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Applied Sciences
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GeoAI Data and Processing in Applied Sciences
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GeoAI Data and Processing in Applied Sciences

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 8841

Special Issue Editors

Prof. Dr. Hyung-Sup Jung

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Guest Editor
1. Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Korea
2. Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Korea
Interests: remote sensing; geoinformatics; satellite data processing; environmental sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Saro Lee

E-Mail Website
Guest Editor
1. Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Republic of Korea
2. Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Republic of Korea
Interests: GIS application; geological hazard; geological resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geospatial artificial intelligence (GeoAI) has become more essential 1) in extracting valuable information from spatial big data and 2) in combining the biggest innovations of technologies used in geospatial sciences and engineering, remote sensing, geoscience information systems, and artificial intelligence (e.g., machine learning, deep learning). The GeoAI technology provides a very important advantage by enabling us to incorporate large amounts of spatiotemporal big spatial data with computational efficiency and flexibility in algorithm. The objectives of this Special Issue are to create a multidisciplinary forum of discussion on recent advances in the research fields of GeoAI for applied sciences and to find new techniques and applications in geospatial sciences and engineering, applied geology, applied biology, applied ecology, applied hydrology, applied environmentology, etc. Potential topics include, but are not limited to:

  • GeoAI data product
  • Validation of GeoAI data product
  • GeoAI data processing
  • Model development for GeoAI data
  • Modeling and mapping using GeoAI data
  • Change detection, target detection and target recognition using GeoAI data
  • Applications and innovative use of GeoAI data for geospatial sciences and engineering
  • Applications and innovative use of GeoAI data for remote sensing
  • Applications and innovative use of GeoAI data for geoscience information system
  • Applications and innovative use of GeoAI data for applied geology
  • Applications and innovative use of GeoAI data for applied biology
  • Applications and innovative use of GeoAI data for applied ecology
  • Applications and innovative use of GeoAI data for applied hydrology
  • Applications and innovative use of GeoAI data for applied environmentology

Prof. Dr. Hyung-Sup Jung
Prof. Dr. Saro Lee
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. Applied Sciences 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 2400 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

  • GeoAI
  • artificial intelligence
  • machine learning
  • deep learning
  • geoscience information system
  • remote sensing
  • geospatial sciences
  • geospatial engineering

Published Papers (4 papers)

