图 书 馆
使用卷积网络和卫星图像识别大规模城市环境中的模式Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale |
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| 课程网址: | http://videolectures.net/kdd2017_albert_convolutional_networks/ |
| 主讲教师: | Adrian Albert |
| 开课单位: | 麻省理工学院 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as higher-level concepts such as land use classes (which encode expert understanding of socio-economic end uses). This kind of data is expensive and labor-intensive to obtain, which limits its availability (particularly in developing countries). We analyze patterns in land use in urban neighborhoods using large-scale satellite imagery data (which is available worldwide from third-party providers) and state-of-the-art computer vision techniques based on deep convolutional neural networks. For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of 20 land use classes across 300 European cities. We use this data to train and compare deep architectures which have recently shown good performance on standard computer vision tasks (image classification and segmentation), including on geospatial data. Furthermore, we show that the deep representations extracted from satellite imagery of urban environments can be used to compare neighborhoods across several cities. We make our dataset available for other machine learning researchers to use for remote-sensing applications. |
| 关 键 词: | 城市规划; 卫星图像; 计算机视觉 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-12-25：wujk |
| 最后编审: | 2023-12-25：wujk |
| 阅读次数: | 13 |