开源低代码工具链，让非专业用户也能高效处理高光谱数据
Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and Classification
- 基于有向无环图构建流程化处理框架，降低代码复杂度
- 支持传统与深度学习模型，兼容实时与静态数据源
- 提供模型序列化功能，便于科研共享与复现
机器学习是分析高维高光谱数据的重要工具，但现有软件要么闭源，要么难以扩展。本文介绍 cuvis.ai，一个开源、低代码的高光谱处理与分类软件生态。该系统用 Python 实现，封装了主流机器学习库，支持经典与深度学习模型在高光谱数据上的训练。代码抽象了操作间的依赖关系，通过有向无环图节点管理从数据采集（包括实时与静态源）到最终分类或属性预测的全流程。用户自定义模型具备便捷的序列化能力，保障可移植性与社区共享。所有代码与数据均公开于 https://github.com/cubert-hyperspectral/cuvis.ai。
原文摘要 · Abstract (English)
Machine learning is an important tool for analyzing high-dimension hyperspectral data; however, existing software solutions are either closed-source or inextensible research products. In this paper, we present cuvis.ai, an open-source and low-code software ecosystem for data acquisition, preprocessing, and model training. The package is written in Python and provides wrappers around common machine learning libraries, allowing both classical and deep learning models to be trained on hyperspectral data. The codebase abstracts processing interconnections and data dependencies between operations to minimize code complexity for users. This software package instantiates nodes in a directed acyclic graph to handle all stages of a machine learning ecosystem, from data acquisition, including live or static data sources, to final class assignment or property prediction. User-created models contain convenient serialization methods to ensure portability and increase sharing within the research community. All code and data are available online: https://github.com/cubert-hyperspectral/cuvis.ai
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