  Despite the rapid evolution of semantic segmentation for land cover
classification in high-resolution remote sensing imagery, integrating multiple
data modalities such as Digital Surface Model (DSM), RGB, and Near-infrared
(NIR) remains a challenge. Current methods often process only two types of
data, missing out on the rich information that additional modalities can
provide. Addressing this gap, we propose a novel \textbf{L}ightweight
\textbf{M}ultimodal data \textbf{F}usion \textbf{Net}work (LMFNet) to
accomplish the tasks of fusion and semantic segmentation of multimodal remote
sensing images. LMFNet uniquely accommodates various data types simultaneously,
including RGB, NirRG, and DSM, through a weight-sharing, multi-branch vision
transformer that minimizes parameter count while ensuring robust feature
extraction. Our proposed multimodal fusion module integrates a
\textit{Multimodal Feature Fusion Reconstruction Layer} and \textit{Multimodal
Feature Self-Attention Fusion Layer}, which can reconstruct and fuse multimodal
features. Extensive testing on public datasets such as US3D, ISPRS Potsdam, and
ISPRS Vaihingen demonstrates the effectiveness of LMFNet. Specifically, it
achieves a mean Intersection over Union ($mIoU$) of 85.09\% on the US3D
dataset, marking a significant improvement over existing methods. Compared to
unimodal approaches, LMFNet shows a 10\% enhancement in $mIoU$ with only a 0.5M
increase in parameter count. Furthermore, against bimodal methods, our approach
with trilateral inputs enhances $mIoU$ by 0.46 percentage points.
