Special Issue "Remote Sensing Images Processing for Disasters Response"

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

Deadline for manuscript submissions: closed (31 March 2021).

Special Issue Editors

Prof. Piero Boccardo
E-Mail Website
Guest Editor
DIST - Politecnico di Torino, Viale Mattioli 39, 10125, Torino, Italy
Interests: emergency management; mobility applications; geomatics for energy; remote sensing and climate change
Dr. Andrea Ajmar
E-Mail Website
Guest Editor
DIST - Politecnico di Torino, Viale Mattioli 39, 10125, Torino, Italy
Interests: emergency mapping; spatial data infrastructure; feature extraction; data quality; mobility services

Special Issue Information

Dear Colleagues,

Remote sensing plays a major role during the disaster response phase, providing “emergency services and public assistance during or immediately after a disaster to save lives, reduce health impacts, ensure public safety and meet the basic subsistence needs of the people affected’’ (UNISDR 2009). This is mainly due to the large and timely availability of different types of remotely sensed data – as well as geospatial information acquired in the field – which can be exploited in the different phases of the disaster management cycle, and are particularly beneficial in the response phase. Several operational mechanisms provide post-event analysis, in the form of data and ready-to-print maps, within hours, from image availability. Satellites (both active and passive sensors) are the main acquisition platform, but aerial (manned and unmanned) platforms are also used to overcome some of the satellite platforms limitations.

The rapid release of data and information is one of the most critical aspects and therefore all the emergency workflow steps should be optimized against this factor. Automatic and semi-automatic extraction processes by means of different techniques (e.g., simple thresholding, indices, machine learning, and deep learning) for event delineation and damage level estimation, should be foreseen to reduce the production and the human interpretation (based on CAPI) times. The integration of unstructured information (i.e., those derived by traditional media and social media) may be useful to increase automatic processes accuracy. Adequately organized and structured collaborative mapping initiatives may provide relevant resources to process and analyze large volumes of data. The perspective of the enlarged stakeholder community, including final users (e.g., civil protection authorities, international organizations), service providers (e.g., Copernicus Emergency Management Service, International Charter Space and Major Disasters, UNOSAT), and relevant international working groups (e.g., the International Working Group on Satellite-Based Emergency Mapping), is considered extremely relevant and to be taken into consideration while developing and proposing new solutions.

This Special Issue will promote and disseminate interdisciplinary research on how to maximize the efficiency of remote sensing techniques in disaster response phases, with the aim of bridging current gaps in respect to the requirements set by the responders.

This Special Issue welcomes contributions from the remote sensing community, in the form of pure and applied research, aimed at providing solutions supporting any step of a disaster response workflow. A non-exhaustive list of analysis steps based on remote sensing techniques includes the following:

  1. Data tasking optimization
  2. Procedure standardization and technical specifications
  3. Effective and efficient access to available reference data
  4. Post-event data acquisition
  5. Imagery pre-processing
  6. Data integration and fusion
  7. Innovative procedures for feature extraction
  8. Emergency crowdmapping
  9. Data publication and dissemination
  10. Best practices and operational examples

Prof. Piero Boccardo
Dr. Andrea Ajmar
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 papers will be 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 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

  • Emergency response
  • remote sensing
  • automatic processing
  • deep learning
  • geomatics
  • spatial information science
  • technical specifications and best practices
  • data acquisition and fusion

Published Papers (4 papers)

