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Remote Sensing
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Incorporating Knowledge-Infused Approaches in Remote Sensing
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Incorporating Knowledge-Infused Approaches in 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 (30 April 2024) | Viewed by 7838

Special Issue Editors

Dr. Marshall (Xiaogang) Ma

E-Mail Website
Guest Editor
Department of Computer Science, University of Idaho, Moscow, ID 83844-1010, USA
Interests: semantic and knowledge graph; data interoperability and provenance; exploratory data analytics and visualization; geoinformatics
Special Issues, Collections and Topics in MDPI journals
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. Sanaz Salati

E-Mail Website
Guest Editor
Lowell Institute for Mineral Resources, University of Arizona, Tucson, AZ 85721, USA
Interests: remote sensing; GIScience; environmental science; data science; geography
Dr. Chao Fan

E-Mail Website
Guest Editor
Department of Geography, New Mexico State University, Las Cruces, NM 88003, USA
Interests: geographical information science; spatial analysis and modeling; remote sensing; climate change; land cover land use change
Special Issues, Collections and Topics in MDPI journals
Dr. Meifang Li

E-Mail Website
Guest Editor
Department of Geography, Dartmouth College, Hanover, NH 03755, USA
Interests: GIScience; spatial epidemiology; spatiotemporal modeling; environmental health
Dr. Zhe Wang

E-Mail Website
Guest Editor
Department of Computer Science, University of Idaho, Moscow, ID 83843, USA
Interests: GIScience; remote sensing; geo-health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing is an integral part of many scientific applications, and demands new thoughts and techniques to be fully exploited. Deploying knowledge models in remote sensing data analysis has been at the center of attention for more than a decade. Various types of knowledge-driven methods have been applied in remote sensing image understanding, including object detection, segmentation, and classification. However, there is limited discussion on machine-readable knowledge and its incorporation in the remote sensing data analysis workflow. In the era of big data and machine learning, it is important to include explicit knowledge in data-intensive studies, which has huge potential to bring new thoughts and approaches in the field of remote sensing. Applying semantic techniques such as vocabularies, taxonomies, and ontologies leads to achieving knowledge that allows the experts of a scientific domain to capture and formalize the concepts and relationships of that domain. As such, in recent years the topics of formal knowledge representation and knowledge-infused machine learning have been increasingly discussed among the remote sensing research community. This Special Issue calls for research articles presenting innovative methods or applications that infuse knowledge in remote sensing data processing. Review and perspective articles that offer insights on this field of research are also welcome.

Dr. Xiaogang Ma
Dr. Ziheng Sun
Dr. Sanaz Salati
Dr. Chao Fan
Dr. Meifang Li
Dr. Zhe Wang
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

  • knowledge model
  • remote sensing
  • hybrid modeling
  • machine learning
  • ontology
  • knowledge graph

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Published Papers (3 papers)

