Multimodal vision–language modeling has emerged as a promising paradigm for remote sensing (RS) image understanding. However, existing methods are limited by fixedresolution visual processing and single-scale vision–language alignment, making it difficult to simultaneously preserve finegrained details and maintain semantic consistency across different spatial granularities. To address these challenges, we propose RAH-VLA, a Resolution-Adaptive Hierarchical Vision– Language Alignment framework for multimodal remote sensing understanding. Specifically, a Dynamic Resolution Input Strategy (DRIS) is developed to enable resolution-adaptive visual representations, while a Multi-scale Vision–Language Alignment Mechanism (MS-VLAM) is introduced to establish hierarchical semantic correspondence across object-level, region-level, and global-level representations. Extensive experiments on multiple remote sensing benchmarks demonstrate that RAH-VLA consistently improves image captioning, visual grounding, and crossmodal reasoning performance while reducing computational redundancy. Qualitative analyses further illustrate the effectiveness of the proposed resolution-adaptive perception and hierarchical vision–language alignment mechanisms, as well as the crossmodality generalization capability of the proposed framework. Overall, RAH-VLA provides an effective and scalable solution for multimodal remote sensing interpretation, offering a practical pathway toward efficient and semantically robust RS vision– language models.

The research aims to study the efficacy of random forest algorithm modelling student grades in software engineering education and compares the predictions made by the model with actual student grades. The data are collected from 88 students from Sanda University in the course "Software Testing Practice" including both subjective ratings by teachers and team leaders across five dimensions: testing requirements, testing plans, testing cases, defect discovery, and testing reports; and students' actual grades in the course "Software Engineering and Project Management" which are objective assessment criteria. It will be compared between two models, Random Forest versus AdaBoost, to determine which is better predicting the students' grades. The overall model designed in this study with subjective ratings from a single course to predict future grades in related courses can be demonstrated to show different performance using their respective different performance models. It has been shown through correlation studies that the random forest model exhibits a greater correlation with actual scores (0.629) than does the AdaBoost model (0.475). More evaluation with measurements such as MAE, MPE, MAPE, as well as R² confirmed the advantages of the random forest model in terms of accuracy and reliability. The model can be further enhanced and made adaptable to various learning environments to more accurately reflect and inform better decision-making in the future and ongoing software engineering education improvements.