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The Emerging Trends and Applications of Big Data and Machine Learning/Artificial Intelligence (AI) in Remote Sensing II

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

Deadline for manuscript submissions: 15 December 2024 | Viewed by 4693

Special Issue Editors

Prof. Dr. Liangxiu Han

E-Mail Website
Guest Editor
School of Computing, Mathematics & Digital Technology, Manchester Metropolitan University, Manchester M15 6BH, UK
Interests: big data/machine learning; artificial intelligence; parallel and distributed computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wenjiang Huang

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: vegetation remote sensing; image processing; image classification; parameter mapping; system development; agricultural application
Special Issues, Collections and Topics in MDPI journals
Dr. Yanbo Huang

E-Mail Website
Guest Editor
Genetics and Sustainable Agriculture Research Unit, U.S. Department of Agriculture–Agricultural Research Service, Mississippi State, MS 39762, USA
Interests: process modeling; optimization and control; remote sensing; artificial intelligence; machine learning; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Jiali Shang

E-Mail Website
Guest Editor
Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
Interests: remote sensing research and application development; land use mapping; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Stefano Pignatti

E-Mail Website
Guest Editor
Institute of Methodologies for Environmental Analysis (IMAA), National Research Council (CNR), C.da S. Loja, 85050 Tito, PZ, Italy
Interests: hyperspectral remote sensing VSWIR-LWIR; sensor data calibration and pre-processing; field spectroscopy; retrieval of surfaces parameters; soil spectral characterization and geology; archaeological site analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remotely sensed data generated by various platforms (e.g., satellite, manned aircraft, unmanned aerial vehicle and ground-based systems) is a unique source of big data, which has great potential for informative decision making in many domains, including agriculture, environment, business activities, and transport.  Recent advances in data science and AI/machine learning have shown great promise in processing, managing and analysing such large and heterogeneous data sources at both local and global scales for various tasks, including land use and land cover mapping (classifications), object-based image analysis (segmentation, object detection), and quantitative modelling (plant biophysical/biochemical parameter retrieval, yield estimation, ecological assessment). This Special Issue aims to provide a refreshing view of current developments/emerging trends and applications in the field. The ultimate goal is to promote research and the sustainable development of advanced big data analytics and AI/machine learning schemes for the efficient analysis of remotely sensed data.

Prof. Dr. Liangxiu Han
Prof. Dr. Wenjiang Huang
Dr. Yanbo Huang
Dr. Jiali Shang
Dr. Stefano Pignatti
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.

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Related Special Issue

Published Papers (3 papers)

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Research

19 pages, 14890 KiB  
Article
Advancing Regional–Scale Spatio–Temporal Dynamics of FFCO2 Emissions in Great Bay Area
by Jing Zhao, Qunqun Zhao, Wenjiang Huang, Guoqing Li, Tuo Wang, Naixia Mou and Tengfei Yang
Remote Sens. 2024, 16(13), 2354; https://doi.org/10.3390/rs16132354 - 27 Jun 2024
Viewed by 527
Abstract
Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion [...] Read more.
Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a “core–subcore–periphery” emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers. Full article
(This article belongs to the Special Issue The Emerging Trends and Applications of Big Data and Machine Learning/Artificial Intelligence (AI) in Remote Sensing II)
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27 pages, 2404 KiB  
Article
A Generic Self-Supervised Learning (SSL) Framework for Representation Learning from Spectral–Spatial Features of Unlabeled Remote Sensing Imagery
by Xin Zhang and Liangxiu Han
Remote Sens. 2023, 15(21), 5238; https://doi.org/10.3390/rs15215238 - 3 Nov 2023
Cited by 1 | Viewed by 1573
Abstract
Remote sensing data has been widely used for various Earth Observation (EO) missions such as land use and cover classification, weather forecasting, agricultural management, and environmental monitoring. Most existing remote-sensing-data-based models are based on supervised learning that requires large and representative human-labeled data [...] Read more.
Remote sensing data has been widely used for various Earth Observation (EO) missions such as land use and cover classification, weather forecasting, agricultural management, and environmental monitoring. Most existing remote-sensing-data-based models are based on supervised learning that requires large and representative human-labeled data for model training, which is costly and time-consuming. The recent introduction of self-supervised learning (SSL) enables models to learn a representation from orders of magnitude more unlabeled data. The success of SSL is heavily dependent on a pre-designed pretext task, which introduces an inductive bias into the model from a large amount of unlabeled data. Since remote sensing imagery has rich spectral information beyond the standard RGB color space, it may not be straightforward to extend to the multi/hyperspectral domain the pretext tasks established in computer vision based on RGB images. To address this challenge, this work proposed a generic self-supervised learning framework based on remote sensing data at both the object and pixel levels. The method contains two novel pretext tasks, one for object-based and one for pixel-based remote sensing data analysis methods. One pretext task is used to reconstruct the spectral profile from the masked data, which can be used to extract a representation of pixel information and improve the performance of downstream tasks associated with pixel-based analysis. The second pretext task is used to identify objects from multiple views of the same object in multispectral data, which can be used to extract a representation and improve the performance of downstream tasks associated with object-based analysis. The results of two typical downstream task evaluation exercises (a multilabel land cover classification task on Sentinel-2 multispectral datasets and a ground soil parameter retrieval task on hyperspectral datasets) demonstrate that the proposed SSL method learns a target representation that covers both spatial and spectral information from massive unlabeled data. A comparison with currently available SSL methods shows that the proposed method, which emphasizes both spectral and spatial features, outperforms existing SSL methods on multi- and hyperspectral remote sensing datasets. We believe that this approach has the potential to be effective in a wider range of remote sensing applications and we will explore its utility in more remote sensing applications in the future. Full article
(This article belongs to the Special Issue The Emerging Trends and Applications of Big Data and Machine Learning/Artificial Intelligence (AI) in Remote Sensing II)
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Graphical abstract

19 pages, 8394 KiB  
Article
A Deep Learning Approach to Increase the Value of Satellite Data for PM2.5 Monitoring in China
by Bo Li, Cheng Liu, Qihou Hu, Mingzhai Sun, Chengxin Zhang, Yizhi Zhu, Ting Liu, Yike Guo, Gregory R. Carmichael and Meng Gao
Remote Sens. 2023, 15(15), 3724; https://doi.org/10.3390/rs15153724 - 26 Jul 2023
Viewed by 1380
Abstract
Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as [...] Read more.
Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, satellite remote sensing AODs are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be obtained with satellite remote sensing under cloudy/hazy conditions or during nighttime. In this work, we introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We quantified the quantitative impact of input variables on the results using sensitivity and visual analysis of the model. This technique provides ground-level PM2.5 concentrations with a high spatial resolution (0.01°) and 24-h temporal coverage, hour-by-hour, complete coverage. In central and eastern China, the 10-fold cross-validation results show that R2 is between 0.8 and 0.9, and RMSE is between 6 and 26 (µg m−3). The relative error varies in different concentration ranges and is generally less than 20%. Better constrained spatiotemporal distributions of PM2.5 concentrations will contribute to improving health effects studies, atmospheric emission estimates, and air quality predictions. Full article
(This article belongs to the Special Issue The Emerging Trends and Applications of Big Data and Machine Learning/Artificial Intelligence (AI) in Remote Sensing II)
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