Department of Real Estate, Kangwon National University, Chuncheon, Gangwon-do, Republic of Korea.
PLoS One. 2024 Sep 19;19(9):e0308788. doi: 10.1371/journal.pone.0308788. eCollection 2024.
In the agriculture and real estate industries, land lot shapes have mostly been classified by visual inspection or hard-crafted rules. These conventional methods are time-consuming, resource-intensive, and subject to human bias. This study aims to fill this gap and alleviate problems inherent in traditional lot classification approaches. This study attempts to classify lot shapes automatically, using a convolutional neural network. A study area was chosen, image data of the lots in the area were collected and preprocessed, and an Xception neural network was specified to classify land lots according to their shapes. The test applied to a different area adjacent to the study area achieved an accuracy of 90.1% and area under the curve (AUC) of 0.96. Additionally, this study demonstrated that shape regularity can be quantified using the output scores from the neural network analysis. This is the first attempt to employ a deep learning algorithm for land management on a micro-spatial scale. The classification approach proposed in this study is expected to encourage the rapid and accurate classification of various lot shapes.
在农业和房地产行业中，土地形状大多是通过视觉检查或手工规则来分类的。这些传统方法耗时、资源密集且容易受到人为偏见的影响。本研究旨在填补这一空白，并缓解传统土地分类方法所固有的问题。本研究尝试使用卷积神经网络自动对土地形状进行分类。选择了一个研究区域，收集并预处理了该区域的土地图像数据，并指定了 Xception 神经网络来根据土地形状对土地进行分类。应用于研究区域相邻的不同区域的测试取得了 90.1%的准确率和 0.96 的曲线下面积（AUC）。此外，本研究表明可以使用神经网络分析的输出分数来量化形状的规则性。这是首次尝试在微观空间尺度上使用深度学习算法进行土地管理。本研究提出的分类方法有望促进对各种土地形状的快速准确分类。
