Tag: NEON

DATA SCIENCE COMPETITION FOR CROSS-SITE DELINEATION AND CLASSIFICATION OF INDIVIDUAL TREES FROM AIRBORNE REMOTE SENSING DATA

Abstract: Delineating and classifying individual trees in remote sensing data is challenging. Many tree crown delineation methods have difficulty in closed-canopy forests and do not leverage multiple datasets. Methods to classify individual species are often accurate for common species, but… Read More

A REMOTE SENSING DERIVED DATA SET OF 100 MILLION INDIVIDUAL TREE CROWNS FOR THE NATIONAL ECOLOGICAL OBSERVATORY NETWORK

Abstract: Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote… Read More

A BENCHMARK DATASET FOR INDIVIDUAL TREE CROWN DELINEATION IN CO-REGISTERED AIRBORNE RGB, LIDAR AND HYPERSPECTRAL IMAGERY FROM THE NATIONAL ECOLOGICAL OBSERVATION NETWORK

Abstract: Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been… Read More

Cross-site learning in deep learning RGB tree crown detection

Abstract: Tree detection is a fundamental task in remote sensing for forestry and ecosystem ecology applications. While many individual tree segmentation algorithms have been proposed, the development and testing of these algorithms is typically site specific, with few methods evaluated… Read More