  Geometric information in the normalized digital surface models (nDSM) is
highly correlated with the semantic class of the land cover. Exploiting two
modalities (RGB and nDSM (height)) jointly has great potential to improve the
segmentation performance. However, it is still an under-explored field in
remote sensing due to the following challenges. First, the scales of existing
datasets are relatively small and the diversity of existing datasets is
limited, which restricts the ability of validation. Second, there is a lack of
unified benchmarks for performance assessment, which leads to difficulties in
comparing the effectiveness of different models. Last, sophisticated
multi-modal semantic segmentation methods have not been deeply explored for
remote sensing data. To cope with these challenges, in this paper, we introduce
a new remote-sensing benchmark dataset for multi-modal semantic segmentation
based on RGB-Height (RGB-H) data. Towards a fair and comprehensive analysis of
existing methods, the proposed benchmark consists of 1) a large-scale dataset
including co-registered RGB and nDSM pairs and pixel-wise semantic labels; 2) a
comprehensive evaluation and analysis of existing multi-modal fusion strategies
for both convolutional and Transformer-based networks on remote sensing data.
Furthermore, we propose a novel and effective Transformer-based intermediary
multi-modal fusion (TIMF) module to improve the semantic segmentation
performance through adaptive token-level multi-modal fusion.The designed
benchmark can foster future research on developing new methods for multi-modal
learning on remote sensing data. Extensive analyses of those methods are
conducted and valuable insights are provided through the experimental results.
Code for the benchmark and baselines can be accessed at
\url{https://github.com/EarthNets/RSI-MMSegmentation}.
