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Smart city article details
| Title | A Two-Stage Cnn Based Satellite Image Analysis Framework For Estimating Building-Count In Residential Built-Up Area |
|---|---|
| ID_Doc | 5676 |
| Authors | Chatterjee S.; Saha S.; Mahapatra P.R.S. |
| Year | 2024 |
| Published | Lecture Notes in Networks and Systems, 998 LNNS |
| DOI | http://dx.doi.org/10.1007/978-981-97-3245-6_2 |
| Abstract | The assessment of population within a certain region holds significant importance in the field of resource allocation and urban planning. The central objective of our proposed research efforts is to aid in assessing the population of a region by estimating building counts through a two-stage framework that analyses low-resolution satellite images using deep learning models based on convolutional neural network (CNN). The first phase of our framework focuses on segmentation of the built-up areas in a satellite image using a Mask-RCNN model, while the second phase employs a regression model based on convolutional neural network (CNN) for estimating the number of buildings within each segmented built-up area without the need for individual extraction of roof-tops. The extraction of roof-tops from low-resolution satellite images for population estimation still poses a huge challenge to the researchers because of poor visual clarity. Further, in densely populated areas, the low contrast of the built-up areas causes huge difficulty in the detection of roof-tops individually. In view of such challenges, we develop a Mask-RCNN model for segmentation of probable built-up areas in low-resolution satellite images instead of attempting to extract every building individually. Subsequently, a CNN-based regression model is developed for estimation of the building-count within the segmented built-up areas in low-resolution satellite images. The proposed framework exhibited a promising level of accuracy while working with low-resolution satellite images. The experimental results with publicly available data validated the capability of the proposed framework, which provides a cost-effective solution for estimating the population in a region. Such an automated framework for population estimation is useful to assess demographic variation, resource allocation for disaster management, smart city construction, and many other socio-economic planning activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. |
| Author Keywords | Building-count estimation; Built-up area extraction; Convolutional neural networks (CNNs); Low-resolution satellite imagery; Population estimation; Urban planning |
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