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Remote Sensing
Special Issues
Deep Learning and Big Data Mining with Remote Sensing
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Deep Learning and Big Data Mining with Remote Sensing

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 4855

Special Issue Editors

Dr. Bandana Kar

E-Mail Website
Guest Editor
National Security Sciences Directorate, Oak Ridge National Laboratory, Knoxville, TN 37831, USA
Interests: risk assessment and prediction; critical infrastructure resilience; urban resilience; remote sensing; geoinformatics; image fusion; spatial data science; social vulnerability; energy justice
Dr. Guido Cervone

E-Mail Website
Guest Editor
Institute for Computational and Data Sciences&Earth and Environmental Systems Institute, Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA
Interests: remote sensing; natural hazards; numerical models; machine learning; artificial intelligence
Dr. Xinyue Ye

E-Mail Website
Guest Editor
Department of Landscape Architecture and Urban Planning&Urban Data Science Lab, Texas A&M University, College Station, TX 77843, USA
Interests: geospatial artificial intelligence; regional science; smart cities; urban computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI/ML / remote sensing / hazards (but also other applications).  I think we need to emphasize remote sensing + AI/ML

The availability of large-scale earth observation data at varying spatial, temporal and spectral resolutions, whether derived from satellite sensors, drones or urban sensors, has enabled these datasets to be used to generate a time-critical situational awareness of different applications, including for extreme events. It is imperative to derive and update such information for applications in cities. The easy access to large volumes of datasets combined with advancements in artificial intelligence/machine learning and computational science has allowed for the generation of derived products and visualization platforms for decision makers. While there is no shortage of using remote sensing data for different applications, the quality of the derived products remains an intellectual and practical challenge.

The aim of this Special Issue is to publish basic and empirical research focused on (i) applying AI/DL along with remote sensing for different applications, with a specific focus given to extreme events; (ii) developing new data and image fusion techniques and statistical/mathematical models that allow high spatio-temporal resolution products to be derived; and (iii) assessing quality issues, including ethics, privacy and reproducibility/replicability of the derived products. The Special Issue contributes to the journal’s goal to publish methodological and applied papers pertaining to every aspect of remote sensing.

  • Challenges in collecting, processing and analyzing Big Data for real-time applications;
  • Data fusion of multi-source and/or heterogeneous datasets;
  • Privacy, reproducibility and replicability of remote sensing and AI-derived products;
  • Geo-visualization techniques to analyze and visualize remote sensing data;
  • Technological advances in hardware and data management;
  • Large-scale spatial data mining;
  • Spatial downscaling;
  • Virtual 3D mapping and analysis using remote sensing data. 

Dr. Bandana Kar
Dr. Guido Cervone
Dr. Xinyue Ye
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

  • data and image fusion
  • reproducibility and replicability
  • EO for emergency management
  • advantages and limitations of AI/DL

Published Papers (3 papers)

