Drones and Butterflies : A Low-Cost UAV System for Rapid Detection and Identification of Unconventional Minefields

Aerially-deployed plastic landmines in post-conflict nations present unique detection and disposal challenges. Their small size, randomized distribution during deployment, and low-metal content make these mines more difficult to identify using traditional methods of electromagnetic mine detection. Perhaps the most notorious of these mines is the Sovietera PFM-1 “butterfly mine,” widely used during the decade-long Soviet-Afghan conflict between 1979 and 1989. Predominantly used by the Soviet forces to block otherwise inaccessible mountain passages, many PFM-1 minefields remain in place due to the high associated costs of access and demining. While the total number of deployed PFM-1 mines in Afghanistan is poorly documented, PFM-1 landmines make up a considerable percentage of the estimated 10 million landmines remaining in place across Afghanistan. Their detection and disposal presents a unique logistical challenge for largely the same reasons that their deployment was rationalized in inaccessible and sparsely populated areas of the country

Automated UAS Aeromagnetic Surveys to Detect MBRL Unexploded Ordnance

Unguided Multiple Barrel Rocket Launcher (MBRL) systems are limited-accuracy, high-impact artillery systems meant to deliver barrages of explosive warheads across a wide area of attack. High rates of failure of MBRL rockets on impact and their wide area of ballistic dispersion result in a long-term unexploded ordnance (UXO) concern across large areas where these systems have been deployed. We field tested a newly-developed UAV (unmanned aerial vehicle)-based aeromagnetic platform to remotely detect and identify unexploded 122 mm rockets of the widely-used BM-21 MBRL. We developed an algorithm that allows near real-time analysis, mapping, and interpretations of magnetic datasets in the field and, as a result, rapid identification of anomalies associated with both surfaced and buried MBRL items of UXO. We tested a number of sensor configurations and calibrated the system for optimal signal-to-noise data acquisition over varying site types and in varying environmental conditions. The use of automated surveying allowed us to significantly constrain the search area for UXO removal or in-place destruction. The results of our field trials conclusively demonstrated that implementation of this geophysical system significantly reduces labor and time costs associated with technical assessment of UXO-contaminated sites in post-conflict regions

Inspiring the Next Generation of Humanitarian Mine Action Researchers

Humanitarian mine action (HMA) is a critically under-researched field when compared to other hazards fields of similar societal impact. A potential solution to this problem is early exposure to and engagement in the HMA field in undergraduate education. Early undergraduate education emphasizing technical and social aspects of HMA can help protect lives by building a robust pipeline of passionate researchers who will find new solutions to the global explosive ordnance (EO) crisis. Early engagement of the next generation of HMA researchers and policy makers can occur through various classroom experiences, undergraduate research projects, and public outreach events. These include but are not limited to course-based undergraduate research experiences (CUREs); presenting research results at local, national, and international conferences; dissemination in edited and peer-reviewed publications; local community events; and through social media outreach. Early engagement, active guidance, and mentorship of such students by mid-career and experienced HMA scholars and practitioners could dramatically reduce the learning curve associated with entry into the HMA sector and allow for more fruitful long-term collaboration between academic institutions, private industry, and leading nongovernmental organizations (NGOs) operating across different facets of HMA

A Cost-Efficient Method for Detecting Unexploded 122mm 9M22U Rockets Using Remote Sensing

Unexploded ordnances (UXOs) are any subsurface weapon that pose the threat of detonation. UXOs pose one of the greatest humanitarian concerns of today, as they contaminate land in countries across the globe and lead to thousands of deaths each year. Our research focuses specifically on the BM-21 Grad, a Soviet multiple rocket launcher that fires 122mm rockets with a failure rate of over 4%. This means that the rockets often do not detonate immediately as intended, but become UXOs lodged underground. We studied the use of magnetometry, specifically the UMT MFAM MagPike remote sensor to detect these rockets. We processed data collected from Chernihiv, Ukraine to conclude that BM-21 Grad 122mm rockets do give off magnetic fields that are detectable using magnetometry, and distance above ground level plays a key role in data clarity

Comparison Between Thermal and Hyper-spectral Image Analysis: White-tailed Deer Population Monitoring in the Binghamton University Nature Preserve

