DEM

Deep learning detects entire multiple-size lunar craters driven by elevation data and topographic knowledge
Deep learning detects entire multiple-size lunar craters driven by elevation data and topographic knowledge

Lunar craters are important geomorphological features, that provide valuable insights into lunar morphology, geology, and impact processes. However, the current understanding of lunar craters of different sizes, especially smaller craters (diameter <5 km), is still incomplete. The lack of understanding of small lunar craters affects our understanding of the lunar surface and its geological history. Therefore, in this study, we propose a deep learning Crater Detection Algorithms (CDA), called Lunar Topographic Knowledge Attention U-Net (LTKAU-Net) that integrates a Digital Elevation Model (DEM) and topographic knowledge.

Feb 7, 2025

DEM-based sand dune analysis: Identification and morphological regionalization in the Grand Erg Oriental
DEM-based sand dune analysis: Identification and morphological regionalization in the Grand Erg Oriental

Sand dunes are predominantly distributed in arid regions. Automatic mapping and regionalization of sand dunes in large-scale areas are crucial to understanding the evolution trends of aeolian sand environments. Different from existing studies primarily focused on mapping the extent of desert areas, this study proposes a framework for automatic identification and comprehensive regionalization based on dunes morphology.

Nov 28, 2024