UAV measurements and AI-driven algorithms fusion for real estate good governance principles support

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引用次数: 0

Abstract

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.
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无人机测量与人工智能驱动的算法融合,为房地产良好治理原则提供支持
本文介绍了一种有效处理空间数据的独创方法,这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)和几何变换算法相结合。研究结果表明(1) 虽然单独应用 YOLO 和 Hough 变换算法的建筑物检测率分别高达 77% 和 83%,(2) 但我们提出了一种新方法来结合空间数据,并通过比较生成的建筑物多边形和现有地籍图来评估所检测建筑物的质量。评估采用基于多边形的比较方法,根据预测建筑轮廓与参考建筑轮廓之间的空间关系计算精确度、召回率、F1-分数和准确度等指标,(3) 与地籍数据相比,加权模型的准确度提高了约 7%。这种创新方法大大改进了空间数据处理,有助于落实房地产良好治理的原则,并为各种土地管理应用提供了宝贵的资产。
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来源期刊
International journal of applied earth observation and geoinformation : ITC journal
International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences
CiteScore
12.00
自引率
0.00%
发文量
0
审稿时长
77 days
期刊介绍: The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.
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