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Research

21 pages, 5414 KiB  
Article
Transformer-Based Subject-Sensitive Hashing for Integrity Authentication of High-Resolution Remote Sensing (HRRS) Images
by Kaimeng Ding, Shiping Chen, Yue Zeng, Yingying Wang and Xinyun Yan
Appl. Sci. 2023, 13(3), 1815; https://doi.org/10.3390/app13031815 - 31 Jan 2023
Cited by 2 | Viewed by 1236
Abstract
The implicit prerequisite for using HRRS images is that the images can be trusted. Otherwise, their value would be greatly reduced. As a new data security technology, subject-sensitive hashing overcomes the shortcomings of existing integrity authentication methods and could realize subject-sensitive authentication of [...] Read more.
The implicit prerequisite for using HRRS images is that the images can be trusted. Otherwise, their value would be greatly reduced. As a new data security technology, subject-sensitive hashing overcomes the shortcomings of existing integrity authentication methods and could realize subject-sensitive authentication of HRRS images. However, shortcomings of the existing algorithm, in terms of robustness, limit its application. For example, the lack of robustness against JPEG compression makes existing algorithms more passive in some applications. To enhance the robustness, we proposed a Transformer-based subject-sensitive hashing algorithm. In this paper, first, we designed a Transformer-based HRRS image feature extraction network by improving Swin-Unet. Next, subject-sensitive features of HRRS images were extracted by this improved Swin-Unet. Then, the hash sequence was generated through a feature coding method that combined mapping mechanisms with principal component analysis (PCA). Our experimental results showed that the robustness of the proposed algorithm was greatly improved in comparison with existing algorithms, especially the robustness against JPEG compression. Full article
(This article belongs to the Special Issue GeoAI Data and Processing in Applied Sciences)
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22 pages, 7156 KiB  
Article
Deep Learning-Based Virtual Optical Image Generation and Its Application to Early Crop Mapping
by No-Wook Park, Min-Gyu Park, Geun-Ho Kwak and Sungwook Hong
Appl. Sci. 2023, 13(3), 1766; https://doi.org/10.3390/app13031766 - 30 Jan 2023
Cited by 3 | Viewed by 1449
Abstract
This paper investigates the potential of cloud-free virtual optical imagery generated using synthetic-aperture radar (SAR) images and conditional generative adversarial networks (CGANs) for early crop mapping, which requires cloud-free optical imagery at the optimal date for classification. A two-stage CGAN approach, including representation [...] Read more.
This paper investigates the potential of cloud-free virtual optical imagery generated using synthetic-aperture radar (SAR) images and conditional generative adversarial networks (CGANs) for early crop mapping, which requires cloud-free optical imagery at the optimal date for classification. A two-stage CGAN approach, including representation and generation stages, is presented to generate virtual Sentinel-2 spectral bands using all available information from Sentinel-1 SAR and Sentinel-2 optical images. The dual-polarization-based radar vegetation index and all available multi-spectral bands of Sentinel-2 imagery are particularly considered for feature extraction in the representation stage. A crop classification experiment using Sentinel-1 and -2 images in Illinois, USA, demonstrated that the use of all available scattering and spectral features achieved the best prediction performance for all spectral bands, including visible, near-infrared, red-edge, and shortwave infrared bands, compared with the cases that only used dual-polarization backscattering coefficients and partial input spectral bands. Early crop mapping with an image time series, including the virtual Sentinel-2 image, yielded satisfactory classification accuracy comparable to the case of using an actual time-series image set, regardless of the different combinations of spectral bands. Therefore, the generation of virtual optical images using the proposed model can be effectively applied to early crop mapping when the availability of cloud-free optical images is limited. Full article
(This article belongs to the Special Issue GeoAI Data and Processing in Applied Sciences)
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20 pages, 16192 KiB  
Article
Semantic Segmentation and Building Extraction from Airborne LiDAR Data with Multiple Return Using PointNet++
by Young-Ha Shin, Kyung-Wahn Son and Dong-Cheon Lee
Appl. Sci. 2022, 12(4), 1975; https://doi.org/10.3390/app12041975 - 14 Feb 2022
Cited by 11 | Viewed by 3085
Abstract
Light detection and ranging (LiDAR) data of 3D point clouds acquired from laser sensors is a crucial form of geospatial data for recognition of complex objects since LiDAR data provides geometric information in terms of 3D coordinates with additional attributes such as intensity [...] Read more.
Light detection and ranging (LiDAR) data of 3D point clouds acquired from laser sensors is a crucial form of geospatial data for recognition of complex objects since LiDAR data provides geometric information in terms of 3D coordinates with additional attributes such as intensity and multiple returns. In this paper, we focused on utilizing multiple returns in the training data for semantic segmentation, in particular building extraction using PointNet++. PointNet++ is known as one of the efficient and robust deep learning (DL) models for processing 3D point clouds. On most building boundaries, two returns of the laser pulse occur. The experimental results demonstrated that the proposed approach could improve building extraction by adding two returns to the training datasets. Specifically, the recall value of the predicted building boundaries for the test data was improved from 0.7417 to 0.7948 for the best case. However, no significant improvement was achieved for the new data because the new data had relatively lower point density compared to the training and test data. Full article
(This article belongs to the Special Issue GeoAI Data and Processing in Applied Sciences)
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15 pages, 2625 KiB  
Article
A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network
by Sung-Hwan Park, Hyung-Sup Jung, Sunmin Lee, Heon-Seok Yoo, Nam-Wook Cho and Moung-Jin Lee
Appl. Sci. 2021, 11(15), 7133; https://doi.org/10.3390/app11157133 - 02 Aug 2021
Cited by 1 | Viewed by 1706
Abstract
In Korea, the Ministry of Environment and regional environment management agencies conduct environmental impact assessments (EIA) to mitigate and assess the impact of major development projects on the environment. EIA Big Data are used in conjunction with a geographical information system (GIS), and [...] Read more.
In Korea, the Ministry of Environment and regional environment management agencies conduct environmental impact assessments (EIA) to mitigate and assess the impact of major development projects on the environment. EIA Big Data are used in conjunction with a geographical information system (GIS), and consist of indicators related to air, soil, and water that are measured before and after the development project. The impact of the development project on the environment can be evaluated through the variations of each indicator. This study analyzed trends in the environmental impacts of development projects during 2007–2016 using 21 types of EIA Big Data. A model was developed to estimate the Korean Environment Institute’s Environmental Impact Assessment Index for Development Projects (KEIDP) using a multi-layer perceptron-based artificial neural network (MLP-ANN) approach. A trend analysis of development projects in South Korea revealed that the mean value of KEIDP gradually increased over the study period. The rate of increase was 0.007 per year, with an R2 value of 0.8. In the future, it will be necessary for all management agencies to apply the KEDIP calculation model to minimize the impact of development projects on the environment and reduce deviations among development projects through continuous monitoring. Full article
(This article belongs to the Special Issue GeoAI Data and Processing in Applied Sciences)
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