Baobab Detection and Analysis to Evaluate Anthropogenic Legacies in Eastern Africa

This data is related to a paper which evaluated different methods for detecting baobab trees in drone imagery, with the aim of comparing them to archaeological sites in eastern Africa. This dataset contains: 1. An ArcGIS Pro geodatabase, with a shapefile of hand-digitized baobab locations for Unguja Island, shapefile data of archaeological sites, shapefiles of settlement data digitized from a 1907 map of the island, squares showing seasonal data for the ZMI imagery, and an outline of the island created from SRTM elevation data, shapefiles and rasters of the automated detection training and testing files, and the results of automated detection using the Max Likelihood classifier, sorted by circularity and pixel size. 2. Excel files showing the nearness analysis for baobab trees, with the raw data and tables available 3. Excel files with confusion matrices for the automated detection in different seasons 4. Python code used for the tree detection viewer 5. A ReadMe file with all workflows described in detail本数据集关联一项研究论文，该论文评估了无人机影像中的猴面包树检测方法，旨在将检测结果与东非地区的考古遗址进行对比分析。 本数据集包含如下内容： 1. ArcGIS Pro地理数据库（geodatabase），其中包含安古迦岛（Unguja Island）的手工数字化猴面包树点位形状文件（shapefile）、考古遗址形状文件、从该岛1907年地图数字化得到的聚落数据形状文件、展示ZMI影像季节性数据的方格、基于SRTM高程数据（SRTM）生成的该岛轮廓、自动化检测训练与测试文件的形状文件与栅格数据，以及使用最大似然分类器（Max Likelihood classifier）得到的自动化检测结果，并按圆度与像素尺寸进行了排序。 2. 包含猴面包树邻近分析的Excel文件，附带可用的原始数据与表格。 3. 包含不同季节下自动化检测混淆矩阵（confusion matrix）的Excel文件。 4. 用于树木检测查看器的Python代码。 5. 包含所有流程详细说明的ReadMe文件（ReadMe）。