From Imaging to Understanding: Methods and Application for Environment, Infrastructure and Human Monitoring

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Image and Video Processing".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1353

Special Issue Editors

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Guest Editor
1. State Research Institute of Aviation Systems, 125319 Moscow, Russia
2. Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia
Interests: computer vision; photogrammetry; remote sensing; 3D imaging; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Laboratorio de Geomática Andina, Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA), Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Mendoza CP 5500, Argentina
Interests: photogrammetry; change detection; geomatics

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Guest Editor
3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), 38123 Trento, Italy
Interests: photogrammetry; laser scanning; optical metrology; 3D; AI; quality control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Progress in imaging devices and means for image processing creates new conditions for retrieving information from images and image sequences. The possibility of acquiring and processing great amounts of time series data allows researchers to take a significant step from registration to intellectual monitoring and understanding various processes in environment, infrastructure, and human-related processes. The advances in these scientific areas will allow us to react timely to crucial changes to forecast their outcomes, and to take the necessary action to provide sustainable environmental health.

The idea to create this Special Issue was a result of discussions at the recent ISPRS workshop on “Photogrammetric and computer vision techniques for environmental and infrastructure monitoring, biometry and biomedicine – PSBB23”. It was devoted to new methods of image analysis focused on the understanding of various processes developing at the time.

This Special Issue addresses the methods, applications, and case studies of monitoring time-elongated processes, focusing on their analysis and understanding. We invite a wide range of researchers in the field of remote sensing data analysis to contribute to this Special Issue. Possible topics include (but are not limited to):

  • Image and image sequences analysis;
  • Machine learning methods for image series analysis;
  • Systems and applications of environmental monitoring;
  • Change detection;
  • Multi-modal and multi-scale data analysis;
  • Image classification and scene classification for environmental and
    infrastructure monitoring;
  • Methods for infrastructure monitoring;
  • Medical image and image sequences analysis for study of humans;
  • Methods for representing and visualizing time series data.

Original research on processing and analyzing spatial–temporal data are invited for publishing in the current Special Issue.

Dr. Vladimir Knyaz
Dr. María Gabriela Lenzano
Dr. Fabio Remondino
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. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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.


  • environmental and infrastructure monitoring
  • image classification
  • time series analysis
  • multi-scale and multi-modal monitoring

Published Papers (1 paper)

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17 pages, 3609 KiB  
Real-Time Dynamic Intelligent Image Recognition and Tracking System for Rockfall Disasters
by Yu-Wei Lin, Chu-Fu Chiu, Li-Hsien Chen and Chao-Ching Ho
J. Imaging 2024, 10(4), 78; - 26 Mar 2024
Viewed by 910
Taiwan, frequently affected by extreme weather causing phenomena such as earthquakes and typhoons, faces a high incidence of rockfall disasters due to its largely mountainous terrain. These disasters have led to numerous casualties, government compensation cases, and significant transportation safety impacts. According to [...] Read more.
Taiwan, frequently affected by extreme weather causing phenomena such as earthquakes and typhoons, faces a high incidence of rockfall disasters due to its largely mountainous terrain. These disasters have led to numerous casualties, government compensation cases, and significant transportation safety impacts. According to the National Science and Technology Center for Disaster Reduction records from 2010 to 2022, 421 out of 866 soil and rock disasters occurred in eastern Taiwan, causing traffic disruptions due to rockfalls. Since traditional sensors of disaster detectors only record changes after a rockfall, there is no system in place to detect rockfalls as they occur. To combat this, a rockfall detection and tracking system using deep learning and image processing technology was developed. This system includes a real-time image tracking and recognition system that integrates YOLO and image processing technology. It was trained on a self-collected dataset of 2490 high-resolution RGB images. The system’s performance was evaluated on 30 videos featuring various rockfall scenarios. It achieved a mean Average Precision (mAP50) of 0.845 and mAP50-95 of 0.41, with a processing time of 125 ms. Tested on advanced hardware, the system proves effective in quickly tracking and identifying hazardous rockfalls, offering a significant advancement in disaster management and prevention. Full article
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