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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >A METHOD FOR ESTIMATING THE NUMBER OF HOUSEHOLDS IN A REGION FROM THE NUMBER OF BUILDINGS ESTIMATED BY DEEP LEARNING WITH THE ADJUSTMENT OF ITS NUMBER USING ANCILLARY DATASETS: CASE STUDY IN DJAKARTA
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A METHOD FOR ESTIMATING THE NUMBER OF HOUSEHOLDS IN A REGION FROM THE NUMBER OF BUILDINGS ESTIMATED BY DEEP LEARNING WITH THE ADJUSTMENT OF ITS NUMBER USING ANCILLARY DATASETS: CASE STUDY IN DJAKARTA

机译：通过深入学习估计的建筑物数量估算一个地区的家庭数量的方法，通过辅助数据集调整其数量：在Djakarta的案例研究

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摘要

The high resolution statistical data such as the number of households in small areas are indispensable for urban planning, disaster prevention and many kinds of business activities. However, it is difficult to obtain the number of households in small areas because census data are usually aggregated in municipal districts. Techniques for automatically analyzing statistical data, e.g., land cover, population density, and the number of households obtained from satellite/aerial images have been continuously studied. In recent years, many methods using deep learning have been proposed in the related literature. In estimating the number of households, the use of buildings, the number of floors and that of rooms are also important information, but it is difficult to obtain such information from only image analysis using deep learning. This study proposes a method for estimating the number of households in 100 meter grid cells from satellite images using deep learning, and adjusting it using ancillary data obtained from a few statistical datasets. The application of this method to Djakarta shows that the difference between the estimated values and the corresponding values of census is less than 10%.
机译：高分辨率统计数据，如小区的家庭数目是城市规划，防灾和多种商业活动中不可或缺的。但是，难以获得小区域中的家庭数量，因为人口普查数据通常在市区汇总。已经不断研究自动分析统计数据的技术，例如陆地覆盖，人口密度和从卫星/航拍图像获得的家庭数量。近年来，在相关文献中提出了许多使用深度学习的方法。在估计家庭的数量时，建筑物的使用，楼层数量和房间的数量也是重要信息，但很难使用深度学习的图像分析获得这些信息。本研究提出了一种使用深度学习从卫星图像估算100米网格单元中的家庭数量的方法，并使用从几个统计数据集获得的辅助数据来调整它。这种方法在Djakarta的应用表明，估计值与人口普查的相应值之间的差异小于10％。

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