We use the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, hei...
Suresh K. Lodha, Darren N. Fitzpatrick, David P. H...
Abstract. This paper introduces a robust variant of AdaBoost, cwAdaBoost, that uses weight perturbation to reduce variance error, and is particularly effective when dealing with da...
We classify 3D aerial LiDAR scattered height data into buildings, trees, roads, and grass using the Support Vector Machine (SVM) algorithm. To do so we use five features: height, ...
Suresh K. Lodha, Edward J. Kreps, David P. Helmbol...
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 segmentati...
This paper presents our solution for KDD Cup 2008 competition that aims at optimizing the area under ROC for breast cancer detection. We exploited weighted-based classification me...