用参考点距离编码地理矢量数据，提升机器学习效果。
Multi-Point Proximity Encoding For Vector-Mode Geospatial Machine Learning
- 基于区域参考点计算形状距离，生成几何特征向量
- 能精确捕捉空间对象间关系，优于栅格化方法
- 适合处理点线面等各类地理矢量数据
矢量模式地理空间数据（点、线、多边形）需转换为适配传统机器学习与人工智能模型的形式。编码方法旨在将形状表示为捕捉其关键几何特性的向量。本文提出基于兴趣区域内一组参考点到形状的缩放距离的编码方法——多点邻近（MultiPoint Proximity, MPP）编码。该方法适用于任意类型形状，可参数化机器学习模型中矢量地理特征的表示。实验表明，MPP编码具备形状中心性与连续性，能基于几何特征区分空间对象，并以高精度捕捉成对空间关系。在所有情况下，其性能均优于基于栅格化的替代方法。
原文摘要 · Abstract (English)
Vector-mode geospatial data -- points, lines, and polygons -- must be encoded into an appropriate form in order to be used with traditional machine learning and artificial intelligence models. Encoding methods attempt to represent a given shape as a vector that captures its essential geometric properties. This paper presents an encoding method based on scaled distances from a shape to a set of reference points within a region of interest. The method, MultiPoint Proximity (MPP) encoding, can be applied to any type of shape, enabling the parameterization of machine learning models with encoded representations of vector-mode geospatial features. We show that MPP encoding possesses the desirable properties of shape-centricity and continuity, can be used to differentiate spatial objects based on their geometric features, and can capture pairwise spatial relationships with high precision. In all cases, MPP encoding is shown to perform better than an alternative method based on rasterization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。