Abstract
Building function labelling plays an important role in understanding human activities inside buildings. This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data with an extreme gradient boosting tree (XGBoost). The classification framework is verified based on a dataset from Shenzhen, China. An extended label system for six building types (residential, commercial, office, industrial, public facilities, and others) was applied, and various social functions were considered. The overall classification accuracies were 88.15% (kappa index = 0.72) and 85.56% (kappa index = 0.69). The importance of features was evaluated using the occurrence frequency of features at decision nodes. In the six-category classification system, the basic building attributes (22.99%) and POIs (46.74%) contributed most to the classification process; moreover, the building footprint (7.40%) and distance to roads (11.76%) also made notable contributions. The result shows that it is feasible to extract building environments from POI labels and building footprint geometry with a dimensional reduction model using an autoencoder. Additionally, crowdsensing data (e.g., POI and distance to roads) will become increasingly important as classification tasks become more complicated and the importance of basic building attributes declines.
Highlights
With the development of sensors and computational techniques, many urban studies have modelled cities as diverse and fine-scale spatial units including functional zones [1,2], blocks [3,4], and buildings [5,6]
As a function label, building type is widely used as a fundamental input in constructing type-wise models in fields such as energy consumption prediction [44], human mobility mapping [45], urban land surface construction, climate modelling [45], and health outcome evaluation [46]
Building function labels play a significant role in understanding the urban environment
Summary
With the development of sensors and computational techniques, many urban studies have modelled cities as diverse and fine-scale spatial units including functional zones [1,2], blocks [3,4], and buildings [5,6]. As a type of population hub, buildings are structures where many human activities occur. These human activities can be in turn classified based on the characteristics of the building where they occur [14]. Among all building characteristics, building type is one of the most commonly used, since it provides a categorical label and corresponding semantic information, which can be leveraged to infer the human activities that occur in the corresponding buildings
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