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Special Issue "Recent Progress in UAV-AI Remote Sensing II"

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

Deadline for manuscript submissions: 10 December 2023 | Viewed by 753

Special Issue Editors

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: hyperspectral remote sensing; multispectral remote sensing; precision agriculture; data processing; data assimilation; pests and diseases; habitat monitoring; risk forecasting
Special Issues, Collections and Topics in MDPI journals
U.S. Department of Agriculture, 3103 F&B Road, College Station, TX 77845, USA
Interests: precision agriculture; pest management; airborne; image processing; multispectral, hyperspectral and thermal imaging systems; unmanned aircraft systems; electronic and spectral sensors
Special Issues, Collections and Topics in MDPI journals
Earth Observation and Satellite Image Applications Laboratory (EOSIAL), School of Aerospace Engineering (SIA), Università di Roma ‘La Sapienza’, Via Salaria, 851, 00138 Roma, Italy
Interests: vegetation remote sensing; disease monitoring; disaster early warning; hyperspectral remote sensing; UAV data processing; assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of Unmanned Aerial Vehicles (UAV) and Artificial Intelligence (AI) technologies has generated increasing interest and started a new field of research applications. Faced with precise or incidental needs in different research areas such as energy, construction, security, agriculture, forestry and ecology, UAV imagery provides the research community with a large amount of widely sourced, real-time and high-resolution data, followed by effective and efficient data mining using AI to obtain new, implicit or useful information to support practical guidance and further applications. Combining the advantages of UAVs and AI enables automated and rapid processing and modelling to gain immediate and spatio-temporally variable knowledge of the target area and significantly reduce the workload of operators and instructors. In order to build a UAV-AI system for solving complex problems, researchers will perform a full range of tasks from data collection to model building, and the achievement of any task will contribute to the development of UAV-AI.

This special issue aims at research covering the interpretation of data obtained from different UAV sensors using artificial intelligence techniques. Research on the integration of multi-source, multi-temporal or multi-scale UAV imagery (e.g. multispectral, hyperspectral, thermal imaging, LiDAR, etc.) and multiple AI domains (e.g. deep learning, reinforcement learning and joint learning) is welcome and aims to address challenges or bottlenecks in each domain. Articles may address, but are not limited to, the following topics:Data processing (multispectral, hyperspectral, thermal, LiDAR, etc.)

  • Real-time object detection, counting, segmentation and tracking
  • Change detection in land, forest, grass
  • Pests, disease, and other disasters monitoring
  • AI algorithms for UAV data
  • UAV-AI system development
  • UAV-AI applications

Dr. Yingying Dong
Dr. Chenghai Yang
Dr. Giovanni Laneve
Prof. Dr. Wenjiang Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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.


  • unmanned aerial vehicles
  • artificial intelligence
  • data fusion
  • data mining
  • UAV-AI algorithms
  • real-time processing
  • change detection
  • disaster monitoring
  • system and applications

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Published Papers (1 paper)

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23 pages, 12081 KiB  
Regional-Scale Monitoring of Wheat Stripe Rust Using Remote Sensing and Geographical Detectors
Remote Sens. 2023, 15(18), 4631; - 21 Sep 2023
Viewed by 463
Realizing the high-precision monitoring of wheat stripe rust over a large area is of great significance in ensuring the safety of wheat production. Existing studies have mostly focused on the fusion of multi-source data and the construction of key monitoring features to improve [...] Read more.
Realizing the high-precision monitoring of wheat stripe rust over a large area is of great significance in ensuring the safety of wheat production. Existing studies have mostly focused on the fusion of multi-source data and the construction of key monitoring features to improve the accuracy of disease monitoring, with less consideration for the regional distribution characteristics of the disease. In this study, based on the occurrence and spatial distribution patterns of wheat stripe rust in the experimental area, we constructed a multi-source monitoring feature set, then utilized geographical detectors for feature selection that integrates the spatial-distribution differences of the disease. The research results show that the optimal monitoring feature set selected by the geographical detectors has a higher monitoring accuracy. Based on the Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector (SVM) models, the disease monitoring results demonstrate that the monitoring feature set constructed in this study has an overall accuracy in its disease monitoring that is 3.2%, 2.7%, and 4.3% higher, respectively, than that of the ReliefF method, with Kappa coefficient higher by 0.064, 0.044, and 0.087, respectively. Furthermore, the optimal monitoring feature set obtained by the geographical detectors method exhibits a higher stability, and the spatial distribution of wheat stripe rust in the monitoring results generated by the different models demonstrates good consistency. In contrast, the features selected by the ReliefF method exhibit significant spatial-distribution differences in the wheat stripe rust among the different monitoring results, indicating poor stability and consistency. Overall, incorporating information on disease spatial-distribution differences in stripe-rust monitoring can improve the accuracy and stability of disease monitoring, and it can provide data and methodological support for regional stripe-rust detection and accurate preventions. Full article
(This article belongs to the Special Issue Recent Progress in UAV-AI Remote Sensing II)
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