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2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Every year there are numerous cases of individuals becoming lost in remote wilderness environments. Principles of search theory have become a foundation for developing more efficient and successful search and rescue methods. Measurements can be taken that describe how easily a search object is to detect. These estimates allow the calculation of the probability of detectionthe probability that an object would have been detected if in the area. This value only provides information about the search area as a whole; it does not provide details about which portions were searched more thoroughly than others. Ground searchers often carry portable GPS devices and their resulting GPS track logs have recently been used to fill in part of this knowledge gap. We created a system that provides a detection likelihood map that estimates the probability that each point in a search area was seen well enough to detect the search object if it was there. This map will be used to aid ground searchers as they search an assigned area, providing real time feedback of what has been "seen." The maps will also assist incident commanders as they assess previous searches and plan future ones by providing more detail than is available by viewing GPS track logs.
2018
US wilderness search and rescue consumes thousands of person-hours and millions of dollars annually. Timeliness is critical: the probability of success decreases substantially after 24 hours. Although over 90% of searches are quickly resolved by standard “reflex” tasks, the remainder require and reward intensive planning. Planning begins with a probability map showing where the lost person is likely to be found. The MapScore project described here provides a way to evaluate probability maps using actual historical searches. In this work we generated probability maps the Euclidean distance tables in (Koester 2008), and using Doke’s (2012) watershed model. Watershed boundaries follow high terrain and may better reflect actual barriers to travel. We also created a third model using the joint distribution using Euclidean and watershed features. On a metric where random maps score 0 and perfect maps score 1, the Euclidean distance model scored 0.78 (95%CI: 0.74–0.82, on 376 cases). The s...
Computational and Mathematical Organization Theory, 2010
In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely location of the missing person. This map is important because it guides the IC in allocating search resources and coordinating efforts, but it often depends almost exclusively on prior experience and subjective judgment. We propose a Bayesian model that utilizes publicly available terrain features data to help model lost person behaviors. This approach enables domain experts to encode uncertainty in their prior estimations and also make it possible to incorporate human behavior data collected in the form of posterior distributions, which are used to build a first-order Markov transition matrix for generating a temporal, posterior predictive probability distribution map. The map can work as a base to be augmented by search and rescue workers to incorporate additional information. Using a Bayes χ 2 test for goodness-of-fit, we show that the model fits a synthetic dataset well. This model also serves as a foundation of a larger framework that allows for easy expansion to incorporate additional factors such as season and weather conditions that affect the lost person's behaviors.
2020
Search theory allows for correction factors to account for conditions (such as night) that affect the sweep width value. Search theory also predicts that the coverage is proportional to the probability of detection by either the inverse cube curve or exponential function (random search). The objective is to determine the correction factors from night searching and validate the coverage curves. An Effective Sweep Width experiment was conducted with the same medium visibility adult-sized targets during both the day and the night in a temperate forest. In addition, high and low visibility clue-sized objects were placed directly on the trail. We found an effective sweep width of 64 meters during the daytime and 22 meters at night for a correction factor of 0.34 for the adult-sized targets. Both high (100% vs 94%) and low (83% vs 43%) visibility clues were more detectable during the day versus night (P<0.001). Searchers with dim flashlights (<200 lux at one meter) resulted in an ad...
Wilderness & Environmental Medicine, 2014
Objective.-Standard-of-practice search management requires that the probability of detection (POD) be determined for each search resource after a task. To calculate the POD, a detection index (W) is obtained by field experiments. Because of the complexities of the land environment, search planners need a way to estimate the value of W without conducting formal experiments. We demonstrate a robust empirical correlation between detection range (Rd) and W, and argue that Rd may reliably be used as a quick field estimate for W. Methods.-We obtained the average maximum detection range (AMDR), Rd, and W values from 10 detection experiments conducted throughout North America. We measured the correlation between Rd and W, and tested whether the apparent relationship between W and Rd was statistically significant. Results.-On average we found W E 1.645 Â Rd with a strong correlation (R 2 ¼ .827). The highvisibility class had W E 1.773 Â Rd (also R 2 ¼ .867), the medium-visibility class had W E 1.556 Â Rd (R 2 ¼ .560), and the low-visibility had a correction factor of 1.135 (R 2 ¼ .319) for Rd to W. Using analysis of variance and post hoc testing, only the high-and low-visibility classes were significantly different from each other (P o .01). We also found a high correlation between the AMDR and Rd (R 2 ¼ .9974). Conclusions.-Although additional experiments are required for the medium-and low-visibility search objects and in the dry-domain ecoregion, we suggest search planners use the following correction factors to convert field-measured Rd to an estimate of the effective sweep width (W): highvisibility W ¼ 1.8 Â Rd; medium-visibility W ¼ 1.6 Â Rd; and low-visibility W ¼ 1.1 Â Rd.
