发表机构
West Virginia University; Green University of Bangladesh; Stamford University Bangladesh（西弗吉尼亚大学; 孟加拉国绿色大学; 孟加拉国斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在UCI城市土地覆盖数据集上对比经典机器学习与表格深度学习模型，发现TDL模型在非线性交互显著且处理类别不平衡时能匹配或超越树集成方法，为城市制图提供互补优势。
AI 中文摘要
城市土地覆盖（ULC）分类在城市规划、环境监测和可持续发展中起着至关重要的作用。我们使用来自UCI机器学习知识库的ULC数据集研究此任务，该数据集包含从高分辨率航空影像中提取的表格特征，涵盖九个类别（如道路、树木、草地、水体）。该数据集呈现了典型的遥感挑战，包括高维性、异构特征和类别不平衡。在统一且可复现的流程中，我们将经典机器学习模型（如逻辑回归、支持向量机、随机森林、XGBoost、CatBoost）与表格深度学习（TDL）模型（TabNet、FT-Transformer、TabTransformer、TabSeq和1D CNN）进行基准测试。为解决类别不平衡问题，我们对TDL模型采用加权交叉熵损失，并使用准确率、宏平均精确率、宏平均召回率、宏平均F1分数、AUC-ROC和混淆矩阵评估性能。我们的结果表明，虽然树集成方法仍是强大的通用基线，但TDL模型在非线性交互显著且不平衡处理有效的情况下，能够匹配或超越其性能，为城市土地覆盖制图提供了互补优势。参见代码：此https URL
英文摘要
Urban Land Cover (ULC) classification plays a crucial role in urban planning, environmental monitoring, and sustainable development. We study this task using the ULC dataset from the UCI Machine Learning Repository, which includes tabular features derived from high-resolution aerial imagery across nine classes (e.g., roads, trees, grass, water). The dataset presents typical remote sensing challenges, including high dimensionality, heterogeneous features, and class imbalance. In a unified, reproducible pipeline, we benchmark classical machine learning models (e.g., Logistic Regression, SVM, Random Forest, XGBoost, CatBoost) against Tabular Deep Learning (TDL) models (TabNet, FT-Transformer, TabTransformer, TabSeq, and 1D CNNs). To address class imbalance, we employ weighted cross-entropy loss for TDL models and evaluate performance using accuracy, macro-precision, macro-recall, macro-F1, AUC-ROC, and confusion matrices. Our results show that while tree ensembles remain strong general baselines, TDL models can match or exceed their performance when non-linear interactions are significant and imbalance handling is effective, providing complementary advantages for urban land cover mapping. See code: https://github.com/mtesha/tdl-vs-ml-urbanlandcover
CommentsPublished in NeurIPS 2025 The 5th Muslims In ML (MusIML) Workshop
