The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. Deep learning is receiving significant research attention for pattern recognition and machine learning. Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. The proposed approach can identify six common rock types with an overall classification accuracy of 97.96%, thus outperforming other established deep-learning models and a linear model. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field.

Selecting training samples is crucial in remote sensing image classification. In this paper, we selected three images—Sentinel-2, GF-1, and Landsat 8—and employed three methods for selecting training samples: grouping selection, entropy-based selection, and direct selection. We then used the selected training samples to train three supervised classification models—random forest (RF), support-vector machine (SVM), and k-nearest neighbor (KNN)—and evaluated the classification results of the three images. According to the experimental results, the three classification models performed similarly. Compared with the entropy-based method, the grouping selection method achieved higher classification accuracy using fewer samples. In addition, the grouping selection method outperformed the direct selection method with the same number of samples. Therefore, the grouping selection method performed the best. When using the grouping selection method, the image classification accuracy increased with the increase in the number of samples within a certain sample size range.

The analysis of rockfall events provides critical insights for deciphering planetary geological processes and reconstructing environmental evolutionary timelines. Conventional visual interpretation methods that rely on orbiter imagery can be inefficient due to their massive datasets and subtle morphological signatures. While deep learning technologies, particularly object detection models, demonstrate transformative potential, they require specific adaptation to planetary imaging constraints, including low contrast, grayscale inputs, and small-target detection. Our coordinated optimization strategy integrates dynamic cropping optimization with architectural innovations: Kolmogorov–Arnold Network based C3 module (KANC3) replaces RepC3 through Legendre polynomial decomposition to strengthen feature representation, while our dynamic cropping strategy significantly improves small-target detection in low-contrast grayscale imagery by mitigating background and target imbalance. Experimental validation on the optimized RMaM-2020 dataset demonstrates that Real-Time Detection Transformer with a ResNet-18 backbone and Kolmogorov–Arnold Network based C3 module (RT-DETR-R18-KANC3) achieves 0.982 precision, 0.955 recall, and 0.964 mAP50 under low-contrast conditions, representing a 1% improvement over the baseline model and exceeding YOLO-series models by >40% in relative performance metrics.

Small-object details in high-resolution unmanned aerial vehicle (UAV) imagery are weakened when wide-area scenes are resized for detector input. Increasing input resolution or exhaustively processing local regions can recover spatial detail, but allocate computation without distinguishing regional value after global detection. This paper proposes MRU-YOLO, a selective local re-observation framework guided by marginal re-observation utility (MRU), which measures the expected detection benefit of reprocessing a candidate region after one global forward pass. MRU-YOLO constructs prediction-conditioned states for nine candidate regions from global detections and region geometry. A learned utility regressor ranks the candidates and selects the two highest-ranked regions for local inference. Source-aware fusion integrates complementary global and local predictions while resolving cross-source conflicts. The pipeline requires no modification to the detector backbone, neck, or detection head. Across three independent runs, MRU-YOLO reached mean mAP50–95 values of 41.82% on SeaDronesSee ODv2 and 21.72% on VisDrone2019-DET, improving YOLO11n-640 by 2.32 and 3.28 percentage points, respectively. Class-wise AP50–95 improved in four of five maritime categories and nine of ten urban categories, while the remaining urban category was effectively unchanged. Learned selection also achieved higher utility capture and normalized discounted cumulative gain at rank 2 (NDCG@2) than predicted density on both datasets. Under batch-1 FP16 inference on an NVIDIA GeForce RTX 3090, the end-to-end pipeline achieved 30.86 FPS on SeaDronesSee ODv2 and 33.24 FPS on VisDrone2019-DET. MRU-YOLO concentrates local inference on regions with the highest expected detection contribution under a fixed local-processing budget.