Evidence map›Paper›PMID 37631665›Full record

ArticleSensors (Basel, Switzerland)2023

Utilizing Drone-Based Ground-Penetrating Radar for Crime Investigations in Localizing and Identifying Clandestine Graves.

Louise Lijcklama À Nijeholt, Tasha Yara Kronshorst, Kees van Teeffelen, Benjamin van Manen, Roeland Emaus, Jaap Knotter, Abeje Mersha

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.6field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Louise Lijcklama À NijeholtTechnologies for Criminal Investigations, Saxion University of Applied Sciences, M.H. Tromplaan 28, 7513 AB Enschede, The Netherlands.
Tasha Yara KronshorstTechnologies for Criminal Investigations, Saxion University of Applied Sciences, M.H. Tromplaan 28, 7513 AB Enschede, The Netherlands.
Kees van TeeffelenUnmanned Robotic Systems, Saxion University of Applied Sciences, Ariënsplein 1, 7511 JX Enschede, The Netherlands.
Benjamin van ManenUnmanned Robotic Systems, Saxion University of Applied Sciences, Ariënsplein 1, 7511 JX Enschede, The Netherlands.ORCID 0000-0001-5465-3064
Roeland EmausBusiness, Building & Technology, Saxion University of Applied Sciences, M.H. Tromplaan 28, 7513 AB Enschede, The Netherlands.ORCID 0009-0005-6978-4135
Jaap KnotterTechnologies for Criminal Investigations, Saxion University of Applied Sciences, M.H. Tromplaan 28, 7513 AB Enschede, The Netherlands.
Abeje MershaUnmanned Robotic Systems, Saxion University of Applied Sciences, Ariënsplein 1, 7511 JX Enschede, The Netherlands.
Saxion · NL

Funding

RAAK Publiek - Nationaal Regieorgaan Praktijkgericht Onderzoek SIA RAAK.PUB07.034TechforFuture TFFnr. 1811
6 · The paper itself

Abstract

The decomposition of a body is influenced by burial conditions, making it crucial to understand the impact of different conditions for accurate grave detection. Geophysical techniques using drones have gained popularity in locating clandestine graves, offering non-invasive methods for detecting surface and subsurface irregularities. Ground-penetrating radar (GPR) is an effective technology for identifying potential grave locations without disturbance. This research aimed to prototype a drone system integrating GPR to assist in grave localization and to develop software for data management. Initial experiments compared GPR with other technologies, demonstrating its valuable applicability. It is suitable for various decomposition stages and soil types, although certain soil compositions have limitations. The research used the DJI M600 Pro drone and a drone-based GPR system enhanced by the real-time kinematic (RTK) global positioning system (GPS) for precision and autonomy. Tests with simulated graves and cadavers validated the system's performance, evaluating optimal altitude, speed, and obstacle avoidance techniques. Furthermore, global and local planning algorithms ensured efficient and obstacle-free flight paths. The results highlighted the potential of the drone-based GPR system in locating clandestine graves while minimizing disturbance, contributing to the development of effective tools for forensic investigations and crime scene analysis.

Indexed as

RadarUnmanned Aerial DevicesAlgorithmsCrimeSoilSoilclandestine gravesdrone-based sensorsground-penetrating radarobstacle avoidanceradargramRTK-GPS

Identifiers

PMID37631665
PMCPMC10459563
OpenAlexW4385759425

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.