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Remote Sensing
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AI-Driven Mapping Using Remote Sensing Data
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AI-Driven Mapping Using Remote Sensing Data

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

Deadline for manuscript submissions: 15 September 2024 | Viewed by 1905

Special Issue Editors

Dr. Li Fang

E-Mail Website
Guest Editor
Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, China
Interests: photogrammetry and remote sensing technology; intelligent spatial information processing and application; point cloud processing and application
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Yang

E-Mail
Guest Editor
School of Geospatial Information, Information Engineering University, Zhengzhou 450002, China
Interests: GeoAI; crowdsourced mapping; cognitive mapping for unmanned system; spatial data mining; location-based service
Special Issues, Collections and Topics in MDPI journals
Dr. Yu Feng

E-Mail Website
Guest Editor
Chair of Cartography and Visual Analytics, Technical University of Munich, 80333 Munich, Germany
Interests: volunteered geographic information; point cloud processing; cartographic generalization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mapping with remote sensing data collected from spaceborne, airborne, and terrestrial platforms has enriched the understanding of the dynamic environment and facilitated decision-making in geospatial domains. With the fast development of AI techniques such as deep learning, knowledge graphs, and large language models (or foundation models), mapping with remote sensing data has reached unprecedented levels of resolution, accuracy, semantic richness, and automation. The transfer continuum of AI advancements to the full pipeline of remote sensing data processing has become a revolutionary endeavor in modern remote sensing research. To facilitate such a research paradigm, it is worthwhile to continuously shed light on the novel applications of AI-driven mapping using remote sensing data, as well as stimulate reflective investigations of the model design principles and benchmarking basis.

This Special Issue will study the AI-driven mapping of remote sensing data by considering novel applications, model design principles, and benchmarking model performances. This Special Issue may cover topics related to AI-driven research into task-oriented remote sensing data processing and applications, data-oriented model design, and benchmark dataset construction and assessment. Articles may address, but are not limited to, the following topics:

  • AI-driven interpretation of remote sensing images;
  • AI-driven dynamic monitoring of land cover and land use using remote sensing data;
  • AI-driven data fusion of remote sensing data and volunteered geographic information;
  • AI-driven geographic registration of remote sensing data;
  • AI-driven urban modeling using remote sensing and geospatial data;
  • AI-driven environment sensing using mobile sensing data;
  • Spatially explicit AI-driven method using remote sensing and geospatial data;
  • Crowdsourcing labels for AI-driven methods using remote sensing and geospatial data.

Dr. Li Fang
Dr. Jian Yang
Dr. Yu Feng
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

  • AI-driven remote sensing
  • image interpretation
  • change detection
  • urban modeling
  • spatially explicit AI
  • crowdsourced label

Published Papers (3 papers)

