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Automatic extraction of urban land information from unmanned aerial vehicle (UAV) data

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Abstract

A high-resolution dataset, such as an unmanned aerial vehicle (UAV) data provides new insight of information extraction for remote sensing applications. Object-based image analysis (OBIA) is emerging as an effective tool in the field of aerial image processing and remote sensing applications. The study primarily demonstrates how UAV data can be utilized for the extraction of urban land spatial information and aims explicitly at the extraction of vacant urban parcels within city premises. The study is initiated with object-based urban feature extraction using Multiresolution segmentation (MRS). Further, classification is performed by defining a set of rules to extract vacant urban parcel spatial information. Digital elevation and normalized surface models (DEM and n-DSM) are utilized for refining the segmentation results. The attribution and reclassification of objects are performed based on DEM and n-DSM values. Moreover, the challenges for removing the obligations in delineating the vacant parcel boundaries are addressed by utilizing the excess vegetation index (EVI). The applicability of the approach is examined by three accuracy indexes, which are completeness, correctness, and quality. Overall high accuracy is obtained for extracted urban land parcels in terms of accuracy indexes. The proposed algorithm can be effectively utilized for numerous applications such as building floor extraction, gathering information for vacant urban parcels within city premises, delineation of building footprints, damaged building estimations, and many more.

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Authors and Affiliations

  1. Civil Engineering Department, Indian Institute of Technology, Roorkee, India

    Anugya Shukla & Kamal Jain

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  1. Anugya Shukla
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  2. Kamal Jain
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Correspondence to Anugya Shukla.

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Communicated by: H. Babaie

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Shukla, A., Jain, K. Automatic extraction of urban land information from unmanned aerial vehicle (UAV) data. Earth Sci Inform 13, 1225–1236 (2020). https://doi.org/10.1007/s12145-020-00498-x

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