The paper introduces an original method for effective spatial data processing, particularly important for land administration and real estate governance. This approach integrates Unmanned Aerial Vehicle (UAV) data acquisition and processing with Artificial Intelligence (AI) and Geometric Transformation algorithms. The results reveal that: (1) while the separate applications of YOLO and Hough Transform algorithms achieve building detection rates up to 77% and 83%, respectively, (2) a novel methodology is proposed to combine spatial data and assess their quality of the detected buildings by comparing the generated building polygons with existing cadastral maps. The evaluation uses a polygon-based comparison approach, which computes metrics such as Precision, Recall, F1-Score, and Accuracy based on the spatial relationships between predicted and reference building contours, (3) the weighted model showed about 7 % improvement in accuracy compared to cadastral data. This innovative approach substantially improves spatial data processing, aiding in implementing principles for real estate good governance and offering a valuable asset for various land administration applications.

中文翻译：

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无人机测量和 AI 驱动算法融合，为房地产良好治理原则提供支持


本文介绍了一种有效的空间数据处理的原始方法，该方法对土地管理和房地产治理尤为重要。这种方法将无人机 （UAV） 数据采集和处理与人工智能 （AI） 和几何变换算法集成在一起。结果表明：（1） 虽然 YOLO 和 Hough Transform 算法的单独应用分别实现了高达 77% 和 83% 的建筑物检测率，但 （2） 提出了一种新的方法，通过将生成的建筑物多边形与现有的地籍图进行比较来组合空间数据并评估其检测到的建筑物的质量。该评估使用基于多边形的比较方法，该方法根据预测和参考建筑等值线之间的空间关系计算精度、召回率、F1 分数和准确性等指标，（3） 与地籍数据相比，加权模型的准确性提高了约 7%。这种创新方法大大改善了空间数据处理，有助于实施房地产良好治理原则，并为各种土地管理应用程序提供了宝贵的资产。