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              <text>A Comprehensive Review of Small Building Detection in Collapsed Images: Advancements and Applications of Machine Learning Algorithms</text>
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              <text>Accurately identifying small buildings in images of collapses is essential for disaster assessment and urban planning. In the context of collapsed images, this study provides an extensive overview of the methods and approaches used for small building detection. The investigation covers developments in machine learning algorithms, their uses, and the consequences for urban development and disaster management. This work attempts to give a brief grasp of the difficulties, approaches, and potential paths in the field of small building detection from collapsed imaging through a thorough investigation of the body of existing literature.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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              <text>Sajitha I.; Sambandam R.K.; John S.P.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-1121 LNNS, pp. 429-438.</text>
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              <text>ISSN: 23673370; ISBN: 978-981977422-7</text>
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              <text>Sajitha I., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; John S.P., Department of Computer Science and Engineering, Jyothi Engineering College, Cheruthuruthy, Kerala, Thrissur, India</text>
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