Derived data for Polat Lake (Eastern Anatolia): DSM, MLP land-cover classification, TWI, solar radiation

This dataset contains the derived geospatial products, trained machine-learning model and source code accompanying the article "Geomorphological and Radiative Controls on a Hypersaline Karst Doline Lake: Insights from Polat Lake (Eastern Anatolia)" submitted to CATENA (Elsevier, 2026). The study characterises Polat Lake — a small (~1.1 ha) hypersaline doline lake in the Erzincan-Divrigi Basin (Eastern Anatolia, Turkiye) — through UAV-based photogrammetry, deep-learning point-cloud classification, geomorphometric analysis and solar-radiation modelling. All raster products are georeferenced to WGS 84 / UTM zone 37N (EPSG:32637); the original DSM was produced at 5.68 cm/pixel ground sampling distance. Folder structure: 01_DSM_and_Orthomosaic -- UAV DSM (5.68 cm + 1 m) and multidirectional hillshade 02_Classification_outputs -- MLP-classified 341-million-point cloud + figure source PNGs 03_MLP_model -- PyTorch weights, scaler params, feature names, architecture 04_TWI_and_SolarRadiation -- TWI, annual solar radiation, aspect classes 05_Vector_layers -- Faults, geology (MTA-derived), water, peaks, contours (shapefiles) 06_Scripts_and_Code -- Training, inference, plotting Python scripts 07_Training_data -- Per-class training-sample CSVs (Bedrock, Evaporite, Soil, Wetland) 08_Geomorphometric_outputs -- Cross-section profile CSV and 2.5D bathymetric raster 09_UAV_metadata -- Agisoft Metashape processing report (camera positions, residuals) 10_Field_photos -- 18 representative UAV / handheld field photographs See README.md and LICENSE.txt at the root of the dataset for full documentation and citation guidance.本数据集包含投稿至《CATENA》（爱思唯尔，2026年）的论文《超盐喀斯特漏斗湖的地貌与辐射控制因素：安纳托利亚东部波拉特湖研究洞察》所附带的衍生地理空间产品、训练完成的机器学习模型及源代码。 本研究针对土耳其安纳托利亚东部埃尔津詹-迪夫里吉盆地内的波拉特湖——一座面积约1.1公顷的超盐性喀斯特漏斗湖——开展特征刻画，所用方法包括基于无人机（Unmanned Aerial Vehicle, UAV）的摄影测量、深度学习点云分类、地貌形态计量分析以及太阳辐射建模。所有栅格产品均采用WGS 84 / UTM第37N带（EPSG:32637）地理坐标系；原始数字表面模型（Digital Surface Model, DSM）的地面采样距离为5.68厘米/像素。 文件夹结构： 01_DSM_and_Orthomosaic —— 无人机数字表面模型（5.68厘米+1米）与多方向晕渲图 02_Classification_outputs —— 经多层感知器（Multi-Layer Perceptron, MLP）分类的3.41亿点云及图像源PNG文件 03_MLP_model —— PyTorch模型权重、标准化参数、特征名称及网络架构 04_TWI_and_SolarRadiation —— 地形湿度指数（Topographic Wetness Index, TWI）、年太阳辐射量与坡向分类图层 05_Vector_layers —— 断层、地质（土耳其地质调查局MTA衍生）、水体、山峰及等高线矢量图层（Shapefile格式） 06_Scripts_and_Code —— 模型训练、推理及绘图所用Python脚本 07_Training_data —— 按类别划分的训练样本CSV文件（涵盖基岩、蒸发岩、土壤、湿地四类） 08_Geomorphometric_outputs —— 剖面CSV文件与2.5D水深栅格图层 09_UAV_metadata —— Agisoft Metashape处理报告（含相机位置、残差信息） 10_Field_photos —— 18张具有代表性的无人机及手持拍摄的野外照片 请查阅数据集根目录下的README.md与LICENSE.txt文件，获取完整文档与引用指南。