Tree Counting

Accurately counting trees from remote sensing data is crucial for sustainable forest management and climate change monitoring. Current research focuses on developing robust algorithms, often employing deep learning architectures like transformers and convolutional neural networks, to process both 2D imagery and 3D point cloud data (e.g., from LiDAR) for improved tree detection and counting accuracy. These methods are being evaluated and refined using benchmark datasets, with a particular emphasis on addressing challenges posed by dense canopies and diverse tree species across varying terrains. Improved tree counting techniques will enhance our ability to monitor forest health, assess carbon sequestration, and inform effective conservation strategies.