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Remote Sensing
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Recent Development of Practical AI in Remote Sensing and Geoinformatics
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Recent Development of Practical AI in Remote Sensing and Geoinformatics

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 22639

Special Issue Editors

Dr. Ziheng Sun

E-Mail Website
Guest Editor
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
Interests: artificial intelligence; climate science; data science; earth observation; environmental science and policy; geoinformation science; geospatial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Nicoleta Cristea

E-Mail Website
Guest Editor
Department of Civil & Environmental Engineering, University of Washington, Seattle, WA, USA
Interests: hydrology; remote sensing of snow; snowmelt; snow modeling

Special Issue Information

Dear Colleagues,

This Special Issue is presented by research groups from the George Mason University and University of Washington, having studied and experimented with AI techniques on remote sensing datasets and geospatial cyberinfrastructure to acquire insights in Earth system sciences, especially in the fields of agriculture, hydrology, and atmosphere. 

This Special Issue aims to highlight contributions of AI-related research in remote sensing and geoinformatics, and illustrate the current progress and achievements, with the hopes of soliciting novel ideas and demonstrations of the practical use of AI for solving challenging scientific problems previously too difficult or even impossible to answer. We are open to manuscripts oriented towards the newest theories and methodologies of applying AI in geospatial data sciences, in environmental studies, agriculture, hydrology, atmosphere, land use and land cover, urban remote sensing, geophysical research, as well as the newest sensors and methods for remote sensing data acquisition, processing, and analysis. Both original research papers and comprehensive literature reviews with unique scientific insights are welcome.

Dr. Ziheng Sun
Dr. Nicoleta Cristea
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.

Published Papers (6 papers)

