The objective of using quickbird image in topographical map updating
Landslides are one of the most destructive natural hazards, and they often cause substantial damage to societies worldwide every year [1,2].The intensity of landslides results in more substantial injuries and loss of life than any other type of natural disaster, including earthquakes, hurricanes, tsunamis, and floods .
For a successful qualitative or quantitative landslide hazard evaluation, compiling a historical landslide-event inventory is particularly crucial for pre-disaster and post-disaster analysis [9,10].
Thus far, landslide inventory maps have largely been generated through visual interpretation of aerial photos or satellite images combined with extensive field surveys.
However, such methods are labor-intensive and expensive and, therefore, inefficient for generating maps of large areas.
Moreover, traditional map-generating techniques require prior knowledge about the involved hazard, and such techniques are highly subjective and have limited reproducibility .
By contrast, a semi-automated or automated classification approach can provide a scheme for addressing the aforementioned problems.
The GA-driven feature optimization procedure offers several feature combinations for subsequent landslide detection.