Against the backdrop of rapidly expanding urban structures, land policies in many countries have been adapted to contain and redirect growth to existing urban structures. However, obstacles remain to measure the effects of policies. In the meantime, geoinformation technologies have given rise to a wide range of approaches to measure and describe urban form. Nevertheless, its application for the assessment of land policy has a high, but not yet fully exploited, potential. It is thus the aim of this research to address and investigate the options of spatial analysis and machine learning in particular to analyse urban form from a land policy perspective. To do so, we develop urban metrics informed by urban planning and land readjustment policies of two countries describing urban form on different spatial levels. We therefore formulate hypotheses on causal relations between policy and form. Based on the metrics, we apply the random forest algorithm to classify the building stock of the region. We then extract the residential areas, those with single-family houses, as this is where the effects of the policy are considered most visible. In a next step, we use random forest to predict the nationality of a building. Through variable importance measures, we identify and discuss urban morphological differences between the two countries and test the hypotheses on effects of land policies. We develop and test the approach for the French-German city-region of Strasbourg using OpenStreetMap data. We identify significant differences in the building coverage ratios, which tend to be higher in Germany. This can be linked to differences in planning regulations. Furthermore, German residential areas appear to be more diverse in urban form. Differences in land readjustment policies have proven to be plausible here, as French policies favour strong actors that develop residential areas more uniformly. In Germany, policies favour fragmented ownership-oriented development of residential areas. The metrics and the applied algorithm for building classification have proven to be robust in terms of data heterogeneity and have shown high levels of accuracy. They could also be successfully used for tracing causal relations.

在迅速扩展的城市结构的背景下，许多国家的土地政策都进行了调整，以遏制增长并将其转移到现有的城市结构。但是，衡量政策效果仍然存在障碍。同时，地理信息技术引起了各种各样的测量和描述城市形态的方法。然而，将其用于评估土地政策具有很高的潜力，但尚未充分开发。因此，本研究的目的是解决和研究空间分析和机器学习的选项，尤其是从土地政策的角度分析城市形态。为此，我们开发了根据两个国家的城市规划和土地调整政策而制定的城市度量标准，这两个国家在不同的空间水平上描述了城市形态。因此，我们对政策与形式之间的因果关系提出了假设。基于指标，我们应用随机森林算法对区域的建筑存量进行分类。然后，我们提取居民区，即拥有单户住宅的居民区，因为这是政策效果最明显的地方。下一步，我们将使用随机森林来预测建筑物的国籍。通过重要性变量的度量，我们确定并讨论了两国之间的城市形态差异，并检验了对土地政策影响的假设。我们使用OpenStreetMap数据开发和测试法国-德国史特拉斯堡城市地区的方法。我们发现建筑物的覆盖率存在显着差异，在德国这种差异往往更大。这可以与计划法规上的差异联系在一起。此外，德国居民区在城市形式上似乎更加多样化。事实证明，土地调整政策的差异是合理的，因为法国的政策偏向于强有力的行动者，他们会更加统一地开发居民区。在德国，政策支持以所有权为导向的住宅区的零散发展。度量标准和用于建筑物分类的应用算法已被证明在数据异质性方面很健壮，并且显示出很高的准确性。它们也可以成功地用于追踪因果关系。政策主张以零散的，以所有权为导向的住宅区开发。度量标准和用于建筑物分类的应用算法已被证明在数据异质性方面很健壮，并且显示出很高的准确性。它们也可以成功地用于追踪因果关系。政策主张以零散的，以所有权为导向的住宅区开发。度量标准和用于建筑物分类的应用算法已被证明在数据异质性方面很健壮，并且显示出很高的准确性。它们也可以成功地用于追踪因果关系。