SiDroForest: Synthetic Siberian Larch Tree Crown Dataset of 10.000 instances in the Microsoft's Common Objects in Context dataset (coco) format

• Main Authors: van Geffen, Femke, Brieger, Frederic, Pestryakova, Luidmila A, Zakharov, Evgenii S, Herzschuh, Ulrike, Kruse, Stefan
• Format: Dataset
• Language: English
• Published: PANGAEA 2021
• Subjects: AWI_Envi; AWI Arctic Land Expedition; Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); Chukotka 2018; EN18062-2; EN18068-2; EN18074-2; EN18078-2; EN18083-2; HEIBRiDS; Helmholtz Einstein International Berlin Research School in Data Science; Larch; machine learning; Polar Terrestrial Environmental Systems @ AWI; RU-Land_2018_Yakutia; Siberia; Siberian Arctic; SiDroForest; synthetic data; UAV; Unmanned aerial vehicle; Alfred Wegener Institute; Berichte zur Polar- und Meeresforschung; Chukotka; Reports on Polar and Marine Research; Yakutia
• Online Access: https://doi.pangaea.de/10.1594/PANGAEA.932795; https://doi.org/10.1594/PANGAEA.932795

This synthetic Siberian Larch tree crown dataset was created for upscaling and machine learning purposes as a part of the SiDroForest (Siberia Drone Forest Inventory) project. The SiDroForest data collection (https://www.pangaea.de/?q=keyword%3A%22SiDroForest%22) consists of vegetation plots covered in Siberia during a 2-month fieldwork expedition in 2018 by the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research in Germany. During fieldwork fifty-six, 50*50-meter vegetation plots were covered by Unmanned Aerial Vehicle (UAV) flights and Red Green Blue (RGB) and Red Green Near Infrared (RGNIR) photographs were taken with a consumer grade DJI Phantom 4 quadcopter. The synthetic dataset provided here contains Larch (Larix gmelinii (Rupr.) Rupr. and Larix cajanderi Mayr.) tree crowns extracted from the onboard camera RGB UAV images of five selected vegetation plots from this expedition, placed on top of full-resized images from the same RGB flights. The extracted tree crowns have been rotated, rescaled and repositioned across the images with the result of a diverse synthetic dataset that contains 10.000 images for training purposes and 2000 images for validation purposes for complex machine learning neural networks. In addition, the data is saved in the Microsoft's Common Objects in Context dataset (COCO) format (Lin et al.,2013) and can be easily loaded as a dataset for networks such as the Mask R-CNN, U-Nets or the Faster R-NN. These are neural networks for instance segmentation tasks that have become more frequently used over the years for forest monitoring purposes. The images included in this dataset are from the field plots: EN18062 (62.17° N 127.81° E), EN18068 (63.07° N 117.98° E), EN18074 (62.22° N 117.02° E), EN18078 (61.57° N 114.29° E), EN18083 (59.97° N 113° E), located in Central Yakutia, Siberia. These sites were selected based on their vegetation content, their spectral differences in color as well as UAV flight angles and the clarity of the UAV images that were taken with ...