E3S Web Conf.
Volume 227, 2021Annual International Scientific Conference on Geoinformatics – GI 2021: “Supporting sustainable development by GIST”
|Number of page(s)||10|
|Section||GIS for Regional Sustainable Development|
|Published online||06 January 2021|
Object-based approach for urban land cover mapping using high spatial resolution data
OU Alba Regia Technical Faculty, Pirosalma str. 1-3, 8000 Székesfehérvár, Hungary
2 UP Faculty of Sciences Institute of Geography, Ifjúság str. 6. 7624 Pécs, Hungary
3 Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (TIIAME), Koriy Niyaziy str., 39, 100000, Tashkent, Uzbekistan.
* Corresponding author: email@example.com
This paper deals with object-oriented image analysis applied for an urban area. Very high-resolution images in conjunction with object-oriented image analysis have been used for land cover detection. Using the eCognition software with object-oriented methods, not only the spectral information but also the shape, compactness and other parameters can be used to extract meaningful objects. The spectral and geometric diversity of urban surfaces is a very complex research issue. It is the main reason why additional information is needed to improve the outcome of classification. The most consistent and relevant characteristic of buildings is their height. Therefore, elevation data (converted from LIDAR data) are used for building extraction, segmentation and classification. The study deals with the problem, how to determine the most appropriate parameters of segmentation, feature extraction and classification methods. The data extraction includes two phases, the first part consists the following steps: data pre-processing, rule set development, multi-scale image segmentation, the definition of features used to map land use, classification based on rule set and accuracy evaluation. The second part of the data process based on classical raster analysis GIS tools like focal and zonal function.
Key words: OBIA / GIS / remote sensing / land cover / urban environment / zonal statistics
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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