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Research

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20 pages, 1352 KiB  
Article
Integrating Spatio-Temporal and Generative Adversarial Networks for Enhanced Nowcasting Performance
by Wenbin Yu, Suxun Wang, Chengjun Zhang, Yadang Chen, Xinyu Sheng, Yu Yao, Jie Liu and Gaoping Liu
Remote Sens. 2023, 15(15), 3720; https://doi.org/10.3390/rs15153720 - 25 Jul 2023
Viewed by 1361
Abstract
Nowcasting has emerged as a critical foundation for services including heavy rain alerts and public transportation management. Although widely used for short-term forecasting, models such as TrajGRU and PredRNN exhibit limitations in predicting low-intensity rainfall and low temporal resolution, resulting in suboptimal performance [...] Read more.
Nowcasting has emerged as a critical foundation for services including heavy rain alerts and public transportation management. Although widely used for short-term forecasting, models such as TrajGRU and PredRNN exhibit limitations in predicting low-intensity rainfall and low temporal resolution, resulting in suboptimal performance during infrequent heavy rainfall events. To tackle these challenges, we introduce a spatio-temporal sequence and generative adversarial network model for short-term precipitation forecasting based on radar data. By enhancing the ConvLSTM model with a pre-trained TransGAN generator, we improve feature resolution. We first assessed the model’s performance on the Moving MNIST dataset and subsequently validated it on the HKO-7 dataset. Employing metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Structural Similarity Index Measure (SSIM), Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR), we compare our model’s performance to existing models. Experimental results reveal that our proposed ConvLSTM-TransGAN model effectively captures weather system evolution and surpasses the performance of other traditional models. Full article
(This article belongs to the Special Issue Recent Development of Practical AI in Remote Sensing and Geoinformatics)
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18 pages, 2413 KiB  
Article
A Dual-Polarization Information-Guided Network for SAR Ship Classification
by Zikang Shao, Tianwen Zhang and Xiao Ke
Remote Sens. 2023, 15(8), 2138; https://doi.org/10.3390/rs15082138 - 18 Apr 2023
Cited by 13 | Viewed by 1974
Abstract
Synthetic aperture radar (SAR) is an advanced active microwave sensor widely used in marine surveillance. As part of typical marine surveillance missions, ship classification in synthetic aperture radar (SAR) images is a significant task for the remote sensing community. However, fully utilizing polarization [...] Read more.
Synthetic aperture radar (SAR) is an advanced active microwave sensor widely used in marine surveillance. As part of typical marine surveillance missions, ship classification in synthetic aperture radar (SAR) images is a significant task for the remote sensing community. However, fully utilizing polarization information to enhance SAR ship classification remains an unresolved issue. Thus, we proposed a dual-polarization information-guided network (DPIG-Net) to solve it. DPIG-Net utilizes available dual-polarization information from the Sentinel-1 SAR satellite to adaptively guide feature extraction and feature fusion. We first designed a novel polarization channel cross-attention framework (PCCAF) to model the correlations of different polarization information for feature extraction. Then, we established a novel dilated residual dense learning framework (DRDLF) to refine the polarization characteristics for feature fusion. The results on the open OpenSARShip dataset indicated DPIG-Net’s state-of-the-art classification accuracy compared with eleven other competitive models, which showed the potential of DPIG-Net to promote effective and sufficient utilization of SAR polarization data in the future. Full article
(This article belongs to the Special Issue Recent Development of Practical AI in Remote Sensing and Geoinformatics)
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19 pages, 1225 KiB  
Article
Mutual Information Boosted Precipitation Nowcasting from Radar Images
by Yuan Cao, Danchen Zhang, Xin Zheng, Hongming Shan and Junping Zhang
Remote Sens. 2023, 15(6), 1639; https://doi.org/10.3390/rs15061639 - 17 Mar 2023
Cited by 4 | Viewed by 1982
Abstract
Precipitation nowcasting has long been a challenging problem in meteorology. While recent studies have introduced deep neural networks into this area and achieved promising results, these models still struggle with the rapid evolution of rainfall and extremely imbalanced data distribution, resulting in poor [...] Read more.
Precipitation nowcasting has long been a challenging problem in meteorology. While recent studies have introduced deep neural networks into this area and achieved promising results, these models still struggle with the rapid evolution of rainfall and extremely imbalanced data distribution, resulting in poor forecasting performance for convective scenarios. In this article, we evaluate the amount of information in different precipitation nowcasting tasks of varying lengths using mutual information. We propose two strategies: the mutual information-based reweighting strategy (MIR) and a mutual information-based training strategy (time superimposing strategy (TSS)). MIR reinforces neural network models to improve the forecasting accuracy for convective scenarios while maintaining prediction performance for rainless scenarios and overall nowcasting image quality. The TSS strategy enhances the model’s forecasting performance by adopting a curriculum learning-like method. Although the proposed strategies are simple, the experimental results show that they are effective and can be applied to various state-of-the-art models. Full article
(This article belongs to the Special Issue Recent Development of Practical AI in Remote Sensing and Geoinformatics)
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28 pages, 16909 KiB  
Article
Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran
by Rasoul Afsari, Saman Nadizadeh Shorabeh, Amir Reza Bakhshi Lomer, Mehdi Homaee and Jamal Jokar Arsanjani
Remote Sens. 2023, 15(5), 1248; https://doi.org/10.3390/rs15051248 - 24 Feb 2023
Cited by 8 | Viewed by 2007
Abstract
The purpose of this study is to assess the vulnerability of urban blocks to earthquakes for Tehran as a city built on geological faults using an artificial neural network—multi-layer perceptron (ANN-MLP). Therefore, we first classified earthquake vulnerability evaluation criteria into three categories: exposure, [...] Read more.
The purpose of this study is to assess the vulnerability of urban blocks to earthquakes for Tehran as a city built on geological faults using an artificial neural network—multi-layer perceptron (ANN-MLP). Therefore, we first classified earthquake vulnerability evaluation criteria into three categories: exposure, sensitivity, and adaptability capacity attributed to a total of 16 spatial criteria, which were inputted into the neural network. To train the neural network and compute an earthquake vulnerability map, we used a combined Multi-Criteria Decision Analysis (MCDA) process with 167 vulnerable locations as training data, of which 70% (117 points) were used for training, and 30% (50 points) were used for testing and validation. The Mean Average Error (MAE) of the implemented neural network was 0.085, which proves the efficacy of the designed model. The results showed that 29% of Tehran’s total area is extremely vulnerable to earthquakes. Our factor importance analysis showed that factors such as proximity to fault lines, high population density, and environmental factors gained higher importance scores for earthquake vulnerability assessment of the given case study. This methodical approach and the choice of data and methods can provide insight into scaling up the study to other regions. In addition, the resultant outcomes can help decision makers and relevant stakeholders to mitigate risks through resilience building. Full article
(This article belongs to the Special Issue Recent Development of Practical AI in Remote Sensing and Geoinformatics)
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24 pages, 8336 KiB  
Article
Urban Feature Extraction within a Complex Urban Area with an Improved 3D-CNN Using Airborne Hyperspectral Data
by Xiaotong Ma, Qixia Man, Xinming Yang, Pinliang Dong, Zelong Yang, Jingru Wu and Chunhui Liu
Remote Sens. 2023, 15(4), 992; https://doi.org/10.3390/rs15040992 - 10 Feb 2023
Cited by 6 | Viewed by 1868
Abstract
Airborne hyperspectral data has high spectral-spatial information. However, how to mine and use this information effectively is still a great challenge. Recently, a three-dimensional convolutional neural network (3D-CNN) provides a new effective way of hyperspectral classification. However, its capability of data mining in [...] Read more.
Airborne hyperspectral data has high spectral-spatial information. However, how to mine and use this information effectively is still a great challenge. Recently, a three-dimensional convolutional neural network (3D-CNN) provides a new effective way of hyperspectral classification. However, its capability of data mining in complex urban areas, especially in cloud shadow areas has not been validated. Therefore, a 3D-1D-CNN model was proposed for feature extraction in complex urban with hyperspectral images affected by cloud shadows. Firstly, spectral composition parameters, vegetation index, and texture characteristics were extracted from hyperspectral data. Secondly, the parameters were fused and segmented into many S × S × B patches which would be input into a 3D-CNN classifier for feature extraction in complex urban areas. Thirdly, Support Vector Machine (SVM), Random Forest (RF),1D-CNN, 3D-CNN, and 3D-2D-CNN classifiers were also carried out for comparison. Finally, a confusion matrix and Kappa coefficient were calculated for accuracy assessment. The overall accuracy of the proposed 3D-1D-CNN is 96.32%, which is 23.96%, 11.02%, 5.22%, and 0.42%, much higher than that of SVM, RF, 1D-CNN, or 3D-CNN, respectively. The results indicated that 3D-1D-CNN could mine spatial-spectral information from hyperspectral data effectively, especially that of grass and highway in cloud shadow areas with missing spectral information. In the future, 3D-1D-CNN could also be used for the extraction of urban green spaces. Full article
(This article belongs to the Special Issue Recent Development of Practical AI in Remote Sensing and Geoinformatics)
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Review

