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Deep Learning Innovations in Remote Sensing
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Deep Learning Innovations 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: 20 February 2025 | Viewed by 2848

Special Issue Editors

Dr. Zhe Wang

E-Mail Website
Guest Editor
National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, Santa Barbara, CA, USA
Interests: GIScience; remote sensing; deep learning
Special Issues, Collections and Topics in MDPI journals
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. Sanaz Salati

E-Mail Website
Guest Editor
Institute for Interdisciplinary Data Sciences (IIDS), University of Idaho, Moscow, ID 83844-1010, USA
Interests: remote sensing; GIScience; environmental science; data science; geography
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. Xiang Que

E-Mail Website
Guest Editor
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
Interests: spatial modeling; big data; machine learning; and data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Hui Wang

E-Mail Website
Guest Editor
Department of Geography and Planning, Appalachian State University, Boone, NC 28608, USA
Interests: GIS; geospatial analysis; climate change; hydrology; land use and land cover; health geography; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancements in deep learning techniques have revolutionized various fields, and remote sensing is no exception. This Special Issue aims to highlight the latest developments, applications, and challenges of deep learning in the realm of remote sensing. Advanced models, including Generative Adversarial Networks (GANs), Large Language Models (LLMs), and diffusion models, have demonstrated significant promise in extracting meaningful information from vast and complex remote sensing data. This has implications for numerous applications, including land cover classification, object detection, change detection, and anomaly detection.

This collection of articles seeks to bring together contributions from researchers around the globe to discuss innovations in network architectures, training methodologies, and data preprocessing techniques. We particularly encourage submissions that investigate applications in environmental, urban, or climate studies. Such investigations could delve into monitoring deforestation, assessing urban sprawl, predicting climate change impacts, and more. Furthermore, the issue will explore the integration of deep learning models with traditional methods, enhancing the accuracy and efficiency of remote sensing analyses. Challenges associated with data quality, computational costs, and model interpretability will also be addressed. By presenting state-of-the-art research and practical case studies, this Special Issue aims to serve as a valuable resource for scientists, engineers, and practitioners “dedicated to advancing the field of remote sensing through the power of deep learning.

Dr. Zhe Wang
Dr. Chao Fan
Dr. Sanaz Salati
Dr. Marshall (Xiaogang) Ma
Dr. Xiang Que
Dr. Hui 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

  • remote sensing
  • GeoAI
  • deep learning
  • machine learning
  • GAN, LLM, SAM, and generative AI
  • image processing and pattern recognition
  • artificial intelligence
  • GIS
  • geostatistics
  • spatial modeling

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Published Papers (3 papers)

