  Convolutional neural networks (CNN) and Transformers have made impressive
progress in the field of remote sensing change detection (CD). However, both
architectures have inherent shortcomings: CNN are constrained by a limited
receptive field that may hinder their ability to capture broader spatial
contexts, while Transformers are computationally intensive, making them costly
to train and deploy on large datasets. Recently, the Mamba architecture, based
on state space models, has shown remarkable performance in a series of natural
language processing tasks, which can effectively compensate for the
shortcomings of the above two architectures. In this paper, we explore for the
first time the potential of the Mamba architecture for remote sensing CD tasks.
We tailor the corresponding frameworks, called MambaBCD, MambaSCD, and
MambaBDA, for binary change detection (BCD), semantic change detection (SCD),
and building damage assessment (BDA), respectively. All three frameworks adopt
the cutting-edge Visual Mamba architecture as the encoder, which allows full
learning of global spatial contextual information from the input images. For
the change decoder, which is available in all three architectures, we propose
three spatio-temporal relationship modeling mechanisms, which can be naturally
combined with the Mamba architecture and fully utilize its attribute to achieve
spatio-temporal interaction of multi-temporal features, thereby obtaining
accurate change information. On five benchmark datasets, our proposed
frameworks outperform current CNN- and Transformer-based approaches without
using any complex training strategies or tricks, fully demonstrating the
potential of the Mamba architecture in CD tasks. Further experiments show that
our architecture is quite robust to degraded data. The source code will be
available in https://github.com/ChenHongruixuan/MambaCD
