Cocoa Probability Maps for Nigeria 2020

Cocoa probability map generated as part of the Master's thesis “Generalization of Cocoa Mapping Across Countries Using Foundation Models and Sparse Labels”, Robotics, Cognition, Intelligence (RCI), Technical University of Munich (TUM). The main dataset, 2020-nigeria-cocoa-probs.zip, consists of multiple GeoTIFF tiles covering Nigeria. Each tile is stored in its local coordinate reference system (CRS), as defined in the metadata of the respective file. All tiles have a spatial resolution of 10 m. Raster pixel values represent the model-predicted probability of cocoa presence, encoded as integers: 0 → 0% probability 250 → 100% probability 255 → No data The values were linearly scaled from the model's continuous 0–1 probability output. The full methodology, model architecture, training procedure, and data sources are described in the aforementioned Master’s thesis. Supplementary Files Three auxiliary prediction sets used in the generation of the final cocoa probability map are also provided for transparency and reproducibility: 2020-aef-predictions.zip – Predictions from the AlphaEarth-based model. 2020-croma-predictions.zip – Predictions from the CROMA foundation model. 2020-baseline-cnn-predictions.zip – Predictions from the baseline CNN model. These datasets are included as supplementary material to document intermediate outputs but are not required for interpreting or using the final cocoa probability map.本数据集为慕尼黑工业大学（Technical University of Munich, TUM）机器人、认知与智能（Robotics, Cognition, Intelligence, RCI）课题组的硕士论文《基于基础模型与稀疏标签实现跨国可可种植区制图泛化》中生成的可可种植区概率分布图。 核心数据集`2020-nigeria-cocoa-probs.zip`包含多张覆盖尼日利亚全境的GeoTIFF瓦片，每张瓦片均采用对应文件元数据中定义的本地坐标参考系统（Coordinate Reference System, CRS）存储，所有瓦片的空间分辨率均为10米。 栅格像素值代表模型预测的可可种植区存在概率，以整数编码： - 0 对应 0% 存在概率 - 250 对应 100% 存在概率 - 255 代表无数据 该数值由模型输出的连续0~1概率结果经线性缩放得到。完整的研究方法、模型架构、训练流程与数据源均已在上述硕士论文中详细说明。 为保障研究的透明度与可复现性，本数据集还附带了三组用于生成最终可可概率分布图的辅助预测集： 1. `2020-aef-predictions.zip`：基于AlphaEarth模型生成的预测结果 2. `2020-croma-predictions.zip`：基于CROMA基础模型生成的预测结果 3. `2020-baseline-cnn-predictions.zip`：基于基线卷积神经网络（Convolutional Neural Network, CNN）模型生成的预测结果 这些数据集仅作为中间输出结果的补充文档材料，无需用于解读或使用最终可可种植区概率分布图。