Drones

UAV aerial imagery for urban low-altitude sensing contains objects that occupy only a few pixels and are densely distributed, so whole-image inference at a limited input resolution represents extremely small objects inadequately. Fixed slicing raises recall but applies uniform parameters to every image, causing redundant computation in low-density regions and misses in high-density regions. We propose Density-Aware Adaptive Slicing Detection (DAASD): a low-threshold whole-image scan builds an object density heatmap, from which the slice size and overlap are selected, using small high-overlap slices for high-density regions and large low-overlap slices for low-density regions; per-slice detections are then mapped back and merged by non-maximum suppression. Using the end-to-end NMS-free YOLO26n on the full VisDrone validation set, DAASD raises the mAP50 from 31.34% to 37.51%, approaching the 37.66% of fixed slicing; on the first 500 test images, used as an additional cross-split reference, the mAP50 rises from 30.81% to 35.21%. It exceeds the 37.66% of fixed slicing. It also exceeds the retrained four-scale YOLO26n + P2 whole-image result of 32.82% by 4.69 points, indicating that preserving the local resolution at inference is more effective than adding a high-resolution feature layer. On the overhead xView dataset, where vehicles are as small as 12 pixels, slicing raises the mAP50 from 0.08% for whole-image inference to 69.19% without retuning the slice policy, confirming cross-dataset generalization. DAASD needs no retraining and has been validated on both the NMS-free YOLO26n and the RT-DETR detector, demonstrating its detector-agnostic nature.

UAV thermal imaging provides high-resolution observations of surface-temperature organization across topographically complex volcanic terrain. This study examines UAV-derived apparent surface temperature across five contrasting volcanic landscapes in the Kula Volcanic Field, western Türkiye. RGB, thermal, and elevation data collected during a two-day UAV campaign were analyzed to compare north-, east-, south-, and west-facing surfaces within common 5-m elevation intervals and along representative south–north swath profiles. Apparent surface temperature showed no consistent relationship with elevation. South-facing surfaces were warmer than north-facing surfaces in 80 of 82 elevation-bin comparisons, while median within-site south–north differences ranged from 5.0 to 30.1 °C. The clearest thermal–topographic organization occurred at Sandal Divlit, where opposing outer and inner slopes formed an alternating warm–cool–warm–cool sequence. More complex patterns occurred at sites characterized by reservoir proximity, fragmented volcanic terrain, agricultural modification, vegetation, and heterogeneous surface materials. Aspect–elevation comparisons provided a consistent basis for interpreting thermal patterns, although aspect did not account for all local variation. UAV thermal imagery can complement RGB imagery and terrain models in volcanic-landform reconnaissance, field targeting, and geoconservation-oriented documentation. Because acquisition timing and solar geometry varied among surveys, the observations represent flight-specific spatial contrasts rather than persistent thermal regimes.

UAVs come in many different platforms such as quadcopters, hexacopters, hybrid models, etc., with diverse attribute types which can be characterised as heterogeneous data. In MCDM, heterogeneous datasets pose several challenges, as most methods are designed for homogeneous datasets. To address these challenges, this paper introduces the MixTOPSIS method by integrating the Gower dissimilarity metric into the TOPSIS framework, enabling the evaluation of alternatives with mixed data types, including numerical, ordinal, and nominal criteria. Therefore, to demonstrate how the method extends TOPSIS for mixed data types, the UAV ranking problem was chosen as the application. According to the simulation results, the top-ranked alternative consistently matched the preferred nominal criteria, while tare and maximum takeoff weight attributes were limiting elements for all options. Sensitivity analyses with gradual and random perturbations of criterion weights were conducted to further examine ranking stability. For the gradual perturbations, the dominant ranking pattern was maintained in 92.78% of the 1441 weight assignments. The analysis with random perturbations also showed substantial ranking stability, with the dominant pattern maintained in 98.02% of cases. The primary originality of MixTOPSIS lies in: (i) evaluating mixed-scale (nominal, ordinal, and numerical) criteria within the TOPSIS distance framework without requiring artificial numerical encoding; (ii) offering a flexible preferred-level mechanism for qualitative attributes; and (iii) introducing criterion-wise contribution metrics that enhance decision explainability. Overall, MixTOPSIS performed effectively, offering a robust, practically useful decision-making tool with interpretable results for complex real-world applications involving diverse data types.