计算机科学
杠杆(统计)
人工智能
足迹
卷积神经网络
任务(项目管理)
特征提取
可扩展性
机器学习
深度学习
代表(政治)
计算机视觉
数据挖掘
数据库
政治学
政治
古生物学
生物
经济
管理
法学
标识
DOI:10.1109/tpami.2017.2750680
摘要
Extracting buildings from aerial scene images is an important task with many applications. However, this task is highly difficult to automate due to extremely large variations of building appearances, and still heavily relies on manual work. To attack this problem, we design a deep convolutional network with a simple structure that integrates activation from multiple layers for pixel-wise prediction, and introduce the signed distance function of building boundaries to represent output, which has an enhanced representation power. To train the network, we leverage abundant building footprint data from geographic information systems (GIS) to generate large amounts of labeled data. The trained model achieves a superior performance on datasets that are significantly larger and more complex than those used in prior work, demonstrating that the proposed method provides a promising and scalable solution for automating this labor-intensive task.
科研通智能强力驱动
Strongly Powered by AbleSci AI