High-resolution UAV visible remote sensing imagery and software program

Description

This dataset primarily provides field-collected high-resolution UAV visible remote sensing imagery. It serves as high-precision ground truth and benchmark data for the refined manual annotation of GF-2 satellite imagery. The dataset encompasses field-captured imagery from three typical epidemic areas: Liaoning, Hunan, and Zhejiang.Researchers can perform infected tree detection by following the step-by-step procedures outlined in the project code.

Steps to reproduce

To generate high-precision ground truth data, aerial surveys were conducted over the core plots using a DJI Mavic 2 Pro Unmanned Aerial Vehicle (UAV) concurrently with the acquisition of the satellite imagery. The acquired UAV data were processed to generate orthomosaics with a spatial resolution better than 0.1 m. Subsequently, utilizing the UAV orthomosaics as a reference, high-precision geometric registration was performed on the preprocessed GF-2 images. By uniformly selecting more than 30 ground control points (GCPs) across each scene, the root mean square error (RMSE) of the registration was strictly constrained to within 0.5 pixels（Zhou, 2023）. This ensured precise spatial correspondence between the satellite imagery and the high-resolution reference imagery, thereby laying a solid foundation for the subsequent fine-grained manual annotation.Based on the high-precision registered GF-2 imagery, fine-grained manual visual in-terpretation and annotation of ground objects were conducted using the UAV imagery as a high-resolution reference. The annotation classification system comprises four categories: background, healthy pine trees, PWD-infected trees, and others. The visual interpretation criteria for the different categories are as follows: the crowns of infected trees typically ap-pear orange-red, bright red, or reddish-brown, with a circular or elliptical shape, and are generally distributed sparsely, occasionally forming clusters of multiple diseased trees. In contrast, healthy crowns are typically dark green with densely aggregated foliage. Typical interfering ground objects other than pines are annotated as "others," while all remaining areas are annotated as "background." All annotated vector polygons were subsequently rasterized into label maps corresponding pixel-by-pixel to the high-dimensional feature cube. Subsequently, to generate samples suitable for training the deep learning model, a sliding window approach was employed to crop the entire high-dimensional feature cube and the corresponding label maps into 256 × 256 pixel patches. To augment the sample size and ensure contextual continuity, the sliding stride was set to 128 pixels. The construction of the dataset followed a rigorous procedure of screening, partition-ing, and balancing. First, all sample patches generated via the sliding window underwent a strict quality screening. Samples wherein the valid annotated area (non-background) accounted for less than 1% of the pixels were discarded to ensure that each sample con-tained sufficient effective information. Following this screening, a total of 18,756 high-quality samples were acquired, with examples illustrated in Figure 2. Subsequently, all high-quality samples were randomly partitioned into a training set, a validation set, and a test set at a ratio of 8:1:1. The actual acquisition details of the sample dataset are presented in Table 2.

Institutions

• Chinese Academy of Sciences

  Beijing, Beijing

Categories

Funders

• National Natural Science Foundation of China

  Beijing, Beijing

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