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Research

18 pages, 5477 KiB  
Article
Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment
by Lijie Chen, Zhe Wang, Xiaogang Ma, Jingwen Zhao, Xiang Que, Jinfu Liu, Ruohai Chen and Yimin Li
Remote Sens. 2024, 16(10), 1678; https://doi.org/10.3390/rs16101678 - 9 May 2024
Viewed by 1595
Abstract
With climate change and urbanization expansion, wetlands, which are some of the largest carbon stocks in the world, are facing threats such as shrinking areas and declining carbon sequestration capacities. Wetland carbon stocks are at risk of being transformed into carbon sources, especially [...] Read more.
With climate change and urbanization expansion, wetlands, which are some of the largest carbon stocks in the world, are facing threats such as shrinking areas and declining carbon sequestration capacities. Wetland carbon stocks are at risk of being transformed into carbon sources, especially those of wetlands with strong land use–natural resource conservation conflict. Moreover, there is a lack of well-established indicators for evaluating the health of wetland carbon stocks. To address this issue, we proposed a novel framework for the safety assessment of wetland carbon stocks using the Super Slack-Based Measure (Super-SBM), and we then conducted an empirical study on the Quanzhou Bay Estuary Wetland (QBEW). This framework integrates the unexpected output indicator (i.e., carbon emissions), the expected output indicators, including the GDP per capita and carbon stock estimates calculated via machine learning (ML)-based remote sensing inversion, and the input indicators, such as environmental governance investigations, climate conditions, socio-economic activities, and resource utilization. The results show that the annual average safety assessment for carbon pools in the QBEW was a meager 0.29 in 2015, signaling a very poor state, likely due to inadequate inputs or excessive unexpected outputs. However, there has been a substantial improvement since then, as evidenced by the fact that all the safety assessments have exceeded the threshold of 1 from 2018 onwards, reflecting a transition to a “weakly effective” status within a safe and acceptable range. Moreover, our investigation employing the Super-SBM model to calculate the “slack variables” yielded valuable insights into optimization strategies. This research advances the field by establishing a safety measurement framework for wetland carbon pools that leverages efficiency assessment methods, thereby offering a quantitative safeguard mechanism that supports the achievement of the “3060” dual-carbon target. Full article
(This article belongs to the Special Issue Incorporating Knowledge-Infused Approaches in Remote Sensing)
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29 pages, 13193 KiB  
Article
Construction of Remote Sensing Indices Knowledge Graph (RSIKG) Based on Semantic Hierarchical Graph
by Chenliang Wang, Wenjiao Shi and Hongchen Lv
Remote Sens. 2024, 16(1), 158; https://doi.org/10.3390/rs16010158 - 30 Dec 2023
Cited by 1 | Viewed by 1843
Abstract
Remote sensing indices are widely used in various fields of geoscience research. However, there are limits to how effectively the knowledge of indices can be managed or analyzed. One of the main problems is the lack of ontology models and research on indices, [...] Read more.
Remote sensing indices are widely used in various fields of geoscience research. However, there are limits to how effectively the knowledge of indices can be managed or analyzed. One of the main problems is the lack of ontology models and research on indices, which makes it difficult to acquire and update knowledge in this area. Additionally, there is a lack of techniques to analyze the mathematical semantics of indices, making it difficult to directly manage and analyze their mathematical semantics. This study utilizes an ontology and mathematical semantics integration method to offer a novel knowledge graph for a remote sensing index knowledge graph (RSIKG) so as to address these issues. The proposed semantic hierarchical graph structure represents the indices of knowledge with an entity-relationship layer and a mathematical semantic layer. Specifically, ontologies in the entity-relationship layer are constructed to model concepts and relationships among indices. In the mathematical semantics layer, index formulas are represented using mathematical semantic graphs. A method for calculating similarity for index formulas is also proposed. The article describes the entire process of building RSIKG, including the extraction, storage, analysis, and inference of remote sensing index knowledge. Experiments provided in this article demonstrate the intuitive and practical nature of RSIKG for analyzing indices knowledge. Overall, the proposed methods can be useful for knowledge queries and the analysis of indices. And the present study lays the groundwork for future research on analysis techniques and knowledge processing related to remote sensing indices. Full article
(This article belongs to the Special Issue Incorporating Knowledge-Infused Approaches in Remote Sensing)
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19 pages, 4685 KiB  
Article
A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods
by Shi Bai and Jie Zhao
Remote Sens. 2023, 15(4), 930; https://doi.org/10.3390/rs15040930 - 8 Feb 2023
Cited by 3 | Viewed by 2468
Abstract
Geochemical data can reflect geological features, making it one of the basic types of geodata that have been widely used in mineral exploration, environmental assessment, resource potential analysis and other research. However, final decisions regarding activities are often limited by the spatial accuracy [...] Read more.
Geochemical data can reflect geological features, making it one of the basic types of geodata that have been widely used in mineral exploration, environmental assessment, resource potential analysis and other research. However, final decisions regarding activities are often limited by the spatial accuracy of geochemical data. Geochemical sampling is sometimes difficult to conduct because of harsh natural and geographic conditions (e.g., mountainous areas with high altitude and complex terrain), meaning that only medium/low-precision survey data could be obtained, which may not be adequate for regional geochemical mapping and exploration. Modern techniques such as remote sensing could be used to address this issue. In recent decades, the development of remote sensing technology has provided a huge amount of earth observation data with high spatial, temporal and spectral resolutions. The advantage of rapid acquisition of spatial and spectral information of large areas has promoted the broad use of remote sensing data in geoscientific research. Remote sensing data can help to differentiate various ground features by recording the electromagnetic response of the surface to solar radiation. Many problems that occur during the process of fusing remote sensing and geochemical data have been reported, such as the feasibility of existing fusion methods and low fusion accuracies that are less useful in practice. In this paper, a new strategy for integrating geochemical data and remote sensing data (referred to as ASTER data) is proposed; this strategy is achieved through linear regression as well as random forest and support vector regression algorithms. The results show that support vector regression can obtain better results for the available data sets and prove that the strategy currently proposed can effectively support the fusion of high-spatial-resolution remote sensing data (15 m) and low-spatial-resolution geochemical data (2000 m) in wide-range accurate geochemical applications (e.g., lithological identification and geochemical exploration). Full article
(This article belongs to the Special Issue Incorporating Knowledge-Infused Approaches in Remote Sensing)
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