  With the advent of NASA's lunar reconnaissance orbiter (LRO), a large amount
of high-resolution digital elevation maps (DEMs) have been constructed by using
narrow-angle cameras (NACs) to characterize the Moon's surface. However, NAC
DEMs commonly contain no-data gaps (voids), which makes the map less reliable.
To resolve the issue, this paper provides a deep-learning-based framework for
the probabilistic reconstruction of no-data gaps in NAC DEMs. The framework is
built upon a state of the art stochastic process model, attentive neural
processes (ANP), and predicts the conditional distribution of elevation on the
target coordinates (latitude and longitude) conditioned on the observed
elevation data in nearby regions. Furthermore, this paper proposes sparse
attentive neural processes (SANPs) that not only reduces the linear
computational complexity of the ANP O(N) to the constant complexity O(K) but
enhance the reconstruction performance by preventing overfitting and
over-smoothing problems. The proposed method is evaluated on the Apollo 17
landing site (20.0{\deg}N and 30.4{\deg}E), demonstrating that the suggested
approach successfully reconstructs no-data gaps with uncertainty analysis while
preserving the high resolution of original NAC DEMs.
