Land cover mapping via crowdsourced multi-directional views: The more directional views, the better

Abstract

In the last decades, a number of crowdsourced land cover datasets have been developed, owning to their great potential to provide human-centric ground observations. In this study, we investigated the GLOBE Observer Land Cover program by assessing the efficacy of its multi-directional data-collecting protocol. Specifically, we explored data characteristics by presenting its unique data sampling protocol, data sample distributions, and similarity across multi-directional views. We developed an end-to-end classification framework that links user-uploaded multi-directional views with their user-provided land cover labels and investigated classification performance with different levels of viewing involvement, using various popular deep learning architectures, under different image fusion strategies. Our study provides empirical evidence that multi-directional views benefit land cover classification. We observe that classification performance improved across four selected deep learning architectures when more directional views were involved. The classification scenario with EfficentNet, the involvement of quadruple views, and the late fusion strategy led to an improvement of 0.084 in the weighted F1 score (from 0.628 to 0.712) compared to the one with single view. We encourage crowdsourced observing and monitoring programs to adopt multi-directional view sampling protocols and call for the development of robust information on fusion strategies that harness the potential of multi-directional views.