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SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)

Authors: Feng, Zhengpeng; She, Yihang; Srinivasan, Keshav;

SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)

Abstract

This page only provides the drone-view image dataset. For the ground-level image dataset, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I). For the point clouds, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III). The dataset contains drone-view RGB images, depth maps and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a color_palette.xlsx file, which contains the RGB_id and corresponding RGB values. All files follow the naming convention: {central_tree_id}_{timestamp}, where {central_tree_id} represents the ID of the tree centered in the image, which is typically in a prominent position, and timestamp indicates the time when the data was collected. Specifically, each 7z file includes the following folders: rgb: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36. depth_pfm: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection. instance_segmentation: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps tree_id to RGB_id. The tree_id can be used to look up detailed information about each tree in obj_info_final.xlsx, while the RGB_id can be matched to the corresponding RGB values in color_palette.xlsx. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image. obj_info_final.xlsx: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm). landscape_info.txt: This file contains the ground location information within the scene, sampled every 0.5 meters. For birch_forest, broadleaf_forest, redwood_forest and rainforest, we also provided COCO-format annotation files (.json). Two such files can be found in these datasets: {name}_coco.json: This file contains the annotation of each tree in the scene. {name}_filtered.json: This file is derived from the previous one, but filtering is applied to rule out overlapping instances. ⚠️: 7z files that begin with "!" indicate that the RGB values in the images within the instance_segmentation folder cannot be found in color_palette.xlsx. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in link1 and link2.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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