Geomatics

The detection and classification of scatterable landmines present a significant challenge for humanitarian demining, particularly in resource-constrained regions. This paper evaluates the use of a deep learning-based strategy using RGB imagery and the YOLOv11 algorithm to detect the most commonly deployed PFM-1 landmines, with the overarching goal of applying this approach to the broad category of scatterable landmines. RGB image-based YOLOv11 detection showed strong precision (78–91%) and recall (76–88%) against validation data for several model variants. Additionally, 3D-printed, paint-matched replicas of PFM-1 landmines were used provisionally as part of out-of-sample (OOS) testing to assess the realistic value of this methodology in the field, along with an inert PFM-1 mine. This demonstrated the potential for 3D-printed replicas to be used as part of the training and assessment process due to their low-cost, scalable, and safe approach, highlighting strong precision (74–80%) but weaker recall (14–24%). Additional edge deployment was tested using the model to demonstrate its capability in locating a minefield using trigonometric relationships and kernel density relationships, further supporting this method in non-technical, first-pass landmine sweeps. These results demonstrate that OOS evaluation is critical in humanitarian demining research to ensure that detection systems are truly field-ready and operationally reliable. This study provides a replicable workflow for deep learning tasks related to surface-laid landmines that can be deployed on edge devices for use in non-technical surveys.

Synthetic aperture radar (SAR) data from Sentinel-1 enable land cover classification independent of cloud cover and illumination; however, classification performance is affected by inherent speckle noise. This study evaluates the influence of eight speckle filtering algorithms on classification accuracy using Sentinel-1 Ground Range Detected (GRD) data across five contrasting terrain types in eastern Slovakia (mountain, forest, urban, cropland, and water). Speckle suppression was assessed using Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Equivalent Number of Looks (ENL). Classification performance was quantified using Support Vector Machine (SVM), Random Forest (RF), and Histogram-based Gradient Boosting (HistGB) under VV, VH, and dual-polarization (VV + VH) configurations with repeated balanced sampling. Classification accuracy varies across terrain types. In croplands, Lee Sigma combined with SVM in VV + VH mode achieved Overall Accuracy (OA) = 0.746 ± 0.010, whereas in mountainous areas, OA = 0.838 ± 0.005 was achieved with Intensity-Driven Adaptive Neighborhood (IDAN) filtering. Urban areas achieved OA = 0.890 ± 0.006, whereas forest classification remained limited (best OA = 0.582 ± 0.011). Water surfaces approached saturation accuracy (OA ≈ 0.9998). Dual polarization improved performance in heterogeneous environments but had a limited effect in homogeneous classes. The results show that terrain structure influences the interaction between speckle filtering and classification performance.

Technological advancements have revolutionized photogrammetry, with the implementation of unmanned aerial vehicles for capturing images from different angles and the ease of obtaining sensor position information at the time of capture. This study evaluates the accuracy of direct georeferencing via Networked Transport of Radio Technical Commission for Maritime Services Via Internet Protocol, in the orthomosaic as a photogrammetric product in a large urban area with steep and highly variable topography, comparing it with the coordinates of nine checkpoints obtained with GNSS equipment connected to the National Active Geodetic Network, managed by the National Institute of Statistics and Geography of Mexico. An orthomosaic of the historic center of Zacatecas was obtained with a resolution of 2.70 cm/pixel. The orthomosaic coordinates, compared to those of the GNSS equipment, show a root mean square error (RMSE) of 0.78 m in the horizontal coordinates and an RMSE of 1.22 m in the vertical coordinates. Previous studies prove the efficiency of the Continuously Operating Reference Station module and network with other aircraft; this study determines that this is true for large areas with high coverage and quality in the internet network, but with rugged topography, the results are not accurate.