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Research

20 pages, 7207 KiB  
Article
Deep Graph-Convolutional Generative Adversarial Network for Semi-Supervised Learning on Graphs
by Nan Jia, Xiaolin Tian, Wenxing Gao and Licheng Jiao
Remote Sens. 2023, 15(12), 3172; https://doi.org/10.3390/rs15123172 - 18 Jun 2023
Cited by 1 | Viewed by 1398
Abstract
Graph convolutional networks (GCNs) are neural network frameworks for machine learning on graphs. They can simultaneously perform end-to-end learning on the attribute information and the structure information of graph data. However, most existing GCNs inevitably encounter the limitations of non-robustness and low classification [...] Read more.
Graph convolutional networks (GCNs) are neural network frameworks for machine learning on graphs. They can simultaneously perform end-to-end learning on the attribute information and the structure information of graph data. However, most existing GCNs inevitably encounter the limitations of non-robustness and low classification accuracy when labeled nodes are scarce. To address the two issues, the deep graph convolutional generative adversarial network (DGCGAN), a model combining GCN and deep convolutional generative adversarial networks (DCGAN), is proposed in this paper. First, the graph data is mapped to a highly nonlinear space by using the topology and attribute information of the graph for symmetric normalized Laplacian transform. Then, through the feature-structured enhanced module, the node features are expanded into regular structured data, such as images and sequences, which are input to DGCGAN as positive samples, thus expanding the sample capacity. In addition, the feature-enhanced (FE) module is adopted to enhance the typicality and discriminability of node features, and to obtain richer and more representative features, which is helpful for facilitating accurate classification. Finally, additional constraints are added to the network model by introducing DCGAN, thus enhancing the robustness of the model. Through extensive empirical studies on several standard benchmarks, we find that DGCGAN outperforms state-of-the-art baselines on semi-supervised node classification and remote sensing image classification. Full article
(This article belongs to the Special Issue Deep Learning and Big Data Mining with Remote Sensing)
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33 pages, 5067 KiB  
Article
An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective
by Lilu Zhu, Feng Wu, Kun Fu, Yanfeng Hu, Yang Wang, Xinmei Tian and Kai Huang
Remote Sens. 2023, 15(10), 2564; https://doi.org/10.3390/rs15102564 - 14 May 2023
Cited by 3 | Viewed by 993
Abstract
With the development and popularization of remote sensing earth observation technology and the remote sensing satellite system, the problems of insufficient proactiveness, relevance and timeliness of large-scale remote sensing supporting services are increasingly prominent, which seriously restricts the application of remote sensing resources [...] Read more.
With the development and popularization of remote sensing earth observation technology and the remote sensing satellite system, the problems of insufficient proactiveness, relevance and timeliness of large-scale remote sensing supporting services are increasingly prominent, which seriously restricts the application of remote sensing resources in multi-domain and cross-disciplinary. It is urgent to help terminal users make appropriate decisions according to real-time network environment and domain requirements, and obtain the optimal resources efficiently from the massive remote sensing resources. In this paper, we propose a recommendation algorithm using fusion of attention and multi-perspective (MRS_AMRA). Based on MRS_AMRA, we further implement an active service recommendation model (MRS_ASRM) for massive multi-source remote sensing resources by combining streaming pushing technology. Firstly, we construct value evaluation functions from multi-perspective in terms of remote sensing users, data and services to enable the adaptive provision of remote sensing resources. Then, we define multi-perspective heuristic policies to support resource discovery, and fusion these policies through the attention network, to achieve the accurate pushing of remote sensing resources. Finally, we implement comparative experiments to simulate accurate recommendation scenarios, compared with state-of-the-art algorithms, such as DIN and Geoportal. Furthermore, MRS_AMRA achieves an average improvement of 10.5% in the recommendation accuracy NDCG@K, and in addition, we developed a prototype system to verify the effectiveness and timeliness of MRS_ASRM. Full article
(This article belongs to the Special Issue Deep Learning and Big Data Mining with Remote Sensing)
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17 pages, 13267 KiB  
Article
ADAfinder Tool Applied to EGMS Data for the Structural Health Monitoring of Urban Settlements
by Annalisa Mele, Michele Crosetto, Andrea Miano and Andrea Prota
Remote Sens. 2023, 15(2), 324; https://doi.org/10.3390/rs15020324 - 05 Jan 2023
Cited by 8 | Viewed by 1500
Abstract
The new European Ground Motion Service (EGMS) opens a new prospect in the study of the ground deformation phenomena influencing structures and infrastructures, at regional scale, exploiting the huge archives of Synthetic Aperture Radar (SAR) images acquired from Sentinel-1 satellites. The research is [...] Read more.
The new European Ground Motion Service (EGMS) opens a new prospect in the study of the ground deformation phenomena influencing structures and infrastructures, at regional scale, exploiting the huge archives of Synthetic Aperture Radar (SAR) images acquired from Sentinel-1 satellites. The research is currently oriented toward developing new methodologies to exploit this great volume of data, the management of which is difficult and onerous in terms of time. A new methodology for the monitoring of the deformations of urban settlements, based on the application of the ADAfinder tool to EGMS measure points, is proposed in this work. It targets the semi-automatic extraction of active deformation areas (ADA), given in the form of maps, with the goal to identify the buildings affected by displacements above a given threshold among all the buildings included in the investigated area. This allows a smart selection of the buildings needing insights about their condition through on-site monitoring or inspections, providing real support for the management of the urban areas. The proposed methodology is applied to two different case study areas in the city of Barcelona (Spain): the Eixample, in the heart of the city, and the Zona Franca, an industrial area near to the harbor. Full article
(This article belongs to the Special Issue Deep Learning and Big Data Mining with Remote Sensing)
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