Multi-task learning based building damage assessment method
Building damage assessment plays an important role in the disaster relief process,influencing the formulation of rescue strategies and optimization of resource allocation.Currently,damage assessment methods based on semantic segmentation face challenges in extracting fine-grained semantic information for damaged buildings.Thus,a multi-task learning based approach for building damage assessment is proposed,dividing the damage assessment into two subtasks as coarse-grained building area extraction and fine-grained damage segmentation.The proposed method utilizes a shared encoder-decoder and context fusion module to achieve coarse-grained extraction of building areas and fine-grained segmentation of building damage.The results of these two tasks are fused using the Hadamard product to obtain the final assessment.Experimental results demonstrate that the proposed multi-task learning based building damage assessment method performs well.
building damage assessmentdeep learningmulti-task learning
NETL
NSTL
万方数据
