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Applied Sciences
Special Issues
UAV Applications in Environmental Monitoring
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UAV Applications in Environmental Monitoring

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 1628

Special Issue Editor

Dr. Pawel Tysiac

E-Mail Website
Guest Editor
Gdansk University of Technology, Faculty of Civil and Environmental Engineering, Department of Geodesy, PL-80-233 Gdansk, Poland
Interests: environmental monitoring; UAV photogrammetry; coastal research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) have potential that is noticeable around the world. Rapid technological growth, combined with relatively low equipment costs, has made empirical studies of the environment common, and case studies appearing in the literature allow for a better understanding of global phenomena related to climate change, as an example. Popular drones, thanks to their universality, enable the assembly and observation of virtually the entire electromagnetic wave spectrum (from RGB cameras to multi- and hyperspectral cameras, ending with thermal cameras and laser scanners). The multitude of emerging applications is enormous. For this reason, this Special Issue of Applied Sciences is being launched to show the wide sensory application in practical terms of environmental monitoring and data processing methods depending on the type of sensor, and to show sensory characteristics depending on the sensor mounted on the unmanned vehicle. I look forward to interesting articles and interesting cooperation, and invite you to submit your works for publication.

 

This Special Issue includes, but is not limited to:

  1. Optimization of flight planning for environmental monitoring tasks depending on the sensor used.
  2. Methods of using UAV sensor aggregation to monitor the environment.
  3. Methods for the calibration and processing of data from UAV sensors for environmental monitoring.
  4. Research on the precision and accuracy of acquired UAV sensory data.

Case studies combining several of the above points are particularly welcome.

Dr. Pawel Tysiac
Guest Editor

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.

Dr. Pawel Tysiac
Guest Editor

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

  • environmental monitoring
  • remote sensing
  • UAV
  • image processing
  • point cloud segmentation

Published Papers (1 paper)

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0 pages, 3148 KiB  
Article
Vision-Language Models for Zero-Shot Classification of Remote Sensing Images
by Mohamad Mahmoud Al Rahhal, Yakoub Bazi, Hebah Elgibreen and Mansour Zuair
Appl. Sci. 2023, 13(22), 12462; https://doi.org/10.3390/app132212462 - 17 Nov 2023
Viewed by 1388
Abstract
Zero-shot classification presents a challenge since it necessitates a model to categorize images belonging to classes it has not encountered during its training phase. Previous research in the field of remote sensing (RS) has explored this task by training image-based models on known [...] Read more.
Zero-shot classification presents a challenge since it necessitates a model to categorize images belonging to classes it has not encountered during its training phase. Previous research in the field of remote sensing (RS) has explored this task by training image-based models on known RS classes and then attempting to predict the outcomes for unfamiliar classes. Despite these endeavors, the outcomes have proven to be less than satisfactory. In this paper, we propose an alternative approach that leverages vision-language models (VLMs), which have undergone pre-training to grasp the associations between general computer vision image-text pairs in diverse datasets. Specifically, our investigation focuses on thirteen VLMs derived from Contrastive Language-Image Pre-Training (CLIP/Open-CLIP) with varying levels of parameter complexity. In our experiments, we ascertain the most suitable prompt for RS images to query the language capabilities of the VLM. Furthermore, we demonstrate that the accuracy of zero-shot classification, particularly when using large CLIP models, on three widely recognized RS scene datasets yields superior results compared to existing RS solutions. Full article
(This article belongs to the Special Issue UAV Applications in Environmental Monitoring)
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