Remote sensing and machine learning to identify vegetation in urban residential gardens

Sonia Williams (Office National Statistics)

Given their environmental and emotional benefit identifying and understanding the features of urban green spaces is becoming of greater importance. Current approaches often assume residential gardens are almost exclusively covered by natural vegetation and do not take in to account urban areas such as steps, patios and paths. The Data Science Campus and Ordnance Survey (OS) have used remote sensing and machine learning techniques to improve upon the current approach used within the ONS to identify the proportion of vegetation in UK residential gardens. A test library of labelled images was created by taking 100 images randomly sampled from Bristol and Cardiff and independently classified to provide a ground truth. Application of several algorithms to the labelled data indicated sensitivity to the presence of shadows. Consequently a neural network classifier was developed specifically to be insensitive to the effects of shadow. Results support the conclusion that a neural network can more accurately classify vegetation and is less susceptible to the effect of shadows when compared with the other algorithms. Additional information can be found at: