While　hurricane　causes　great　damage　in　resident　buildings,　very　few　efficient　responses　can　be　delivered　to　rescuers.　Combining　satellite　image　and　Convolutional　Neural　Network　(CNN)　transfer　learning,　rescuers　can　locate　the　damaged　buildings　in　time.　Therefore,　it　is　crucial　to　determine　the　factors　of　transfer　learning　performance　in　this　case.　In　this　paper,　we　used　VGG16　as　our　base　model.　We　investigated　it　from　three　aspects:　1.　the　input　image　size,　2.　the　network　structure　(including　adding　filters　and　changing　top　dense　layers),　3.　the　classifier　activation　function.　The　results　show　the　size　of　the　input　images　influences　the　performance　the　most,　and　the　activation　function　of　the　classifier　has　the　smallest　effect.