The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 673–678, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-673-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 673–678, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-673-2020

  25 Aug 2020

25 Aug 2020

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

R. Ito1, K. Hara1, Y. Shimazaki1, N. Mori1, Y. Kani1, and A. Okabe2 R. Ito et al.
  • 1PASCO CORPOLATION, Tokyo, Japan
  • 2Aoyama Gakuin University, Tokyo, Japan

Keywords: Deep learning, Convolutional neural network, Household estimation, Urban classification

Abstract. 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%.