The rapid overpopulation of White-Tailed Deer (Odocoileus virginianus) has severely harmed the Northeast region of the United States. Affected regions have seen increased environmental degradation due to overbrowsing, increased instances of deer-vehicle collisions, and an uptick in Lyme Disease contraction. The overpopulation of White-Tailed Deer (WTD) is mainly due to anthropogenic causes such as the overhunting and over regulation of the primary predators of WTD. Therefore, fully understanding the severity of the WTD overpopulation is crucial in combating the issue and making informed management decisions. The scope of our study focuses on determining the most effective image types and image processing techniques in regards to analyzing census data on mammalian wildlife populations. We will be conducting a UAV-based drone survey of WTD in the Binghamton University Nature Preserve collecting both thermal and hyperspectral data. We will then recruit approximately 100-150 untrained college students, split them into two groups, and have each group review a different drone flight. Each student will individually estimate the amount of deer in the data set they were provided. By placing the student estimates on a bell curve for each flight, we will be able to identify which image type is most effective for counting deer with an eye untrained in image analysis. The results of this experiment will allow us to create a novel methodology that will help us, as well as other scientists, utilize drone-based surveys to more accurately gather census data on WTD.https://orb.binghamton.edu/research_days_posters_2022/1112/thumbnail.jp

Issue 22.3 Message from the Director

Message from CISR Director, Ken Rutherfor

From the Interim Director

We are living in uncertain times as we face an unprecedented global health crisis. In keeping with the tradition of adaptation and creativity in the mine action community, we have read numerous examples of national clearance programs and implementing partner organizations adjusting operations in order to continue survey, clearance, and risk education work in line with required COVID-19 restrictions. As some places begin to resume a greater range of activity, we recognize the threat presented by this new virus remains pronounced, and we wish all of you safety in your daily lives and good health

Using Receiver Functions to Study Flat Slab Subduction Zones in Central Chile

Subduction zones are a common geologic feature around the world. They are regions where dense oceanic crust descends into the mantle beneath less dense continental crust. Subduction zone margins are characterized by their presence of earthquakes and volcanoes. The longest subduction margin is along western South America where subduction of the Nazca plate is responsible for formation of the Andes. Along this margin there are distinct segments that are described by the changes in geometry of the down-going Nazca plate. The Chilean-Pampean segment is distinguished by a region of flat slab subduction geometry that corresponds to an absence of seismicity and volcanic dormancy.This study uses receiver functions, a passive source seismology tool that helps visualize vertical and lateral velocity changes of large structure at depth, to image the flat slab region. This study is focused on analyzing the lateral velocity changes above the flat slab in order to determine effects of slab geometry on fluid metasomatism in the mantle, indicated by anisotropy. Anisotropy in this region is proposed to originate from serpentinization of peridotite, caused by fluids escaping from the down-going Nazca plate. Constraining anisotropy in this region will provide implications for both seismicity and volcanism on the surface.https://orb.binghamton.edu/research_days_posters_spring2020/1023/thumbnail.jp

How to Implement Drones and Machine Learning to Reduce Time, Costs, and Dangers Associated with Landmine Detection

Two rapidly emerging technologies revolutionizing scientific problem solving are unpiloted aerial systems (UAS), commonly referred to as drones, and deep learning algorithms.1 Our study combines these two technologies to provide a powerful auxiliary tool for scatterable landmine detection. These munitions are traditionally challenging for clearance operations due to their wide area of impact upon deployment, small size, and random minefield orientation. Our past work focused on developing a reliable UAS capable of detecting and identifying individual elements of PFM-1 minefields to rapidly assess wide areas for landmine contamination, minefield orientation, and possible minefield overlap. In our most recent proof-of-concept study we designed and deployed a machine learning workflow involving a region-based convolutional neural network (R-CNN) to automate the detection and classification process, achieving a 71.5% rate of successful detection.2 In subsequent trials, we expanded our dataset and improved the accuracy of the CNN to detect PFM-1 anti-personnel mines from visual (RGB) UAS-based imagery to 91.8%. In this paper, we intend to familiarize the demining community with the strengths and limitations of UAS and machine learning and suggest a fit of this technology as a key auxiliary first look area reduction technique in humanitarian demining operations. As part of this effort, we seek to provide detailed guidance on how to implement this technique for non-technical survey (NTS) support and area reduction of confirmed and suspected hazardous areas with minimal resources and funding