A review of location encoding for GeoAI: methods and applications
A review of location encoding for GeoAI: methods and applications
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DOI：
10.1080/13658816.2021.2004602
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发表时间：
2021-11
影响因子：
5.7
通讯作者：
Gengchen Mai;K. Janowicz;Yingjie Hu;Song Gao;Bo Yan;Rui Zhu;Ling Cai;Ni Lao
中科院分区：
文献类型：
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作者：
Gengchen Mai;K. Janowicz;Yingjie Hu;Song Gao;Bo Yan;Rui Zhu;Ling Cai;Ni Lao
在更广泛的地球科学领域，人工智能模型的一个共同需求是在隐藏的嵌入空间中编码各种类型的空间数据，如点、折线、多边形、图形或光栅，以便它们可以很容易地合并到深度学习模型中。一个基本步骤是将单个点位置编码到嵌入空间中，这样这种嵌入对于下游机器学习模型来说是学习友好的。我们称这个过程为位置编码。然而，对位置编码、其潜在应用和需要解决的关键挑战缺乏系统的综述。本文旨在填补这一空白。本文首先给出了位置编码的形式化定义，并讨论了位置编码在GeoAI研究中的必要性。接下来，我们对位置编码的研究现状进行了全面的综述。我们根据位置编码模型的输入和编码方法将其分为不同的类别，并根据它们的参数化、多尺度、距离保持和方向感知进行了比较。我们证明了现有的位置编码器可以统一在一个公式框架下。讨论了位置编码的应用。最后，我们指出了未来需要解决的几个挑战。
ABSTRACT A common need for artificial intelligence models in the broader geoscience is to encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters, in a hidden embedding space so that they can be readily incorporated into deep learning models. One fundamental step is to encode a single point location into an embedding space, such that this embedding is learning-friendly for downstream machine learning models. We call this process location encoding. However, there lacks a systematic review on location encoding, its potential applications, and key challenges that need to be addressed. This paper aims to fill this gap. We first provide a formal definition of location encoding, and discuss the necessity of it for GeoAI research. Next, we provide a comprehensive survey about the current landscape of location encoding research. We classify location encoding models into different categories based on their inputs and encoding methods, and compare them based on whether they are parametric, multi-scale, distance preserving, and direction aware. We demonstrate that existing location encoders can be unified under one formulation framework. We also discuss the application of location encoding. Finally, we point out several challenges that need to be solved in the future.