A TIMBER Framework for Mining Urban Tree Inventories Using Remote Sensing Datasets
A TIMBER Framework for Mining Urban Tree Inventories Using Remote Sensing Datasets
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DOI：
10.1109/icdm.2018.00183
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Yiqun Xie;Han Bao;S. Shekhar;Joseph K. Knight
中科院分区：
文献类型：
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作者：
Yiqun Xie;Han Bao;S. Shekhar;Joseph K. Knight
树木清单是许多社会应用（例如城市规划）的重要数据集。然而，大多数城市地区仍然没有树木库存。我们的目标是使用遥感数据集在城市地区的个体层面上实现大规模树木识别的自动化。由于城市场景中景观的复杂性以及地面实况数据的缺乏，该问题具有挑战性。在相关工作中，树木识别算法主要集中在景观大多与树木同质的受控森林区域，使得该方法难以推广到城市环境。我们提出了一个 TIMBER 框架来在复杂的城市环境中查找单个树木，并提出了一个核心对象缩减（CORE）算法来提高 TIMBER 的计算效率。实验表明，TIMBER 可以高效、高精度地检测城市树木。
Tree inventories are important datasets for many societal applications (e.g., urban planning). However, tree inventories still remain unavailable in most urban areas. We aim to automate tree identification at individual levels in urban areas at a large scale using remote sensing datasets. The problem is challenging due to the complexity of the landscape in urban scenarios and the lack of ground truth data. In related work, tree identification algorithms have mainly focused on controlled forest regions where the landscape is mostly homogeneous with trees, making the methods difficult to generalize to urban environments. We propose a TIMBER framework to find individual trees in complex urban environments and a Core Object REduction (CORE) algorithm to improve the computational efficiency of TIMBER. Experiments show that TIMBER can efficiently detect urban trees with high accuracy.