Semantic Change Detection (SCD) is recognized as both a crucial and
challenging task in the field of image analysis. Traditional methods for SCD
have predominantly relied on the comparison of image pairs. However, this
approach is significantly hindered by substantial imaging differences, which
arise due to variations in shooting times, atmospheric conditions, and angles.
Such discrepancies lead to two primary issues: the under-detection of minor yet
significant changes, and the generation of false alarms due to temporal
variances. These factors often result in unchanged objects appearing markedly
different in multi-temporal images. In response to these challenges, the
MapChange framework has been developed. This framework introduces a novel
paradigm that synergizes temporal-invariant historical map data with
contemporary high-resolution images. By employing this combination, the
temporal variance inherent in conventional image pair comparisons is
effectively mitigated. The efficacy of the MapChange framework has been
empirically validated through comprehensive testing on two public datasets.
These tests have demonstrated the framework's marked superiority over existing
state-of-the-art SCD methods.

Semantic Change Detection（SCD）是图像分析领域中被认为是至关重要且具有挑战性的任务。传统 SCD 方法主要依赖于图像对比，但这种方法受到拍摄时间、大气条件和角度变化等的显著影响。为了解决这些问题，开发了 MapChange 框架，它将历史地图数据与高分辨率图像相结合，有效地减轻了传统图像对比中的时间变化影响。经过全面测试，该框架在两个公共数据集上表现出显著优势，验证了其有效性。