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。

MapChange: 基于深度三元组网络的历史地图不变性增强语义变化检测

MapChange: Enhancing Semantic Change Detection with Temporal-Invariant  Historical Maps Based on Deep Triplet Network

In this paper, we propose Self-Contrastive Decorrelation (SCD), a
self-supervised approach. Given an input sentence, it optimizes a joint
self-contrastive and decorrelation objective. Learning a representation is
facilitated by leveraging the contrast arising from the instantiation of
standard dropout at different rates. The proposed method is conceptually simple
yet empirically powerful. It achieves comparable results with state-of-the-art
methods on multiple benchmarks without using contrastive pairs. This study
opens up avenues for efficient self-supervised learning methods that are more
robust than current contrastive methods.

本文提出了一种自监督学习方法 SCD，其通过对输入句子进行联合自对比和去相关优化来学习表示，并通过利用标准 dropout 的实例化产生的对比来实现。该方法在多个基准测试中取得了与最先进方法相当的结果，而不使用对比对。