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Research

32 pages, 16524 KiB  
Article
HI-CMAIM: Hybrid Intelligence-Based Multi-Source Unstructured Chinese Map Annotation Interpretation Model
by Jiaxin Ren, Wanzeng Liu, Jun Chen, Xiuli Zhu, Ran Li, Tingting Zhao, Jiadong Zhang, Yuan Tao, Shunxi Yin, Xi Zhai, Yunlu Peng and Xinpeng Wang
Remote Sens. 2025, 17(2), 204; https://doi.org/10.3390/rs17020204 - 8 Jan 2025
Viewed by 472
Abstract
Map annotation interpretation is crucial for geographic information extraction and intelligent map analysis. This study addresses the challenges associated with interpreting Chinese map annotations, specifically visual complexity and data scarcity issues, by proposing a hybrid intelligence-based multi-source unstructured Chinese map annotation interpretation method [...] Read more.
Map annotation interpretation is crucial for geographic information extraction and intelligent map analysis. This study addresses the challenges associated with interpreting Chinese map annotations, specifically visual complexity and data scarcity issues, by proposing a hybrid intelligence-based multi-source unstructured Chinese map annotation interpretation method (HI-CMAIM). Firstly, leveraging expert knowledge in an innovative way, we constructed a high-quality expert knowledge-based map annotation dataset (EKMAD), which significantly enhanced data diversity and accuracy. Furthermore, an improved annotation detection model (CMA-DB) and an improved annotation recognition model (CMA-CRNN) were designed based on the characteristics of map annotations, both incorporating expert knowledge. A two-stage transfer learning strategy was employed to tackle the issue of limited training samples. Experimental results demonstrated the superiority of HI-CMAIM over existing algorithms. In the detection task, CMA-DB achieved an 8.54% improvement in Hmean (from 87.73% to 96.27%) compared to the DB algorithm. In the recognition task, CMA-CRNN achieved a 15.54% improvement in accuracy (from 79.77% to 95.31%) and a 4-fold reduction in NED (from 0.1026 to 0.0242), confirming the effectiveness and advancement of the proposed method. This research not only provides a novel approach and data support for Chinese map annotation interpretation but also fills the gap of high-quality, diverse datasets. It holds practical application value in fields such as geographic information systems and cartography, significantly contributing to the advancement of intelligent map interpretation. Full article
(This article belongs to the Special Issue Deep Learning Innovations in Remote Sensing)
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19 pages, 349 KiB  
Article
Ethical Framework to Assess and Quantify the Trustworthiness of Artificial Intelligence Techniques: Application Case in Remote Sensing
by Marina Paolanti, Simona Tiribelli, Benedetta Giovanola, Adriano Mancini, Emanuele Frontoni and Roberto Pierdicca
Remote Sens. 2024, 16(23), 4529; https://doi.org/10.3390/rs16234529 - 3 Dec 2024
Cited by 1 | Viewed by 863
Abstract
In the rapidly evolving field of remote sensing, Deep Learning (DL) techniques have become pivotal in interpreting and processing complex datasets. However, the increasing reliance on these algorithms necessitates a robust ethical framework to evaluate their trustworthiness. This paper introduces a comprehensive ethical [...] Read more.
In the rapidly evolving field of remote sensing, Deep Learning (DL) techniques have become pivotal in interpreting and processing complex datasets. However, the increasing reliance on these algorithms necessitates a robust ethical framework to evaluate their trustworthiness. This paper introduces a comprehensive ethical framework designed to assess and quantify the trustworthiness of DL techniques in the context of remote sensing. We first define trustworthiness in DL as a multidimensional construct encompassing accuracy, reliability, transparency and explainability, fairness, and accountability. Our framework then operationalizes these dimensions through a set of quantifiable metrics, allowing for the systematic evaluation of DL models. To illustrate the applicability of our framework, we selected an existing case study in remote sensing, wherein we apply our ethical assessment to a DL model used for classification. Our results demonstrate the model’s performance across different trustworthiness metrics, highlighting areas for ethical improvement. This paper not only contributes a novel framework for ethical analysis in the field of DL, but also provides a practical tool for developers and practitioners in remote sensing to ensure the responsible deployment of DL technologies. Through a dual approach that combines top-down international standards with bottom-up, context-specific considerations, our framework serves as a practical tool for ensuring responsible AI applications in remote sensing. Its application through a case study highlights its potential to influence policy-making and guide ethical AI development in this domain. Full article
(This article belongs to the Special Issue Deep Learning Innovations in Remote Sensing)
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30 pages, 17378 KiB  
Article
High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN
by Yue Li, Xiaorui Wang, Chao Zhang, Zhonggen Zhang and Fafa Ren
Remote Sens. 2024, 16(23), 4350; https://doi.org/10.3390/rs16234350 - 21 Nov 2024
Cited by 1 | Viewed by 599
Abstract
 To overcome the problems in existing infrared remote sensing image generation methods, which make it difficult to combine high fidelity and high efficiency, we propose a High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN (HFIRSIGM_GRSMP) [...] Read more.
 To overcome the problems in existing infrared remote sensing image generation methods, which make it difficult to combine high fidelity and high efficiency, we propose a High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN (HFIRSIGM_GRSMP) in this paper. Firstly, based on the global radiation scattering mechanism, the HFIRSIGM_GRSMP model is constructed to address the problem of accurately characterizing factors that affect fidelity—such as the random distribution of the radiation field, multipath scattering, and nonlinear changes—through the innovative fusion of physical models and deep learning. This model accurately characterizes the complex radiation field distribution and the image detail-feature mapping relationship from visible-to-infrared remote sensing. Then, 8000 pairs of image datasets were constructed based on Landsat 8 and Sentinel-2 satellite data. Finally, the experiment demonstrates that the average SSIM of images generated using HFIRSIGM_GRSMP reaches 89.16%, and all evaluation metrics show significant improvement compared to the contrast models. More importantly, this method demonstrates high accuracy and strong adaptability in generating short-wave, mid-wave, and long-wave infrared remote sensing images. This method provides a more comprehensive solution for generating high-fidelity infrared remote sensing images.  Full article
(This article belongs to the Special Issue Deep Learning Innovations in Remote Sensing)
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