Generative Simulation Benchmarking for planetary geology survey missions for extreme data sparsity scenarios

说明与用途

生成式仿真为极度数据稀缺的行星地质模型提供了可扩展的基准评估方法。

问题

Evaluating machine learning models for planetary geology with extremely sparse, biased, and geographically narrow data leads to over‑optimistic benchmarks that fail in new terrains.

结果

Using generative benchmarks provides unlimited labeled data from distributions unseen by the model, enabling a realistic assessment of generalization in extreme data sparsity scenarios.

工作流

• Build a generative terrain model conditioned on geological parameters
• Generate synthetic scenes with known ground truth
• Sample a stratified grid of geological conditions
• Evaluate the perception model across the grid and report robustness profiles

证据

The benchmark had leaked information. My validation set shared the same simulated geological province as my training set, and my model had simply memorized the distribution of that one region.

来源

Because the generator knows the ground truth by construction, you get unlimited labeled data from distributions your model has never seen.

来源