Artificial intelligence (AI) is changing fundamentally the way how IT solutions are implemented and operated across all application domains, including the geospatial domain. This contribution outlines AI-based techniques for 3D point clouds and geospatial digital twins as generic components of geospatial AI. First, we briefly reflect on the term “AI” and outline technology developments needed to apply AI to IT solutions, seen from a software engineering perspective. Next, we characterize 3D point clouds as key category of geodata and their role for creating the basis for geospatial digital twins; we explain the feasibility of machine learning (ML) and deep learning (DL) approaches for 3D point clouds. In particular, we argue that 3D point clouds can be seen as a corpus with similar properties as natural language corpora and formulate a “Naturalness Hypothesis” for 3D point clouds. In the main part, we introduce a workflow for interpreting 3D point clouds based on ML/DL approaches that derive domain-specific and application-specific semantics for 3D point clouds without having to create explicit spatial 3D models or explicit rule sets. Finally, examples are shown how ML/DL enables us to efficiently build and maintain base data for geospatial digital twins such as virtual 3D city models, indoor models, or building information models.

人工智能（AI）从根本上改变了IT解决方案在所有应用程序领域（包括地理空间领域）中的实现和操作方式。此文稿概述了基于3D点云和地理空间数字孪生的基于AI的技术，这些技术是地理空间AI的通用组件。首先，我们从软件工程的角度简要回顾一下“ AI”一词，并概述将AI应用于IT解决方案所需的技术发展。接下来，我们将3D点云定性为地理数据的关键类别，并确定其在创建地理空间数字孪生的基础中的作用；我们将说明针对3D点云的机器学习（ML）和深度学习（DL）方法的可行性。尤其是，我们认为3D点云可以看作是具有与自然语言语料库相似的属性的语料库，并为3D点云制定了“自然假说”。在主要部分中，我们介绍了一种基于ML / DL方法解释3D点云的工作流，该方法可以为3D点云导出特定于领域和特定于应用程序的语义，而不必创建明确的空间3D模型或明确的规则集。最后，示例显示了ML / DL如何使我们能够有效地构建和维护地理空间数字双胞胎的基础数据，例如虚拟3D城市模型，室内模型或建筑信息模型。我们介绍了一种基于ML / DL方法的3D点云解释工作流程，该方法可以为3D点云导出特定于领域和特定于应用程序的语义，而无需创建明确的空间3D模型或明确的规则集。最后，示例显示了ML / DL如何使我们能够有效地构建和维护地理空间数字双胞胎的基础数据，例如虚拟3D城市模型，室内模型或建筑信息模型。我们介绍了一种基于ML / DL方法的3D点云解释工作流程，该方法可以为3D点云导出特定于领域和特定于应用程序的语义，而无需创建明确的空间3D模型或明确的规则集。最后，示例显示了ML / DL如何使我们能够有效地构建和维护地理空间数字双胞胎的基础数据，例如虚拟3D城市模型，室内模型或建筑信息模型。