# Automatic Building Height Estimation: Machine Learning Models for Urban Image Analysis
###### Abstract  
Artificial intelligence (AI) is delivering major advances in the construction engineering sector in this era of building information modelling, applying data collection techniques based on urban image analysis. In this study, building heights were calculated from street-view imagery based on a semantic segmentation machine learning model. The model has a fully convolutional architecture and is based on the HRNet encoder and ResNexts depth separable convolutions, achieving fast runtime and state-of-the-art results on standard semantic segmentation tasks. Average building heights on a pilot German street were satisfactorily estimated with a maximum error of 3 m. Further research alternatives are discussed, as well as the difficulties of obtaining valuable training data to apply these models in countries with no training datasets and different urban conditions. This line of research contributes to the characterisation of buildings and the estimation of attributes essential for the assessment of seismic risk using automatically processed street-view imagery.  
artificial intelligence; semantic segmentation; convolutional neural networks; building height estimation; seismic exposure; street view imagery +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Automatic Building Height Estimation: Machine Learning Models for Urban Image Analysis  
**Citation:** [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON].  
includes the development of specific training datasets for this purpose, as proposed by [PERSON] et al. [6].  
The determination of seismic exposure is based on the characterisation of the attributes of the structure with different levels of detail to define the class of exposure of each building [7], as in the methods proposed by the Global Earthquake Model (GEM) foundation [4]. Previous research identified that one of the most influential attributes for seismic exposure classification is the height of the building or the number of floors [7; 8]. As the attribute that is needed is the number of floors, a building height estimation with a maximum absolute error of one floor would be sufficient. There is a need for automated machine learning models to estimate this parameter in cities with no available data, as is the case of the capitals in Central America, subjected to high potential seismic hazard and high physical and social vulnerability [9].  
Street view images are readily available in cities in developing countries or can be obtained at little cost. It is necessary to develop models capable of obtaining building heights from single-view images, allowing researchers to obtain this parameter in cities in developing countries from images available on the Internet or captured using mobile phones, creating the necessary databases for obtaining the seismic exposure of the buildings in a city.  
The aim of this research is to develop low-cost methods for building height data collection for the purposes of seismic risk assessment in cities in developing countries in Central America. This research contributes to determining the exposure of buildings using street view images processed by various convolutional neural networks. This will provide valuable tools that accurately estimate relevant seismic exposure features using free imagery, thus being easily applicable to the monitoring of large areas of cities in developing countries.  
This research applies technology originally developed for autonomous car driving to the gathering of building data and automatic building surveying. A new model is proposed combining RestNext and HRNet, and a method is developed for building height estimation using semantic segmentation and a single street view image.