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Research

23 pages, 6165 KiB  
Article
RIRNet: A Direction-Guided Post-Processing Network for Road Information Reasoning
by Guoyuan Zhou, Changxian He, Hao Wang, Qiuchang Xie, Qiong Chen, Liang Hong and Jie Chen
Remote Sens. 2024, 16(14), 2666; https://doi.org/10.3390/rs16142666 - 21 Jul 2024
Viewed by 373
Abstract
Road extraction from high-resolution remote sensing images (HRSIs) is one of the tasks in image analysis. Deep convolutional neural networks have become the primary method for road extraction due to their powerful feature representation capability. However, roads are often obscured by vegetation, buildings, [...] Read more.
Road extraction from high-resolution remote sensing images (HRSIs) is one of the tasks in image analysis. Deep convolutional neural networks have become the primary method for road extraction due to their powerful feature representation capability. However, roads are often obscured by vegetation, buildings, and shadows in HRSIs, resulting in incomplete and discontinuous road extraction results. To address this issue, we propose a lightweight post-processing network called RIRNet in this study, which include an information inference module and a road direction inference task branch. The information inference module can infer spatial information relationships between different rows or columns of feature images from different directions, effectively inferring and repairing road fractures. The road direction inference task branch performs the road direction prediction task, which can constrain and promote the road extraction task, thereby indirectly enhancing the inference ability of the post-processing model and realizing the optimization of the initial road extraction results. Experimental results demonstrate that the RIRNet model can achieve an excellent post-processing effect, which is manifested in the effective repair of broken road segments, as well as the handling of errors such as omission, misclassification, and noise, proving the effectiveness and generalization of the model in post-processing optimization. Full article
(This article belongs to the Special Issue AI-Driven Mapping Using Remote Sensing Data)
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18 pages, 5061 KiB  
Article
Generating 10-Meter Resolution Land Use and Land Cover Products Using Historical Landsat Archive Based on Super Resolution Guided Semantic Segmentation Network
by Dawei Wen, Shihao Zhu, Yuan Tian, Xuehua Guan and Yang Lu
Remote Sens. 2024, 16(12), 2248; https://doi.org/10.3390/rs16122248 - 20 Jun 2024
Viewed by 451
Abstract
Generating high-resolution land cover maps using relatively lower-resolution remote sensing images is of great importance for subtle analysis. However, the domain gap between real lower-resolution and synthetic images has not been permanently resolved. Furthermore, super-resolution information is not fully exploited in semantic segmentation [...] Read more.
Generating high-resolution land cover maps using relatively lower-resolution remote sensing images is of great importance for subtle analysis. However, the domain gap between real lower-resolution and synthetic images has not been permanently resolved. Furthermore, super-resolution information is not fully exploited in semantic segmentation models. By solving the aforementioned issues, a deeply fused super resolution guided semantic segmentation network using 30 m Landsat images is proposed. A large-scale dataset comprising 10 m Sentinel-2, 30 m Landsat-8 images, and 10 m European Space Agency (ESA) Land Cover Product is introduced, facilitating model training and evaluation across diverse real-world scenarios. The proposed Deeply Fused Super Resolution Guided Semantic Segmentation Network (DFSRSSN) combines a Super Resolution Module (SRResNet) and a Semantic Segmentation Module (CRFFNet). SRResNet enhances spatial resolution, while CRFFNet leverages super-resolution information for finer-grained land cover classification. Experimental results demonstrate the superior performance of the proposed method in five different testing datasets, achieving 68.17–83.29% and 39.55–75.92% for overall accuracy and kappa, respectively. When compared to ResUnet with up-sampling block, increases of 2.16–34.27% and 8.32–43.97% were observed for overall accuracy and kappa, respectively. Moreover, we proposed a relative drop rate of accuracy metrics to evaluate the transferability. The model exhibits improved spatial transferability, demonstrating its effectiveness in generating accurate land cover maps for different cities. Multi-temporal analysis reveals the potential of the proposed method for studying land cover and land use changes over time. In addition, a comparison of the state-of-the-art full semantic segmentation models indicates that spatial details are fully exploited and presented in semantic segmentation results by the proposed method. Full article
(This article belongs to the Special Issue AI-Driven Mapping Using Remote Sensing Data)
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19 pages, 6973 KiB  
Article
The Diverse Mountainous Landslide Dataset (DMLD): A High-Resolution Remote Sensing Landslide Dataset in Diverse Mountainous Regions
by Jie Chen, Xu Zeng, Jingru Zhu, Ya Guo, Liang Hong, Min Deng and Kaiqi Chen
Remote Sens. 2024, 16(11), 1886; https://doi.org/10.3390/rs16111886 - 24 May 2024
Viewed by 675
Abstract
The frequent occurrence of landslides poses a serious threat to people’s lives and property. In order to evaluate disaster hazards based on remote sensing images via machine learning means, it is essential to establish an image database with manually labeled boundaries of landslides. [...] Read more.
The frequent occurrence of landslides poses a serious threat to people’s lives and property. In order to evaluate disaster hazards based on remote sensing images via machine learning means, it is essential to establish an image database with manually labeled boundaries of landslides. However, the existing datasets do not cover diverse types of mountainous landslides. To address this issue, we propose a high-resolution (1 m) diverse mountainous landslide remote sensing dataset (DMLD), including 990 landslide instances across different terrain in southwestern China. To evaluate the performance of the DMLD, seven state-of-the-art deep learning models with different loss functions were implemented on it. The experiment results demonstrate not only that all of these deep learning methods with different characteristics can adapt well to the DMLD, but also that the DMLD has potential adaptability to other geographical regions. Full article
(This article belongs to the Special Issue AI-Driven Mapping Using Remote Sensing Data)
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