MAPSRNet offers a practical solution for scenarios where HR imagery is limited or unavailable, highlighting its potential for large-scale remote sensing applications and demonstrating that perceptual and structural fidelity, rather than pixel-level similarity, can drive superior performance in urban land cover segmenta...
Satellite imagery often suffers from limited spatial resolution and, in many cases, high acquisition costs. These factors restrict their use in applications such as urban monitoring, land management, and wildlife studies. This work proposes an AI-based super-resolution approach that leverages high resolution aerial ima...
The experiments demonstrate that SR pre-processing improves quantitative fidelity and downstream task performance, enabling low-resolution satellites to compete more effectively with commercial imagery.
High-resolution aerial imagery plays a significant role in many fields such as urban planning, environmental monitoring, weather prediction, disaster management, change detection and map generation. However, acquiring high-resolution data is often limited by sensor capacities and cost constraints. Moreover, in aerial r...
Results highlight the effectiveness of SOD-MSC for dense small object detection in complex traffic environments, with promising applications in congestion monitoring, vehicle counting, and related ITS tasks.
Road segmentation from satellite imagery is critical for urban planning and transportation analysis, but is often limited by the low spatial resolution of publicly available data and the high cost of high-resolution alternatives. This study evaluates the impact of super-resolution (SR) on urban road network extraction...
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