Au­to­mat­ed dam­age anal­y­sis

Automated damage analysis
Au­to­mat­ed dam­age anal­y­sis
Credit: xView2 data, processed by DLR

Automated damage analysis

Af­ter a nat­u­ral dis­as­ter it is im­por­tant to record the dam­age quick­ly. In DLR's 'Da­ta4Hu­man' project, the dam­age to build­ings is to be record­ed au­to­mat­i­cal­ly – us­ing in­tel­li­gent meth­ods to pro­cess da­ta ac­quired us­ing re­mote sens­ing. The ex­am­ple here shows dam­age anal­y­ses for sev­er­al nat­u­ral dis­as­ters in the USA – be­fore and af­ter Hur­ri­canes Flo­rence and Matthew, a flood dis­as­ter in the Mid­west and the bush fire in San­ta Rosa. DLR ex­perts used the pub­lic xView2 da­ta set for the anal­y­sis.

After a natural disaster it is important to record the damage quickly. In DLR's 'Data4Human' project, the damage to buildings is to be recorded automatically – using intelligent methods to process data acquired using remote sensing. The example here shows damage analyses for several natural disasters in the USA – before and after Hurricanes Florence and Matthew, a flood disaster in the Midwest and the bush fire in Santa Rosa. DLR experts used the public xView2 data set for the analysis.

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