Bio-geo-referenced maps were built using a geographical software package (ArcGIS 10.1, ESRI, Redlands, CA, USA), as well as satellite pictures collected from a public source (http://landsatlook.usgs.gov/, accessed 02 July 2019) and shapefiles created by the Tanzanian National Bureau of Stat...
Landsat images were selected as the data source on the Google Earth Engine (GEE) platform, and samples were used as input data for land-use classification. The spectral, texture, terrain, and canopy features were extracted as indicators for random forest-based land-use classification. Such ...
Mortoja and Yigitcanlar (2022) used night-time light data and Landsat images to explore urban growth patterns over 30 years in a comparative case study between the Dhaka Metropolitan Development area and large urban centres in Australia [111]. For this, they used the Land Change Modeller (LCM...
The maps were constructed by correlating the information from the observation sheets with remote sensing and GIS techniques to identify the distribution of different land uses (residential, commercial, green areas, etc.). The mapping was performed during the second half of 2021, using Landsat ...