For the first time in history, a scientifically sound yet practical method for objectively determining detection probabilities for objects of importance to search and rescue (SAR) in the land environment was successfully developed and field-tested. Data was collected using volunteer searchers and analyzed with simplified analysis techniques, all at very low cost. This work opens the door for resolving search planning and evaluation issues that have been vigorously debated within the land SAR community for nearly 30 years but never settled. Searching is by its very nature a probabilistic process. However, a carefully planned search using the right tools and concepts is significantly more likely to succeed and, of equal importance when lives are at stake, succeed sooner. Planning a search consists of evaluating all the available information and then, since it is not generally possible to do a thorough search everywhere all at once, deciding how to best utilize the available, and often...
Coordinating and managing emergency response scenarios in wilderness areas is a difficult task, especially when the search team is distinguished by a wide diversity of sensory-motor and cognitive skills. Moreover, local terrain characteristics and environmental conditions have a strong influence on the performance of the exploration tasks executed by the searchers. To cope with these issues, we present a mission support tool, integrated with geographic information systems (GIS), to assist monitoring and decision-making of mission plans. The proposed framework introduces novel strategies to estimate search efficacy according to agent and environment characteristics, an optimization-based mission planning component which assists the allocation and scheduling of search tasks, and a simulation environment which enables the user to ascertain the outcome of the defined plans.
This paper presents the design of a system that is being developed to utilize wireless local databases for making necessary local information readily available to search and rescue teams following a natural disaster. The Search and Rescue Data Access Point (SR-DAP) system is designed for local data storage and retrieval using sensor nodes deployed on the exterior sides of buildings. The paper reports on the results of the interviews conducted with experts to identify types of information items that are needed during search and rescue operations. Also, memory requirements for identified information groups are given. The implementation details of the developed system are provided and initial findings of data reading/writing tests performed in a laboratory environment are presented. The technical feasibility of using wireless sensors for local data storage and retrieval is discussed in the light of the initial findings.
Proceedings of the 2008 International Conference on System of Systems Engineering, IEEE , 2008
This paper describes Canine Augmentation Technology (CAT) for use in urban search and rescue (USAR). CAT is a WiFi enabled sensor array that is worn by a trained canines deployed in urban disasters. The system includes, but is not limited to, cameras that provide emergency responders with real-time data to remotely monitor, analyze and take action during USAR operations. An analysis is made of the current tools available to USAR workers including rescue robots and canine search teams. From this analysis came the design of CAT--a system that extracts the strengths of each available USAR tool and combines them to compliment each other. Our experiments yield promising results that CAT may provide significant help to rescuers.
When a person falls off of a large ship, it takes several minutes to assemble a rescue team, during which the person may be lost forever. To maximize the likelihood of rescue, a GPS-based rescue system was designed that could be automatically deployed. This system includes a small, battery-powered victim locating unit, designed to be installed on lifejackets; a rescue vehicle, which autonomously navigates to the victim using GPS; and a mothership host system, which provides navigation vectors to steer the rescue vehicle to the victim and back to the mothership successfully.
2009 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC), 2009
Wilderness & Environmental Medicine, 2014
Reports of overdue persons are common for search and rescue personnel. Search incidents for missing persons are conducted following established industry standard practices, which are continuously refined through experience and the analysis of previous search operations. Throughout this process, elements of uncertainty exist, and the knowledge and experience of the searchers and search managers may influence the outcome significantly. A sound knowledge of current search tactics will help search and rescue medical providers function more effectively during search operations. Initial actions during a search incident include 3 primary tasks that must be accomplished on any search: investigation, containment, and then hasty search efforts. Concurrent with these initial actions are the establishment of the search area and a formal US National Incident Management System incident command system. That is essential for an efficient operation and will lay the groundwork for expanding the operation past the initial operational period. The goal of applying these standard search management practices is to allow searchers to maximize their efforts, reduce some of the inherent uncertainty, and most importantly, place searchers in a position to detect the missing person.
Scientific Reports, 2022
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Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction - HRI '12, 2012
In wilderness search and rescue, objects not native or typical to a scene may provide clues that indicate the recent presence of the missing person. This paper presents the results of augmenting an aerial wilderness search-and-rescue system with an automated spectral anomaly detector for identifying unusually colored objects. The detector dynamically builds a model of the natural coloring in the scene and identifies outlier pixels, which are then filtered both spatially and temporally to find unusually colored objects. These objects are then highlighted in the search video as suggestions for the user, thus shifting a portion of the user's task from scanning the video to verifying the suggestions. This paper empirically evaluates multiple potential detectors then incorporates the best-performing detector into a suggestion system. User study results demonstrate that even with an imperfect detector users' detection increased significantly. Results further indicate that users' false positive rates did not increase, though performance in a secondary task did decrease. Furthermore, users subjectively reported that the use of detector-based suggestions made the overall task easier. These results suggest that such suggestion-based systems for search can increase overall searcher performance but that additional external tasks should be limited.