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34 pages, 4799 KiB  
Review
A Review of Practical AI for Remote Sensing in Earth Sciences
by Bhargavi Janga, Gokul Prathin Asamani, Ziheng Sun and Nicoleta Cristea
Remote Sens. 2023, 15(16), 4112; https://doi.org/10.3390/rs15164112 - 21 Aug 2023
Cited by 11 | Viewed by 11815
Abstract
Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for revolutionizing data analysis and applications in many domains of Earth sciences. This review paper synthesizes the existing literature on AI applications in remote sensing, consolidating and analyzing AI methodologies, outcomes, and [...] Read more.
Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for revolutionizing data analysis and applications in many domains of Earth sciences. This review paper synthesizes the existing literature on AI applications in remote sensing, consolidating and analyzing AI methodologies, outcomes, and limitations. The primary objectives are to identify research gaps, assess the effectiveness of AI approaches in practice, and highlight emerging trends and challenges. We explore diverse applications of AI in remote sensing, including image classification, land cover mapping, object detection, change detection, hyperspectral and radar data analysis, and data fusion. We present an overview of the remote sensing technologies, methods employed, and relevant use cases. We further explore challenges associated with practical AI in remote sensing, such as data quality and availability, model uncertainty and interpretability, and integration with domain expertise as well as potential solutions, advancements, and future directions. We provide a comprehensive overview for researchers, practitioners, and decision makers, informing future research and applications at the exciting intersection of AI and remote sensing. Full article
(This article belongs to the Special Issue Recent Development of Practical AI in Remote Sensing and Geoinformatics)
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