An automated approach for street trees detection using mobile laser scanner data

Abstract

The trees on the street are the primary element of urban vegetation and essential for environmental conservation. Environmental quality improvement and offering amenities for inhabitants are the fundamental roles of street trees. The visual magnificence of urban landscape is also reliant on them. Accurate, conversant inventory information and monitoring conditions are necessary for urban horticultural planning, maintenance, for land use and land cover management. In this propound research work, an automated method for detecting the street trees utilizing Mobile Laser Scanner (MLS) data has been proposed. The proposed method consists of four phases. The first phase divides the MLS dataset into regular two-dimensional square grids. Only eligible grids are further sliced into vertical layers in the second phase. Region growing is been performed to detect the street trees in the third phase of methodology. Elimination of redundant tree points has been performed in the last phase of proposed methodology. This approach has been tested on a MLS dataset. The result shows the precision, recall and F Score of the method for street trees detection as 97.64%, 96.77% and 0.9729 respectively. Significance of Present Study Large number of trees are evacuated during road broadening and maintenance. Therefore, creation of up-to-date street trees inventory is required for proper monitoring and planning of their growth rate, place and proximity in order to save them. Mobile laser scanning facilitates the mapping of road side objects at high speed. However, till now lots of manual work has been presented in processing of Lidar dataset and the bottleneck processing has shifted from the data collection phase to data processing phase. Often number of days are disbursed on processing of data which are captured in couple of hours due to the massive data size. In such scenario to analyze a particular kind of geo-spatial feature preferably would be better to extract feature from the huge data size. Therefore, automated and time efficient extraction of geospatial feature is very indispensable. In the present study an automated method for the detection of street trees has been proposed. Manual processing of Lidar dataset for the identification of trees has been nearly eliminated along with less time complexity and good accuracy. The major implication of this research is that it can automatically, time efficiently and accurately detect the street trees from entire big and bulky captured Lidar point cloud dataset. The urban environmental conservation, monitoring, development and other related authorities shall be considerably benefitted from this research.