Transactions in GIS, 2015
US wilderness search and rescue consumes thousands of person-hours and millions of dollars annually. Timeliness is critical: the probability of success decreases substantially after 24 hours. Although over 90% of searches are quickly resolved by standard "reflex" tasks, the remainder require and reward intensive planning. Planning begins with a probability map showing where the lost person is likely to be found. The MapScore project described here provides a way to evaluate probability maps using actual historical searches. In this work we generated probability maps the Euclidean distance tables in (Koester 2008), and using Doke's (2012) watershed model. Watershed boundaries follow high terrain and may better reflect actual barriers to travel. We also created a third model using the joint distribution using Euclidean and watershed features. On a metric where random maps score 0 and perfect maps score 1, the Euclidean distance model scored 0.78 (95%CI: 0.74-0.82, on 376 cases). The simple watershed model by itself was clearly inferior at 0.61, but the Combined model was slightly better at 0.81 (95%CI: 0.77-0.84).
In recent years, wireless sensor networks have been used in applications of data gathering and target localization across large geographical areas. In this thesis, we study the issues involved in applying wireless sensor networks to search and rescue of lost hikers in trails and focus on the optimal placement of sensors and access points such that the cost of search and rescue is minimized. Particularly, we address two problems: a) how to identify the lost hiker position as accurately as possible, i.e., obtain a small search region containing the lost hiker; and (b) how to search efficiently in search regions for different trail topologies and search agent capabilities. We study the problem of achieving smaller search regions with different problem attributes. For simpler trail topologies, we propose theoretical models that consider both efficiency and accuracy criteria and present analytical results. For complicated graph topologies, we develop efficient heuristic algorithms with various heuristics. In addition, we analyze the difference of single hiker and multiple hiker scenarios with different hiking dynamics. After access point deployment is decided, the actual cost of search in individual search regions can be computed.
Wireless technologies suitable for Search and Rescue (SaR) operations are becoming crucial for the success of such missions. In avalanche scenarios, the snow depth and the snowpack profile significantly influence the wireless propagation of technologies used to locate victims, such as ARVA (in French: appareil de recherche de victimes d'avalanche) systems. In this work, we explore the potential of LoRa technology under challenging realistic conditions. For the first time, we collect radiopropagation data and the contextual snow profile when the transmitter is buried over a 50 × 50 m area resembling a typical human-triggered avalanche. Specifically, we detail the methodology adopted to collect data through three test types: cross, maximum distance, and drone flyover. The data are annotated with accurate ground truth which allows evaluating localization algorithms based on the RSSI (received signal strength indicator) and SNR (signal-to-noise ratio) of LoRa units. We conducted tests under various environmental conditions, ranging from dry to wet snowpacks. Our results demonstrate the high quality of the LoRa channel, even when the target is buried at a depth of 1 meter in snow with a high liquid water content. At the same time, we quantify the effects of two main degrading factors for the LoRa propagation: the amount of the snow and the liquid water content existing in the snowpack profiles.
2010
Video-equipped mini unmanned aerial vehicles (mini-UAVs) are becoming increasingly popular for surveillance, remote sensing, law enforcement, and search and rescue operations, all of which rely on thorough coverage of a target observation area. However, coverage is not simply a matter of seeing the area (visibility) but of seeing it well enough to allow detection of targets of interest, a quality we here call "see-ability". Video flashlights, mosaics, or other geospatial compositions of the video may help place the video in context and convey that an area was observed, but not necessarily how well or how often. This paper presents a method for using UAV-acquired video georegistered to terrain and aerial reference imagery to create geospatial video coverage quality maps and indices that indicate relative video quality based on detection factors such as image resolution, number of observations, and variety of viewing angles. When used for offline post-analysis of the video, or for online review, these maps also enable geospatial quality-filtered or prioritized nonsequential access to the video. We present examples of static and dynamic see-ability coverage maps in wilderness searchand-rescue scenarios, along with examples of prioritized nonsequential video access. We also present the results of a user study demonstrating the correlation between see-ability computation and human detection performance.
2015
Emergencies and crisis are an inevitable fact of modern life, with extreme weather events, fires, hazmat spills and traffic accidents happening often and in every jurisdiction. The potential consequences are indisputable: serious injury and/or death to the public and to responding personnel, damage to public and private property and the risk of long-term financial repercussions, among others. Under the resulting chaotic and challenging working conditions, Urban Search and Rescue (USaR) crews must make quick decisions under stress to determine the location of trapped victims as quickly and as accurately as possible. The EU FP7 project INACHUS presents a holistic approach in providing a system that aims at achieving significant time reduction related to the USaR phase by advancing wide-area situation awareness solutions for improved detection and localization of trapped victims, assisted by simulation tools for predicting structural failures and a decision support mechanism incorporat...
The Journal of Search and Rescue, 2023
Effective sweep width (W) promises objective probability of detection (POD) values for guiding missingperson search efforts. However, methods for measuring W produce large uncertainties. Also, models for generating POD from W have not been validated for ground-based search. The authors applied leastsquare fits of POD data collected in the field for air-scent dog teams as well as human searchers using the two most prevalent models to derive W values. The method routinely fits the data to an R-square of >.8 with more statistical power than previous methods, and appears to be detector-agnostic. The authors present recommendations for optimizing its use in the field.
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