban change detection, we make an observation of the literatures on change detection in the last five years, which focuses on the disparate multi-source RS images and multi-objective scenarios determined according to scene category. Based on the survey, a general change detection framework, including change information extraction, data fusion, and analysis of multi-objective scenarios modules, is summarized. Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. On this basis, not only the evolution process and relationship of the representative solutions are elaborated in the module description, through emphasizing the feasibility of fusing diverse data and the manifold application scenarios, we also advocate a complete change detection pipeline. At the end of the paper, we conclude the current development situation and put forward possible research direction of urban change detection, in the hope of providing insights to the following research. Full article
(This article belongs to the Special Issue Data Fusion for Urban Applications)
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