<p><strong>Abstract.</strong> Singapore, branded as a “City in a Garden”, has a long standing commitment to green the nation, one which has resulted in trees becoming an integral component of the urban environment. Similarly for its digital twin, Virtual Singapore, we undertake the research to automate the population of this virtual city with semantically and biologically representative trees in a CityGML (City Geography Markup Language) format. This paper presents our framework of modeling trees for Virtual Singapore, showcasing an array of methodologies in data acquisition of light detection and ranging (LiDAR) and satellite images, tree extraction and quantification, and 3D tree modeling at LODs (level of details) 1, 2 and 3. The paper will also highlight challenges and chosen methodologies along with the preliminary results of this framework.</p>

<p><strong>Abstract.</strong> 3D tree database provides essential information of tree species abundance, spatial distribution and tree height for forest mapping, sustainable urban planning and 3D city modelling. Fusion of passive optical satellite imagery and active Lidar data can potentially be exploited for operational forest inventory. However, such fusion requires very high geometric accuracy for both data sets. This paper proposes an approach for 3D tree information extracted from passive and active data integrating into existing tree database by effectively using geometric information of satellite camera model and laser scanner scanning geometry. The paper also presents the individual methods for tree crown identification and delineation from satellite images and lidar point cloud data respectively, the geometric correction of tree position from tree top to tree base. The ground truth accuracy assessment for the tree extracted is also present.</p>

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