Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Supervised Parametric Classification of Aerial LiDAR Data
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 3 - Volume 03
Scene completion using millions of photographs
ACM SIGGRAPH 2007 papers
Automatic extraction and delineation of single trees from remote sensing data
Machine Vision and Applications
Automatic alignment of large-scale aerial rasters to road-maps
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
A perception-based color space for illumination-invariant image processing
ACM SIGGRAPH 2008 papers
Land cover change detection: a case study
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic extraction of road intersection position, connectivity, and orientations from raster maps
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
2D tree detection in large urban landscapes using aerial LiDAR data
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Bridge detection in grid terrains and improved drainage enforcement
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Gabor descriptors for aerial image classification
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
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We propose an automatic approach to tree detection from aerial imagery. First a pixel-level classifier is trained to assign a {tree, non-tree} label to each pixel in an aerial image. The pixel-level classification is then refined by a partitioning algorithm to a clean image segmentation of tree and non-tree regions. Based on the refined segmentation results, we adopt template matching followed by greedy selection to locate individual tree crowns. As training a pixel-level classifier requires manual generation of ground-truth tree masks, we propose methods for automatic model and training data selection to minimize the manual work and scale the algorithm to the entire globe. We test the algorithm on thousands of production aerial images across different countries. We demonstrate high-quality tree detection results as well as good scalability of the proposed approach.