Abstract
Building foot prints and building count information in urban areas are very much essential for planning and monitoring developmental activities, efficient natural resource utilization, and provision of civic facilities by governments. Remote sensing data such as satellite/aerial imagery in association with digital elevation model is widely used for automatic extraction of building information. Many researchers have developed different methods for maximizing the detection percentage with minimum errors. A comparative study of different methods available in the literature is presented in this paper by analyzing the primary data sets, derived data sets, and their usage in the automated and semiautomated extraction methods. It is found that the success of the method for automatic building detection in urban areas primarily depends on using combination of high-resolution image data with digital elevation model.
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Pendyala, V.S.S.N.G.K., Kalluri, H.K., Venkataraman, V.R., Rao, C.V. (2019). Comparative Study of Automatic Urban Building Extraction Methods from Remote Sensing Data. In: Bapi, R., Rao, K., Prasad, M. (eds) First International Conference on Artificial Intelligence and Cognitive Computing . Advances in Intelligent Systems and Computing, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-13-1580-0_58
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