Abstract

The efficient management of coal resources is becoming increasingly important in Poland and Europe, especially during the ongoing energy transition. This paper presents an automated methodology for on-demand inventory of coal dumps and heaps, addressing the limitations of traditional manual surveying techniques. The proposed system integrates Unmanned Aerial Vehicles (UAVs) for rapid spatial data acquisition, IoT sensors for real-time monitoring of environmental parameters, and the QGIS open-source Geographic Information System for data processing and volume calculation. The methodology details the process of generating accurate Digital Elevation Models (DEMs) from UAV imagery using OpenDroneMap software and subsequent volume calculation using a custom algorithm developed within the QGIS Model Designer. This algorithm addresses the challenges of data processing, including the removal of “bad pixels” and geometric correction, to ensure accurate volume estimations. The results demonstrate the potential of the integrated system to provide accurate and timely inventory data, crucial for optimizing coal demand forecasting, production planning, and distribution logistics. This approach offers significant advantages over traditional methods by enhancing safety, reducing labor intensity, and enabling more frequent and precise measurements. The developed solution aligns with the goals of the Polish “Dynamic management of coal demand, production, resource management, and distribution logistics in an economy implementing a decarbonized energy mix” (DynGOSP) project, supporting the modernization of the Polish coal mining sector.

Details

Title
Automated on-demand inventory of coal dumps and heaps using UAV imagery and IoT-driven data processing
Author
Orzeł, Bartosz; Tokarczyk, Jarosław; Michalak, Dariusz; Szewerda, Kamil
Pages
117-143
Publication year
2025
Publication date
2025
Publisher
Polish Academy of Sciences
ISSN
08600953
e-ISSN
22992324
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3263424586
Copyright
© 2025. This work is licensed under https://creativecommons.org/licenses/by-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.