Citation: Nasar, W.; Da Silva Torres,
R.; Gundersen, O.E.; Karlsen, A.T.
The Use of Decision Support in
Search and Rescue: A Systematic
Literature Review. ISPRS Int. J.
Geo-Inf. 2023, 12, 182. https://
doi.org/10.3390/ijgi12050182
Academic Editor: Wolfgang Kainz
Received: 21 January 2023
Revised: 17 April 2023
Accepted: 19 April 2023
Published: 25 April 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
International Journal of
Geo-Information
Review
The Use of Decision Support in Search and Rescue: A
Systematic Literature Review
Wajeeha Nasar
1,
* , Ricardo Da Silva Torres
1,2
, Odd Erik Gundersen
3
and Anniken T. Karlsen
1
1
Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering,
NTNU—Norwegian University of Science and Technology, 8900 Ålesund, Norway
2
Wageningen Data Competence Center and Farm Technology Group, Wageningen University and Research,
6708 PB Wageningen, The Netherlands
3
Department of Computer Science, Faculty of Information Technology and Electrical Engineering,
NTNU—Norwegian University of Science and Technology, 7034 Trondheim, Norway
* Correspondence: wajeeha.nasar@ntnu.no
Abstract:
Whenever natural and human-made disasters strike, the proper response of the concerned
authorities often relies on search and rescue services. Search and rescue services are complex
multidisciplinary processes that involve several degrees of interdependent assignments. To handle
such complexity, decision support systems are used for decision-making and execution of plans within
search and rescue operations. Advances in data management solutions and artificial intelligence
technologies have provided better opportunities to make more efficient and effective decisions
that can lead to improved search and rescue operations. This paper provides findings from a
bibliometric mapping and a systematic literature review performed to: (1) identify existing search
and rescue processes that use decision support systems, data management solutions, and artificial
intelligence technologies; (2) do a comprehensive analysis of existing solutions in terms of their
research contributions to the investigated domain; and (3) investigate the potential for knowledge
transfer between application areas. The main findings of this review are that non-conventional
data management solutions are commonly used in land rescue operations and that geographical
information systems have been integrated with various machine learning approaches for land rescue.
However, there is a gap in the existing research on search and rescue decision support at sea, which
can motivate future studies within this specific application area.
Keywords:
artificial intelligence; data management; decision support; disaster management;
geographical information systems; search and rescue operations; spatial analysis; systematic review
1. Introduction
Natural and human-made disasters, including hurricanes, floods, bushfires, avalanches,
droughts, epidemics or pandemics, and terrorist attacks, have drastic effects on human
beings, societies, economies, and the environment. According to the International Federa-
tion of Red Cross and Red Crescent Societies (IFRC) World Disaster Report 2020, disasters
caused by climate change have surged by 35 percent over the last decade. A total of
400,000 people have died in these calamities, and 1.7 billion people have been affected [1].
According to the United Nations University—The Institute for Environment and Human
Security (UNU-EHS) Interconnected Disasters Report 2020/2021, the world has witnessed
several record-breaking disasters during the year 2020, including the COVID-19 pan-
demic, Texas cold wave, Amazon wildfire, Vietnam heavy storms, and Amphan cyclone on
India–Bangladesh border [
2
]. These global disasters have affected or killed hundreds of
people and caused billions of US dollars in damage. This scenario has fostered an in-
creasing interest in the development of systems designed to support Search and Rescue
(SAR) processes.
ISPRS Int. J. Geo-Inf. 2023, 12, 182. https://doi.org/10.3390/ijgi12050182 https://www.mdpi.com/journal/ijgi
