URBAN GROWTH MONITORING – REMOTE SENSING METHODS FOR SUSTAINABLE DEVELOPMENT
Keywords: urban growth, CNN, classification, urban areas, sustainable development
Abstract. Urban areas account for a small fraction of the Earth's surface but have a disproportionate impact on its surroundings regarding mass, energy, and resources. An exponential increase in the urban population has been observed since the mid-20th century. As expected by the United Nations (UN), by the year 2050, 68.4% of the world population will live in cities with a population of 20,000 or more. Due to enormous socio-economic pressures resulting from population expansion, urbanization and intensive changes in the landscape, an urban development program to make cities and human settlements inclusive, safe, resilient, and sustainable has become of the utmost importance since the 2005 World Summit in Rio and further adopted in 2015 by UN as the "2030 Agenda" for Sustainable Development. The study focused on monitoring the SDG 11 target 11.3.1. they are defined as a ratio of land consumption rate to the population growth rate because mapping urban land quickly and accurately is indispensable for watershed run-off prediction and other planning applications. There is no well-established, consistent way to measure either urban land sprawl or population growth. However, remote sensing methods and satellite-derived data make it possible to monitor urban growth rates over large areas in a relatively short time. There are many techniques for urban land cover automatically mapping. These techniques can be broadly grouped into two general types: those based on the input data classification, including pixel- and object-based classifications and those based on directly segmenting the indices, such as the commonly used normalized difference vegetation index (NDVI), normalized build-up area index (NDBI), and their modifications.
The authors used classical pixel (supervised classification with Spectral Angle Mapper classification and KNN methods) and objectbased classification in the presented research. In addition, spectral indices, i.e., NDVI, NDBI and their modifications to derive buildup areas, were applied. Moreover, the authors focused on the recent deep learning and machine learning methods, i.e., the utilization of spatial-context information in multi-temporal data to learn hierarchical feature representations. All methods of detecting built-up areas were compared and assessed based on the available cartographic data.