城市结构解码:通过深度学习和遥感来高精度识别建筑材料
Kun Sun1, Qiaoxuan Li2, Qiance Liu1,3
1SDU Life Cycle Engineering, Department of Green Technology, University of Southern Denmark, Odense, 5230, Denmark.
Environmental science and ecotechnology
|March 4, 2025
概括
我们开发了一个使用人工智能和街景图像的自动化系统,以精确识别建筑材料,以更好地减少碳排放和改造战略. 这种可扩展的方法有助于可持续的城市规划和提高能源效率.
科学领域:
- 城市规划与可持续发展
- 环境科学中的人工智能
- 遥感和地理空间分析
背景情况:
- 准确的建筑材料数据对于实现碳减排,改造和城市循环性至关重要.
- 当前的材料数据库往往是特定于项目或地理限制,只提供近似数据.
- 不足够的记录和成本限制阻碍了获得大规模,精确的物质信息.
研究的目的:
- 通过远程传感和谷歌街景图像来识别建筑材料的新型自动化框架.
- 为了证明框架在多样化的丹麦城市景观中的可扩展性和适应性.
- 为可持续城市发展提供高分辨率的物质分配洞察力.
主要方法:
- 使用深度学习模型训练在传感技术和谷歌街景数据.
- 最初的模型培训和验证是使用Odense.se的综合数据集进行的.
- 该框架扩展到哥本哈根,奥胡斯和阿尔堡的建筑材料特征.
主要成果:
- 通过高分辨率在多个丹麦城市成功识别了屋顶和立面材料.
- 证明了该模型的可扩展性和适应性,以适应不同的地理环境和建筑风格.
- 提供了关于建筑材料在不同城市环境中的分布的详细见解.
结论:
- 自动化框架为精确的建筑材料识别提供了一个可扩展和适应的解决方案.
- 这些发现对于提供可持续城市规划和修订建筑规范以减少排放至关重要.
- 通过利用这些高分辨率材料分布见解,可以实现优化改造工作.
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