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We present a scalable approach to tree detection in large urban landscapes using aerial LiDAR data. Similar to our previous work in 2006, our current method consists of segmentation followed by classification. However, unlike our previous work, the current approach does not use color information or aerial imagery, and hence is more generally applicable. Also, our current approach has been successfully tested on two very large datasets, which are many orders of magnitude larger than the dataset used in 2006. Specifically, we use a North American dataset, containing 125 million LiDAR returns over 3 km2, and a European dataset, containing 200 million LiDAR returns over 7 km2. For both datasets, we report precision and recall rates of over 95%.