Reliable knowledge of power transmission tower locations is fundamental for large-scale inspection and asset management in modern power grids. However, in satellite and aerial remote sensing imagery, towers typically appear as small, slender structures embedded in cluttered backgrounds, which leads to frequent missed and false detections. To address this challenge, we propose SCOPE-YOLO, an integrated super-resolution-plus-detection framework tailored for scalable transmission and distribution tower monitoring. In the first stage, low-resolution image patches are enhanced by a Real-ESRGAN ×4 super-resolution frontend, which restores high-frequency lattice details and sharpens tower boundaries. The reconstructed images are then processed by SCOPE-YOLO, a YOLOv11-based detector that incorporates a Cross-Scale Feature Aggregation (CFA) module, a Gather–Distribute (GD) routing mechanism, and a high-resolution P2 detection head, together with SAT and layered inference strategies to strengthen small-object perception under complex backgrounds. Experiments on the public SRSPTD dataset demonstrate that SCOPE-YOLO improves F1 score by 0.051 and raises mAP@0.5 by 10.2 percentage points over the YOLOv11-s baseline, while maintaining a compact model size. Compared with a broad set of state-of-the-art detectors, SCOPE-YOLO achieves the best overall performance, reaching 82.8% mAP@0.5 for power tower detection. Cross-domain evaluation on the GZ-PTD test set further confirms the effectiveness of the super-resolution–detection pipeline: Real-ESRGAN×4@2048 + SCOPE-YOLO increases Recall from 0.8621 to 0.9278 and mAP@0.5 from 0.8365 to 0.9132 relative to the low-resolution baseline, substantially reducing missed detections of small and weak tower targets in real-world scenes.

Rapid urban population aging and increasing land constraints pose significant challenges for improving both service coverage and spatial equity in elderly care facility planning. This study develops an integrated optimization framework that simultaneously addresses accessibility, efficiency, and equity in the spatial allocation of elderly care facilities. First, an improved Gaussian Two-Step Floating Catchment Area (G2SFCA) method is employed to evaluate accessibility patterns across multiple facility types under both walking and driving scenarios. Second, resource allocation equity is quantified using Lorenz curves and spatial Gini coefficients to identify mismatches between elderly care supply and population demand. Building upon these analyses, a fairness-oriented maximum covering location model—termed the Equity Maximum Covering Location Problem (EMCLP)—is formulated and further transformed into a Markov Decision Process. A deep reinforcement learning-based algorithm is subsequently designed to solve the EMCLP under complex spatial constraints. Experiment results demonstrate that the proposed approach achieves improved computational efficiency while maintaining robust solution quality, and effectively enhances service provision in underserved areas through differentiated functional allocation strategies.