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Research

Article
Near-Real-Time Flood Mapping Using Off-the-Shelf Models with SAR Imagery and Deep Learning
Remote Sens. 2021, 13(12), 2334; https://doi.org/10.3390/rs13122334 - 14 Jun 2021
Viewed by 713
Abstract
Timely detection of flooding is paramount for saving lives as well as evaluating levels of damage. Floods generally occur under specific weather conditions, such as excessive precipitation, which makes the presence of clouds very likely. For this reason, radar-based sensors are most suitable [...] Read more.
Timely detection of flooding is paramount for saving lives as well as evaluating levels of damage. Floods generally occur under specific weather conditions, such as excessive precipitation, which makes the presence of clouds very likely. For this reason, radar-based sensors are most suitable for near-real-time flood mapping. The public dataset Sen1Floods11 recently released by the Cloud to Street is one example of ongoing beneficial initiatives to employ deep learning for flood detection with synthetic aperture radar. The present study used this dataset to improve flood detection using well-known segmentation architectures, such as SegNet and UNet, as networks. In addition, this study provided a deeper understanding of which set of polarized band combination is more suitable for distinguishing permanent water, as well as flooded areas from the SAR image. The overall performance of the models with various kinds of labels and a combination of bands to detect all surface water areas were also assessed. Finally, the trained models were tested on a completely different location at Kerala, India, during the 2018 flood for verifying their performance in the real-world situation of a flood event outside of the given test set in the dataset. The results prove that trained models can be used as off-the-shelf models to achieve an intersection over union (IoU) as high as 0.88 in comparison with optical images. The omission and commission error were less than 6%. However, the most important result is that the processing time for the whole satellite image was less than 1 min. This will help significantly for providing analysis and near-real-time flood mapping services to first responder organizations during flooding disasters. Full article
(This article belongs to the Special Issue Remote Sensing Images Processing for Disasters Response)
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Article
Increasing Timeliness of Satellite-Based Flood Mapping Using Early Warning Systems in the Copernicus Emergency Management Service
Remote Sens. 2021, 13(11), 2114; https://doi.org/10.3390/rs13112114 - 27 May 2021
Viewed by 884
Abstract
Remotely sensed images have become an important source of information for actors involved in disaster management and satellite-based emergency mapping (SEM) is increasingly used to support the response phase in the first hours and days after a disaster occurs. The delivery timeliness of [...] Read more.
Remotely sensed images have become an important source of information for actors involved in disaster management and satellite-based emergency mapping (SEM) is increasingly used to support the response phase in the first hours and days after a disaster occurs. The delivery timeliness of the crisis information is key to the success of SEM. In the Copernicus Emergency Management Service (CEMS), a procedure was tested during the past 5 years which links the European Flood Awareness System (EFAS) with the on-demand Rapid Mapping module in order to anticipate satellite tasking in view of an upcoming activation of the service for riverine floods. This study aims at assessing if the procedure has helped to improve the delivery timeliness of the first flood impact information. For the assessment, we used the Rapid Mapping performance statistics recorded from June 2016 to December 2020. Standard Rapid Mapping activations for floods were compared with those preceded by an EFAS based pre-tasking request. The focus was on essential time stamps such as activation start, provision of imagery and the availability of derived information products. For the pre-tasking-related activations, we further compared the EFAS predictions with the Rapid Mapping user request and compared flood predictions with actual observations. Our results show that the EFAS based pre-tasking improves the timeliness of the first product delivery due to the fact that satellite images could be acquired earlier compared to activations without pre-tasking. Full article
(This article belongs to the Special Issue Remote Sensing Images Processing for Disasters Response)
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Article
Satellite-Derived Variation in Burned Area in China from 2001 to 2018 and Its Response to Climatic Factors
Remote Sens. 2021, 13(7), 1287; https://doi.org/10.3390/rs13071287 - 28 Mar 2021
Viewed by 544
Abstract
Fire is one of the most widespread and destructive disasters, which causes property losses, casualties, and disruption of the balance of ecosystems. Therefore, it is highly necessary for firefighting to study the variations in fire and its climatic attributions. This study analyzed the [...] Read more.
Fire is one of the most widespread and destructive disasters, which causes property losses, casualties, and disruption of the balance of ecosystems. Therefore, it is highly necessary for firefighting to study the variations in fire and its climatic attributions. This study analyzed the characteristics of fire-burned area (BA) and its response to climatic factors in seven subregions of China from 2001 to 2018 using satellite remote sensing BA products. The results show that the BA in China and most of its subregions shows a decreasing trend. In general, it is negatively correlated with precipitation and positively correlated with air temperature and wind speed based on the regression and correlation analyses. Based on Pearson correlation and random forest methods, it is also found that the temperature is commonly an important factor contributing to BA in China, except for R2 (Inner Mongolia region), where wind speed is more important, and R5 (South China), where precipitation is more important, which coexists at annual and seasonal scales. Besides temperature, precipitation in spring and summer is the main driving factor, such as in R1 (Northeast China), R5, R6 (Northwest China) and R7 (Qinghai–Tibet Plateau) in spring and R4 (Central China), R5 and R7 in summer; and wind speed in autumn and winter is the main driving factor, such as in R2 and R4 in autumn and R2, R3, R5, R6 and R7 in winter. Finally, the distributions of BA with respect to each climatic factor were also analyzed to quantify the range of climatic factors with maximum BA occurrence. Full article
(This article belongs to the Special Issue Remote Sensing Images Processing for Disasters Response)
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Article
Robust Mosaicking of Lightweight UAV Images Using Hybrid Image Transformation Modeling
Remote Sens. 2020, 12(6), 1002; https://doi.org/10.3390/rs12061002 - 20 Mar 2020
Cited by 2 | Viewed by 948
Abstract
This paper proposes a robust feature-based mosaicking method that can handle images obtained by lightweight unmanned aerial vehicles (UAVs). The imaging geometry of small UAVs can be characterized by unstable flight attitudes and low flight altitudes. These can reduce mosaicking performance by causing [...] Read more.
This paper proposes a robust feature-based mosaicking method that can handle images obtained by lightweight unmanned aerial vehicles (UAVs). The imaging geometry of small UAVs can be characterized by unstable flight attitudes and low flight altitudes. These can reduce mosaicking performance by causing insufficient overlaps, tilted images, and biased tiepoint distributions. To solve these problems in the mosaicking process, we introduce the tiepoint area ratio (TAR) as a geometric stability indicator and orthogonality as an image deformation indicator. The proposed method estimates pairwise transformations with optimal transformation models derived by geometric stability analysis between adjacent images. It then estimates global transformations from optimal pairwise transformations that maximize geometric stability between adjacent images and minimize mosaic deformation. The valid criterion for the TAR in selecting an optimal transformation model was found to be about 0.3 from experiments with two independent image datasets. The results of a performance evaluation showed that the problems caused by the imaging geometry characteristics of small UAVs could actually occur in image datasets and showed that the proposed method could reliably produce image mosaics for image datasets obtained in both general and extreme imaging environments. Full article
(This article belongs to the Special Issue Remote Sensing Images Processing for Disasters Response)
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