Mapping urban trees within cadastral parcels using an object-based convolutional neural network

Urban trees offer significant benefits for improving the sustainability and liveability of cities, but its monitoring is a major challenge for urban planners. Remote-sensing based technologies can effectively detect, monitor and quantify urban tree coverage as an alternative to field-based measurements. Automatic extraction of urban land cover features with high accuracy is a challenging task and it demands artificial intelligence workflows for efficiency and thematic quality. In this context, the objective of this research is to map urban tree coverage per cadastral parcel of Sandy Bay, Hobart from very high-resolution aerial orthophoto and LiDAR data using an Object Based Convolution Neural Network (CNN) approach. Instead of manual preparation of a large number of required training samples, automatically classified Object based image analysis (OBIA) output is used as an input samples to train CNN method. Also, CNN output is further refined and segmented using OBIA to assess the accuracy. The result shows 93.2% overall accuracy for refined CNN classification. Similarly, the overlay of improved CNN output with cadastral parcel layer shows that 21.5% of the study area is covered by trees. This research demonstrates that the accuracy of image classification can be improved by using a combination of OBIA and CNN methods. Such a combined method can be used where manual preparation of training samples for CNN is not preferred. Also, our results indicate that the technique can be implemented to calculate parcel level statistics for urban tree coverage that provides meaningful metrics to guide urban planning and land management practices.