Uav-Based Detection of Unknown Radioactive Biomass Deposits in Chernobyl’s Exclusion Zone

Abstract

Shortly after the explosion of the Chernobyl nuclear power plant (ChNPP) in 1986, radioactive fall-out and contaminated trees (socalled Red Forest) were buried in the Chernobyl Exclusion Zone (ChEZ). These days, exact locations of the buried contaminated material are needed. Moreover, 3D vegetation maps are necessary to simulate the impact of tornados and forest fire. After 30 years, some of the so-called trenches and clamps are visible. However, some of them are overgrown and have slightly settled in the centimeter and decimeter range. This paper presents a pipeline that comprises 3D vegetation mapping and machine learning methods to precisely map trenches and clamps from remote sensing data. The dataset for our experiments consists of UAV-based LiDAR data, multi-spectral data, and aerial gamma-spectrometry data. Depending on the study areas overall accuracies ranging from 95.6 % to 99.0 % were reached for the classification of radioactive deposits. Our first results demonstrate an accurate and reliable UAV-based detection of unknown radioactive biomass deposits in the ChEZ.

UAV‐assisted real‐time evidence detection in outdoor crime scene investigations

“…Specifically, the drone utilization today is spreading across a wide range of military [4][5][6][7] and security applications [1,2,5,8,9], but also in search and rescue [10,11] and traffic monitoring [10,12,13] operations. Furthermore, the contribution of UAVs is significant in sectors of mapping and land administration [14,15], real estate [4,8], insurance [1,8], construction and infrastructure [8,10], hazardous inspections and detections [8,16,17], agriculture [4,8,10,18], telecommunications [10], media and entertainment [1,11], e-commerce and delivery [19][20][21], ecology and environmental conservation [11,[22][23][24][25][26][27][28], meteorology [4,[29][30][31], and academic research…”

Ionizing Radiation Monitoring Technology at the Verge of Internet of Things

“…This requires the development of autonomous path finding that avoids obstacles from buildings and terrains. The authors of [124] used a UAV-based survey system to detect unknown radioactive biomass deposits in Chernobyl's exclusion zone. UAV-based LiDar data, multi-spectral, and gamma spectrometry data, along with the machine learning methods, were used to precisely map trenches and clamps.…”

Classification of Tree Species and Standing Dead Trees by Fusing Uav-Based Lidar Data and Multispectral Imagery in the 3d Deep Neural Network Pointnet++

“…Because the overflown study area is inaccessible, no ground control points could be used. Therefore, photogrammetric point clouds were registered to georeferenced lidar point clouds using an iterative closest point algorithm 2 , resulting in a root mean squared error of 0.237 m (Briechle et al, 2018).…”

“…3.1.2 Classifier training For the baseline method, the dataset comprised 32 GEOM features and 14 EC features (see Table 1), as well as the five most important MS features generated from the VI orthomosaics. In a preprocessing step, highly correlated redundant features were eliminated from the feature set, here based on the application of a threshold (0.9) to feature-tofeature cross-correlation (Briechle et al, 2018). Next, an RF classifier was trained, including recursive feature elimination (RFE) based on Kuhn (2008) and a feature relevance assessment.…”