Carolina Bay Object Detector Images and Labels

Contained here (in CarolinaBayTrainingData.zip) are the images (jpegs) and labels (csv) that were used to train a bounding box object detector for Carolina Bays detailed in the following paper:

Lundine, M., Trembanis, A., Using Convolutional Neural Networks for Detection and Morphometric Analysis of Carolina Bays from Publicly Available Digital Elevation Models, Remote Sensing, 2021, Volume 13(18), 3770, https://doi.org/10.3390/rs13183770.

Each image is a single channel jpeg. The channel corresponds to gridded LiDAR elevation values remapped to 256 values. Each image was normalized individually for maximum contrast.

The labels csv contains the coordinates for each bounding box annotation of Carolina Bays in each available image. The columns are: filename, width, height, label, xmin, ymin, xmax, ymax, label_value. Each row is an annotation of a Carolina Bay. Filename corresponds to the image, width is the width in pixels of that image, height is the height in pixels of that image. xmin, ymin, xmax, ymax are the bounding box coordinates for the annotation. Label is 'bay' for each annotation, with a label_value of 1.

Feel free to experiment with this dataset, add to it, and improve upon the results.

Also contained here (in CarolinaBayDetections.zip) are the detection results (as unaggregated polygons, as aggregated polygons, as smooth polygons, and as points).

本数据集打包于CarolinaBayTrainingData.zip中，包含用于训练卡罗莱纳湾（Carolina Bays）边界框目标检测器（bounding box object detector）的图像（JPEG格式）与标签（CSV格式），相关研究详见下述论文：
Lundine M、Trembanis A. 利用卷积神经网络（Convolutional Neural Networks）从公开数字高程模型中检测卡罗莱纳湾并开展形态计量分析[J]. 遥感, 2021, 13(18): 3770. https://doi.org/10.3390/rs13183770.

每张图像均为单通道JPEG图像，其通道对应重新映射至256个灰度级的网格化激光雷达（LiDAR）高程数值。所有图像均单独进行归一化处理以实现最佳对比度。

该标签CSV文件包含每张可用图像中卡罗莱纳湾边界框标注的坐标信息，各列依次为：文件名（filename）、图像宽度（单位：像素）、图像高度（单位：像素）、标注类别（label）、xmin、ymin、xmax、ymax、标注值（label_value）。每一行对应一个卡罗莱纳湾的标注，所有标注的label字段均为"bay"，对应的label_value为1。

欢迎基于本数据集开展实验、拓展数据集并优化检测结果。

本压缩包CarolinaBayDetections.zip中还包含检测结果，格式涵盖未聚合多边形、聚合多边形、平滑多边形以及点集数据。