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 [3].

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 [11].

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.

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The objective of using quickbird image in topographical map updating introduction

The objective of using quickbird image in topographical map updating

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