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Topology-based terrain segmentation using Apache Spark
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AI
DOI：10.1080/13658816.2025.2577249.png)
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Abstract
En 中文
Terrain topology plays an important role in simulations and segmentation. A widely used terrain representation is the Triangulated Irregular Network (TIN). However, topological analysis on TINs is challenging due to high time and memory requirements, which limit the size of the terrain that can be analyzed. We address this problem by proposing a novel framework for efficient and scalable analysis of large TINs based onMorse theoryusing Apache Spark. The proposed framework, namedMorse–Spark, is based on a data structure for encoding the minimal information of a triangle mesh. Morse–Spark provides optimized methods for the local extraction of many connectivity relations, beginning with the global retrieval of the Vertex–Triangle relation. These relations serve as the foundation for computing terrain morphology through integrated, scalable algorithms. To evaluate the effectiveness and scalability of such a framework, we compare Morse–Spark against a vanilla Spark implementation, aMPI-supported Topology Toolkit (MPI-TTK)implementation, and three well-established software libraries for the topological analysis of TINs. Our experimental evaluation with real-world TINs shows that Morse–Spark can effectively handle datasets around 13 times larger than those processed by state-of-the-art tools for distributed computing (e.g. MPI-TTK).
Keywords:
Terrain analysis
topological method
data structure
Apache Spark
Journal
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5.1
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2.7K
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