Presently remote sensing appears to be the only technology that makes it possible to conduct an inventory of tree taxa throughout the whole city. One of the main challenges during a remote sensing implementation project is the constructing of a map legend for tree taxa. The article presents the first comprehensive investigation into the construction of a legend for urban tree species mapping in the entire city, using machine learning and airborne data. The analyses were based on the CatBoost algorithm, which is relatively new in environmental research, as well as hyperspectral and LiDAR data acquired by instrument fusion. The analysis covered the entire city of Warsaw (517.24 km). The main aim of the study was to compare three approaches to creating map legends of taxonomic diversity in city trees. This research sought to answer which of these three approaches to creating a map legend produces a result with the highest application potential for use by urban greenery managers. Two scenarios are based on different levels of taxonomic organization. The first one, based on a generic level, contained 42 classes and obtained an overall accuracy score (OA) of 0.809. The second one, on the species level, consisted of 81 classes, and the OA reached an accuracy of 0.728. The third scenario presents a mixed approach by grouping classes as different combinations of genus, species and variety. It contained 60 classes and its OA totaled to 0.798. The obtained results indicate that while in the classification process the generic scenario achieved the highest accuracy and the species scenario had the most complex legend, the mixed scenario emerged as the most useful for the greenery managers because it presented a compromise between the complexity of the legend and the high accuracy of the map. In the mixed scenario, accurate classification was achieved by separating high-accuracy individual taxa at the species level and grouping the rest at different taxonomic levels. One of the most significant novelties of our research is proving that remote sensing currently makes it possible to develop a map of tree species containing as many as 60 classes of legends and to achieve an accuracy that allows for practical application in urban greenery management. The study lists suggestions of practical strategies for enhancing map accuracy such as aggregating similar species into species groups, creating distinct groups for ornamental cultivars with unique leaf characteristics or combining species or genera with similar traits.

中文翻译：

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用于城市树种测绘的机载数据和机器学习：加强图例设计，提高地图对城市绿化管理的适用性

目前，遥感似乎是唯一能够对整个城市的树木分类群进行清查的技术。遥感实施项目期间的主要挑战之一是构建树类群的地图图例。本文首次对利用机器学习和机载数据构建整个城市的城市树种测绘图例进行了全面调查。这些分析基于环境研究中相对较新的 CatBoost 算法，以及通过仪器融合获取的高光谱和激光雷达数据。分析覆盖了整个华沙市（517.24 公里）。该研究的主要目的是比较三种创建城市树木分类多样性地图图例的方法。本研究试图回答这三种创建地图图例的方法中哪一种所产生的结果对城市绿化管理者来说具有最高的应用潜力。两种情景基于不同级别的分类组织。第一个基于通用级别，包含 42 个类别，总体准确度得分 (OA) 为 0.809。第二个是物种层面的，包括81个类，OA的准确度达到0.728。第三种情况提出了一种混合方法，将类别分组为属、种和变种的不同组合。它包含 60 个类，其 OA 总计为 0.798。获得的结果表明，虽然在分类过程中，通用场景达到了最高的准确性，物种场景具有最复杂的图例，但混合场景对绿化管理者来说是最有用的，因为它呈现了图例复杂性之间的折衷。以及地图的高精度。在混合场景中，通过在物种水平上分离高精度的个体类群并在不同的分类水平上对其余类群进行分组来实现准确的分类。我们研究中最重要的创新之一是证明，遥感目前可以开发包含多达 60 类传说的树种地图，并达到可在城市绿化管理中实际应用的精度。该研究列出了提高地图准确性的实用策略的建议，例如将相似的物种聚合为物种组，为具有独特叶子特征的观赏品种创建不同的组，或者将具有相似特征的物种或属组合起来。