The rapid growth of major cities across the counties demands accurate building extraction techniques. Unmanned Aerial Vehicles (UAV) help in obtaining terrain information that can be used to extract urban features. Recent advancement has led to the capture of aerial images of the earth surface in micro-detail using UAV. These aerial images enable us to perform classification, feature extraction, and height estimation at a finer scale. In this work, aerial images of the university campus were captured using a quadcopter drone equipped with high-resolution camera and satellite navigation system. Approximately 500 images were captured in the study area with necessary overlap and side lap. Captured images were subjected to aerial triangulation, dense image matching, and point cloud generation to produce Digital Surface Models (DSM) and orthophoto. Various machine learning algorithms—random forest (RF), support vector machine (SVM), naïve Bayes (NB) and artificial neural networks (ANN)—have been used to extract building rooftops from derivatives of UAV-captured imageries, and accuracies were compared. Algorithms were trained using both spectral and elevation information to extract building rooftops, and improvements shown due to the addition of elevation data in training the model are observed. The proposed method is aimed at improving building-level information extraction and providing accurate building information to aid authorities for better planning and management.

中文翻译：

---

使用人工神经网络从基于无人机的图像中自动提取建筑物

各县主要城市的快速发展需要准确的建筑提取技术。无人机 (UAV) 有助于获取可用于提取城市特征的地形信息。最近的进步导致使用无人机捕获地球表面的微细节航拍图像。这些航拍图像使我们能够在更精细的尺度上执行分类、特征提取和高度估计。在这项工作中，大学校园的航拍图像是使用配备高分辨率相机和卫星导航系统的四轴无人机拍摄的。在研究区域捕获了大约 500 张图像，并具有必要的重叠和侧边重叠。捕获的图像经过空中三角测量、密集图像匹配和点云生成，以生成数字表面模型 (DSM) 和正射影像。各种机器学习算法——随机森林 (RF)、支持向量机 (SVM)、朴素贝叶斯 (NB) 和人工神经网络 (ANN)——已被用于从无人机捕获的图像的衍生物中提取建筑物屋顶，并比较精度. 使用光谱和高程信息来训练算法以提取建筑物屋顶，并且观察到由于在训练模型中添加高程数据而显示的改进。所提出的方法旨在改进建筑级信息提取并提供准确的建筑信息以帮助当局更好地规划和管理。并比较了精度。使用光谱和高程信息来训练算法以提取建筑物屋顶，并且观察到由于在训练模型中添加高程数据而显示的改进。所提出的方法旨在改进建筑级信息提取并提供准确的建筑信息以帮助当局更好地规划和管理。并比较了精度。使用光谱和高程信息来训练算法以提取建筑物屋顶，并且观察到由于在训练模型中添加高程数据而显示的改进。所提出的方法旨在改进建筑级信息提取并提供准确的建筑信息以帮助当局更好地规划和管理。