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Drones
Special Issues
Rapid Disaster Assessment and Post-Disaster Recovery Using UAV Remote Sensing
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Rapid Disaster Assessment and Post-Disaster Recovery Using UAV Remote Sensing

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Ecology".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1294

Special Issue Editors

Dr. Alessio Calantropio

E-Mail Website
Guest Editor
Department of Science and Technology (DIST), University of Napoli Parthenope, Napoli, Italy
Interests: target recognition; marine sensing
Dr. Vincenzo Di Pietra

E-Mail Website
Guest Editor
Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, 10129 Torino, Italy
Interests: UWB; seamless navigation; GNSS; UAV; photogrammetry
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel Angel Maté-González

E-Mail Website
Guest Editor
Departamento de Ingeniería Cartográfica y del Terreno, Universidad de Salamanca, 37008 Salamanca, Spain
Interests: cartographic; geodetic and photogrammetric engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural and man-made disasters continue to pose critical threats to populations and infrastructure across the globe. Rapid, precise, and scalable methods for damage assessment and post-disaster monitoring are urgently needed. UAV-based remote sensing technologies have emerged as a transformative solution due to their ability to quickly reach affected areas, provide high-resolution imagery, and support diverse sensing modalities. These capabilities enable not only efficient initial assessments but also long-term monitoring of reconstruction and recovery efforts.

This Special Issue of Drones aims to collect original research articles and review papers focusing on the use of UAV-based remote sensing for disaster response and recovery. Contributions should explore both theoretical advancements and practical implementations, highlighting the role of UAVs in supporting resilient, informed, and sustainable disaster management strategies. The proposed theme aligns closely with the journal’s scope by showcasing innovative drone applications for remote sensing in high-impact real-world scenarios.

  • Rapid mapping techniques using UAVs for post-disaster assessment;
  • 3D reconstruction of damaged environments;
  • Change detection and semantic segmentation in disaster zones;
  • Integration of UAV data with GIS and satellite data;
  • Machine learning and AI applications for UAV data analysis;
  • Multi-sensor UAV platforms for rescue and recovery;                 
  • Case studies on real-world disaster response using drones;
  • Legal, ethical, and logistical considerations in UAV emergency deployments.

We welcome original research papers, comprehensive reviews, methodological contributions, and field studies.

Dr. Alessio Calantropio
Dr. Vincenzo Di Pietra
Dr. Miguel Angel Maté-González
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 250 words) can be sent to the Editorial Office for assessment.

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. Drones 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 2600 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

  • UAV photogrammetry
  • rapid mapping
  • disaster response
  • post-disaster recovery
  • change detection
  • AI and remote sensing
  • multi-sensor platforms
  • emergency mapping
  • UAV-GIS integration
  • damage assessment

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

25 pages, 8947 KB  
Article
Advancing Real-Time Aerial Wildfire Detection Through Plume Recognition and Knowledge Distillation
by Pirunthan Keerthinathan, Juan Sandino, Sutharsan Mahendren, Anuraj Uthayasooriyan, Julian Galvez, Grant Hamilton and Felipe Gonzalez
Drones 2025, 9(12), 827; https://doi.org/10.3390/drones9120827 - 28 Nov 2025
Viewed by 195
Abstract
Uncrewed aerial systems (UAS)-based remote sensing and artificial intelligence (AI) analysis enable real-time wildfire or bushfire detection, facilitating early response to minimize damage and protect lives and property. However, their effectiveness is limited by three issues: distinguishing smoke from fog, the high cost [...] Read more.
Uncrewed aerial systems (UAS)-based remote sensing and artificial intelligence (AI) analysis enable real-time wildfire or bushfire detection, facilitating early response to minimize damage and protect lives and property. However, their effectiveness is limited by three issues: distinguishing smoke from fog, the high cost of manual annotation, and the computational demands of large models. This study addresses the three key challenges by introducing plume as a new indicator to better distinguish smoke from similar visual elements, and by employing a hybrid annotation method using knowledge distillation (KD) to reduce expert labour and accelerate labelling. Additionally, it leverages lightweight YOLO Nano models trained with pseudo-labels generated from a fine-tuned teacher network to lower computational demands while maintaining high detection accuracy for real-time wildfire monitoring. Controlled pile burns in Canungra, QLD, Australia, were conducted to collect UAS-captured images over deciduous vegetation, which were subsequently augmented with the Flame2 dataset, which contains wildfire images of coniferous vegetation. A Grounding DINO model, fine-tuned using few-shot learning, served as the teacher network to generate pseudo-labels for a significant portion of the Flame2 dataset. These pseudo-labels were then used to train student networks consisting of YOLO Nano architectures, specifically versions 5, 8, and 11 (YOLOv5n, YOLOv8n, YOLOv11n). The experimental results show that YOLOv8n and YOLOv5n achieved an mAP@0.5 of 0.721. Plume detection outperforms smoke indicators (F1: 76.1–85.7% vs. 70%) in fog and wildfire scenarios. These findings underscore the value of incorporating plume as a distinct class and utilizing KD, both of which enhance detection accuracy and scalability, ultimately supporting more reliable and timelier wildfire monitoring and response. Full article
(This article belongs to the Special Issue Rapid Disaster Assessment and Post-Disaster Recovery Using UAV Remote Sensing)
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34 pages, 6981 KB  
Article
Increasing Automation on Mission Planning for Heterogeneous Multi-Rotor Drone Fleets in Emergency Response
by Ilham Zerrouk, Esther Salamí, Cristina Barrado, Gautier Hattenberger and Enric Pastor
Drones 2025, 9(12), 816; https://doi.org/10.3390/drones9120816 - 24 Nov 2025
Viewed by 437
Abstract
Drones are increasingly vital for disaster management, yet emergency fleets often consist of heterogeneous platforms, complicating task allocation. Efficient deployment requires rapid assignment based on vehicle and payload characteristics. This work proposes a three-step method composed of fleet analysis, area decomposition and trajectory [...] Read more.
Drones are increasingly vital for disaster management, yet emergency fleets often consist of heterogeneous platforms, complicating task allocation. Efficient deployment requires rapid assignment based on vehicle and payload characteristics. This work proposes a three-step method composed of fleet analysis, area decomposition and trajectory generation for multi-rotor drone surveillance, aiming to achieve complete area coverage in minimal time while respecting no-fly zones. The three-step method generates optimized trajectories for all drones in less than 2 min, ensuring uniform precision and reduced flight distance compared to state-of-the-art methods, achieving mean distance gains of up to 9.31% with a homogeneous fleet of 10 drones. Additionally, a comparative analysis of area partitioning algorithms reveals that simplifying the geometry of the surveillance region can lead to more effective divisions and less complex trajectories. This simplification results in approximately 8.4% fewer turns, even if it slightly increases the total area to be covered. Full article
(This article belongs to the Special Issue Rapid Disaster Assessment and Post-Disaster Recovery Using UAV Remote Sensing)
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