Kavli Affiliate: Jie Shan
| First 5 Authors: Xiangxi Tian, Jie Shan, , ,
| Summary:
Generating continuous environmental models from sparsely sampled data is a
critical challenge in spatial modeling, particularly for topography.
Traditional spatial interpolation methods often struggle with handling sparse
measurements. To address this, we propose a Transformer-based Generative Model
for Spatial Interpolation (T-GMSI) using a vision transformer (ViT)
architecture for digital elevation model (DEM) generation under sparse
conditions. T-GMSI replaces traditional convolution-based methods with ViT for
feature extraction and DEM interpolation while incorporating a terrain
feature-aware loss function for enhanced accuracy. T-GMSI excels in producing
high-quality elevation surfaces from datasets with over 70% sparsity and
demonstrates strong transferability across diverse landscapes without
fine-tuning. Its performance is validated through extensive experiments,
outperforming traditional methods such as ordinary Kriging (OK) and natural
neighbor (NN) and a conditional generative adversarial network (CGAN)-based
model (CEDGAN). Compared to OK and NN, T-GMSI reduces root mean square error
(RMSE) by 40% and 25% on airborne lidar data and by 23% and 10% on spaceborne
lidar data. Against CEDGAN, T-GMSI achieves a 20% RMSE improvement on provided
DEM data, requiring no fine-tuning. The ability of model on generalizing to
large, unseen terrains underscores its transferability and potential
applicability beyond topographic modeling. This research establishes T-GMSI as
a state-of-the-art solution for spatial interpolation on sparse datasets and
highlights its broader utility for other sparse data interpolation challenges.
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