MapChange: Enhancing Semantic Change Detection with Temporal-Invariant
  Historical Maps Based on Deep Triplet Network
Yinhe Liu Sunan Shi Zhuo Zheng Jue Wang Shiqi Tian Yanfei Zhong
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.