发表机构
Jahangirnagar University; Florida State University; The University of Oklahoma; Urban Development Directorate（贾汉吉尔纳加尔大学; 佛罗里达州立大学; 俄克拉荷马大学; 城市发展局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对深度学习提取建筑轮廓存在的拓扑不一致问题，提出多域GeoAI质量控制框架，采用DT分类器实现错误检测，可有效净化GIS矢量数据库，残留错误大幅降低。
AI 中文摘要
从高分辨率影像中基于深度学习提取建筑轮廓，常产生拓扑不一致的矢量数据，无法直接用于地理信息系统（GIS）数据库入库。为解决该问题，本文提出一种多域地理人工智能（GeoAI）质量控制框架，可自动检测误差以系统性净化矢量轮廓数据库。在孟加拉国的5个无人机（UAV）勘测站点，使用U-Net（ResNet-34）和SAM-LoRA（ViT-B）生成候选轮廓；提取的栅格掩码经矢量化、几何正则化，并在空间排他性约束下合并以消除重复表示。本文使用24个预测因子，涵盖几何、空间上下文及栅格衍生的光谱与纹理属性；机器学习（ML）分类器在开发分区（B至D站点）上训练，并在空间独立的测试集（E站点，超参数调参与类别平衡均未使用该站点）上严格验证。实验结果显示，使用决策树（DT）的几何与空间上下文预测因子，为识别对象级边界变形提供了最有效的判别依据；在未见过的测试站点上，DT的准确率达95.31%、F1分数为91.06%、马修斯相关系数（MCC）为0.880。在数据库层面，该框架成功识别87.34%的错误轮廓，同时保留98.31%的合格结构，将残留错误比例从27.32%降至4.62%，使最终数据库纯度提升至95.38%，对应相对错误降低83.09%。研究结果表明，后分割对象级机器学习为可投入使用的地理信息系统（GIS）工作流提供了高度可迁移、稳健的自动化质量保障机制。
英文摘要
Deep learning-based building footprint extraction from high-resolution imagery often produces topologically inconsistent vectors unfit for direct GIS database ingestion. To address this, we present a multidomain GeoAI quality control framework that automates error detection to systematically purify vector footprint databases. Candidate footprints were generated across five UAV survey sites in Bangladesh using U-Net (ResNet-34) and SAM-LoRA (ViT-B). The extracted raster masks were vectorized, geometrically regularized, and consolidated under a spatial-exclusivity constraint to eliminate duplicate representations. We used twenty-four predictors capturing geometric, spatial-contextual, and raster-derived spectral and texture properties. Machine Learning (ML) classifiers were trained on a development partition (Sites B-D) and rigorously validated on a spatially independent test set (Site E) excluded from hyperparameter tuning and class balancing. The experimental results demonstrate that geometric and spatial-contextual predictors using Decision Tree (DT) provide the most effective discriminatory evidence for identifying object-level boundary deformations. DT achieved an accuracy of 95.31%, an F1-score of 91.06%, and a Matthews correlation coefficient (MCC) of 0.880 on the unseen testing site. At the database level, this framework successfully identified 87.34% of erroneous footprints while maintaining 98.31% of acceptable structures, reducing the residual error proportion from 27.32% to 4.62% and improving final database purity to 95.38%. This translates into a relative error reduction of 83.09%. The findings indicate that post-segmentation object-level ML provides a highly transferable, robust mechanism for automated quality assurance in production-ready geographic information system (GIS) workflows.
Comments19 pages, 10 figures, 8 tables
