  Context modeling is critical for remote sensing image dense prediction tasks.
Nowadays, the growing size of very-high-resolution (VHR) remote sensing images
poses challenges in effectively modeling context. While transformer-based
models possess global modeling capabilities, they encounter computational
challenges when applied to large VHR images due to their quadratic complexity.
The conventional practice of cropping large images into smaller patches results
in a notable loss of contextual information. To address these issues, we
propose the Remote Sensing Mamba (RSM) for dense prediction tasks in large VHR
remote sensing images. RSM is specifically designed to capture the global
context of remote sensing images with linear complexity, facilitating the
effective processing of large VHR images. Considering that the land covers in
remote sensing images are distributed in arbitrary spatial directions due to
characteristics of remote sensing over-head imaging, the RSM incorporates an
omnidirectional selective scan module to globally model the context of images
in multiple directions, capturing large spatial features from various
directions. Extensive experiments on semantic segmentation and change detection
tasks across various land covers demonstrate the effectiveness of the proposed
RSM. We designed simple yet effective models based on RSM, achieving
state-of-the-art performance on dense prediction tasks in VHR remote sensing
images without fancy training strategies. Leveraging the linear complexity and
global modeling capabilities, RSM achieves better efficiency and accuracy than
transformer-based models on large remote sensing images. Interestingly, we also
demonstrated that our model generally performs better with a larger image size
on dense prediction tasks. Our code is available at
https://github.com/walking-shadow/Official_Remote_Sensing_Mamba.
