# An Evidence Hierarchy for Bayesian Object Classification via OSINT-Aided Heterogeneous Sensor Fusion

arXiv: https://arxiv.org/abs/2605.22259 · HTML: https://mlsanity.com/paper/2605.22259/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 8 of 8)

- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution](https://mlsanity.com/paper/1905.01752/) (2019)
- [MineInsight: A Multi-sensor Dataset for Humanitarian Demining Robotics in Off-Road Environments](https://mlsanity.com/paper/2506.04842/) (2025)
- [Learning Surface Terrain Classifications from Ground Penetrating Radar](https://mlsanity.com/paper/2404.09094/) (2024)
- [Classification of Buried Objects from Ground Penetrating Radar Images by using Second Order Deep Learning Models](https://mlsanity.com/paper/2410.07117/) (2024)
- [Fusion of hyperspectral and ground penetrating radar to estimate soil moisture](https://mlsanity.com/paper/1804.05273/) (2018)
- [SODA-CitrON: Static Object Data Association by Clustering Multi-Modal Sensor Detections Online](https://mlsanity.com/paper/2602.22243/) (2026)

## Abstract (arXiv)

Heterogeneous sensor fusion is vital for detecting, localizing, and classifying CBRNE threats. However, individual sensors are often only capable of detecting a subset of relevant threats with varying reliability or can even provide only indirect threat indications, making threat classification challenging. Furthermore, high clutter rates on the sensor side present a great challenge for fusion systems. Additionally, the limited availability of high quality datasets hinders the advancement of learning-based detection and classification models in smart sensors. To mitigate these sensor related shortcomings, a context-aware and domain knowledge-enhanced fusion process is proposed. First, a novel evidence hierarchy is established that enables modeling of direct, indicative, and contextual information. Second, contextual information about the environment is introduced into the fusion process, by collecting, processing, and exploiting OSINT inputs. Third, all levels of the evidence hierarchy are used to craft a Bayesian threat type classification mechanism with domain knowledge-informed priors. The proposed methodology is evaluated in simulated scenarios, and the results demonstrate the benefit of the proposed fusion approach in terms of robustness to clutter and prior mismatch, with an overall classification accuracy of up to 95%.