Searching Lost People With Uavs: The System and Results of the Close-Search Project

Molina, Pere; Colomina, Ismael; Vitoria, Teo; Silva, P. F.; Skaloud, Jan; Kornus, Wolfgang; Prades, R.; Aguilera, Carmen

doi:10.5194/isprsarchives-xxxix-b1-441-2012

Cited by 45 publications

(33 citation statements)

References 3 publications

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“…Collected data are mostly images but can also be gas concentrations or radioactivity levels as demonstrated by the tragic event in Fukushima (Sanada and Torii, 2015;Martin et al, 2016). Focusing on image collection, they can be used for early impact assessment, to inspect collapsed buildings and to evaluate structural damages on common infrastructures (Chou et al, 2010;Molina et al 2012;Murphy et al, 2008;Pratt et al, 2009) or cultural heritage sites (Pollefeys et al, 2001;Manferdini et al, 2012;Koutsoudisa et al, 2014;Lazzari et al, 2017). Environmental and geological monitoring can profit from fast multi-temporal acquisitions delivering high-resolution images (Thamm and Judex, 2006;Niethammer et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Giordan

Hayakawa

Nex

et al. 2018

Nat. Hazards Earth Syst. Sci.

140

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Abstract. The number of scientific studies that consider possible applications of remotely piloted aircraft systems (RPASs) for the management of natural hazards effects and the identification of occurred damages strongly increased in the last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of the literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPASs can acquire on-demand ultra-high-resolution images that can be used for the identification of active processes such as landslides or volcanic activities but can also define the effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazards. IntroductionIn the last three decades, the number of natural disasters showed a positive trend with an increase in the number of affected populations. Disasters not only affected the poor and characteristically more vulnerable countries but also those thought to be better protected. The Annual Disaster Statistical Review describes recent impacts of natural disasters on the population and reports 342 naturally triggered disasters in 2016 (Guha-Sapir et al., 2017). This is less than the annual average disaster frequency observed from 2006 to 2015 (376.4 events). However, natural disasters are still responsible for a high number of casualties (8733 death). In the period 2006-2015, the average number of causalities caused annually by natural disasters is 69 827. In 2016, hydrological disasters (177) had the largest share in natural disaster occurrence (51.8 %), followed by meteorological disasters (96; 28.1 %), climatological disasters (38; 11.1 %) and geophysical disasters (31; 9.1 %) (Guha-Sapir et al., 2017). To face these disasters, one of the most important solutions is the use of systems able to provide an adequate level of information for correctly understanding these events and their evolution. In this context, surveying and monitoring natural hazards gained importance. In particular, during the emergency phase it is very important to evaluate and control the phenomenon of evolution, preferably operating in near real time or real time, and consequently, use this information for a better risk assessment scenario. The available acquired data must be processed rapidly to support the emergency services and decision makers.Recently, the use of remote sensing (satellite and airborne platform) in the field of natural hazards and disasters has become common, also supported by the increase in geospatial technologies and the ability to provide and process up-to-date imagery (Joyce et al., 2009;Tarolli, 2014). Remotely sensed data play an integral role in predicting hazard events such as floods and landslides, subsidence events and other ground instabilities. Because of their acquisition mode and capabilPublished by Copern...

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Section: Introductionmentioning

confidence: 99%

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Giordan

Hayakawa

Nex

et al. 2018

Nat. Hazards Earth Syst. Sci.

140

View full text Add to dashboard Cite

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“…These systems are commonly used for high-resolution surveys [29][30][31][32][33][34][35][36][37][38] as they allow flights to be performed in an automatic way [39,40]. The miniaturization of imaging and positioning sensors also reduces the payload and thus enables flights up to about one hour long [41].…”

Section: Introductionmentioning

confidence: 99%

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

et al. 2018

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Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20-0.30 m, but decreases to 0.06-0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.

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“…There are numerous examples of how UAVs can be used to assist SAR missions. They include: use of airborne video and thermal cameras to identify human shapes, use of low quality aerial imagery in the human identity recognition, person's tracking, processing UAV‐acquired images being combinations of a few channels of spectrum, application of synthetic aperture radar for target detection, transportation of first need goods to victims …”

Section: Introductionmentioning

confidence: 99%

“…There are numerous examples of how UAVs can be used to assist SAR missions. They include: use of airborne video and thermal cameras to identify human shapes, [3][4][5] use of low quality aerial imagery in the human identity recognition, 6 person's tracking, 7 processing UAV-acquired images being combinations of a few channels of spectrum, [8][9][10] application of synthetic aperture radar for target detection, 11,12 transportation of first need goods to victims. 3,13 Remotely piloted aerial systems, which are nowadays commonly used by geoscientists, become powerful tools for supporting medical activities in a sense that they may reduce search time.…”

Section: Introductionmentioning

confidence: 99%

The nested k‐means method: A new approach for detecting lost persons in aerial images acquired by unmanned aerial vehicles

Niedzielski

Jurecka

Stec

et al. 2017

Journal of Field Robotics

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A new method, named as the nested k‐means, for detecting a person captured in aerial images acquired by an unmanned aerial vehicle (UAV), is presented. The nested k‐means method is used in a newly built system that supports search and rescue (SAR) activities through processing of aerial photographs taken in visible light spectra (red‐green‐blue channels, RGB). First, the k‐means classification is utilized to identify clusters of colors in a three‐dimensional space (RGB). Second, the k‐means method is used to verify if the automatically selected class of colors is concurrently spatially clustered in a two‐dimensional space (easting‐northing, EN), and has human‐size area. The UAV images were acquired during the field campaign carried out in the Izerskie Mountains (SW Poland). The experiment aimed to observe several persons using an RGB camera, in spring and winter, during various periods of day, in uncovered terrain and sparse forest. It was found that the nested k‐means method has a considerable potential for detecting a person lost in the wilderness and allows to reduce area to be searched to 4.4 and 7.3% in spring and winter, respectively. In winter, land cover influences the performance of the nested k‐means method, with better skills in sparse forest than in the uncovered terrain. In spring, such a relationship does not hold. The nested k‐means method may provide the SAR teams with a tool for near real‐time detection of a person and, as a consequence, to reduce search area to approximately 0.5–7.3% of total terrain to be visited, depending on season and land cover.

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Searching Lost People With Uavs: The System and Results of the Close-Search Project

Cited by 45 publications

References 3 publications

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

The nested k‐means method: A new approach for detecting lost persons in aerial images acquired by unmanned aerial vehicles

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