Remote sensing dataset for machine learning in archaeological site recognition

The repository's data contains fragments of imagery used as input for machine learning. This data was developed from three types of imagery: Aerial photographs, Rao's Q index created from a DTM, SAR imagery. All three source layers were converted to grayscale images and then combined in specific proportions: 65% aerial photographs, 20% Rao's Q index, and 15% SAR imagery. These chosen proportions maximized the model's accuracy by mitigating noise in the SAR data while still incorporating subtle terrain variations. The conversion to grayscale was done to facilitate faster model training and to account for the varying colors of vegetation and soil features. This research was funded by the National Science Centre, Poland under Grant no. 2024/08/X/ST10/00587本仓库所包含的数据为用作机器学习输入的影像片段。该数据集基于三类影像源构建： 航空影像（Aerial Photographs）、 由数字地形模型（DTM）生成的Rao Q指数（Rao's Q index）、 合成孔径雷达（SAR）影像。 上述三类源图层均被转换为灰度图像，并按特定比例融合：航空影像占65%、Rao Q指数占20%、合成孔径雷达（SAR）影像占15%。该比例设置通过抑制SAR数据中的噪声，同时保留细微地形变化特征，实现了模型精度的最大化。将影像转换为灰度图像，旨在加快模型训练速度，并适配植被与土壤特征的色彩差异。 本研究由波兰国家科学中心（National Science Centre, Poland）资助，项目编号为2024/08/X/ST10/00587。