GeoAI has emerged as an exciting interdisciplinary research area that
combines spatial theories and data with cutting-edge AI models to address
geospatial problems in a novel, data-driven manner. While GeoAI research has
flourished in the GIScience literature, its reproducibility and replicability
(R&R), fundamental principles that determine the reusability, reliability, and
scientific rigor of research findings, have rarely been discussed. This paper
aims to provide an in-depth analysis of this topic from both computational and
spatial perspectives. We first categorize the major goals for reproducing GeoAI
research, namely, validation (repeatability), learning and adapting the method
for solving a similar or new problem (reproducibility), and examining the
generalizability of the research findings (replicability). Each of these goals
requires different levels of understanding of GeoAI, as well as different
methods to ensure its success. We then discuss the factors that may cause the
lack of R&R in GeoAI research, with an emphasis on (1) the selection and use of
training data; (2) the uncertainty that resides in the GeoAI model design,
training, deployment, and inference processes; and more importantly (3) the
inherent spatial heterogeneity of geospatial data and processes. We use a deep
learning-based image analysis task as an example to demonstrate the results'
uncertainty and spatial variance caused by different factors. The findings
reiterate the importance of knowledge sharing, as well as the generation of a
"replicability map" that incorporates spatial autocorrelation and spatial
heterogeneity into consideration in quantifying the spatial replicability of
GeoAI research.

GeoAI 研究偏重于 GIScience 文献，本文从计算和空间角度深入分析了 GeoAI 研究的重复性和可复制性，包括验证、学习和适应方法以解决相似或新问题，以及研究结果的普适性。通过深度学习的图像分析任务实例，强调了训练数据选择和使用、GeoAI 模型设计、训练和部署过程中的不确定性，以及地理空间数据和过程的空间异质性对结果的影响。结果强调了知识共享的重要性，并提出了一个综合考虑空间自相关和空间异质性的 “可复制性图” 用于量化 GeoAI 研究的空间可复制性。