PlantationBench: A Multiscale, Multimodal Remote Sensing Benchmark for Plantation Mapping Under Distribution Shift

Angela Tsao, David B. Lobell; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 2965-2975

Satellite-based land cover maps fail to distinguish between natural forests and planted trees when certifying agricultural plantations as deforestation-free, a critical need for smallholder farmers facing emerging legislation around sustainable supply chains (such as the European Union Deforestation Regulation). Previous efforts to map tree plantations exclude smallholder farms, which require intra-field detail about individual tree crowns that can only be observed in costly, very-high resolution (VHR, <=1m) satellite imagery, and limit scalability. To provide a replicable benchmark for mapping tree crop plantations, we introduce a novel earth observations dataset of plantation and forest locations across the African continent. We compile for these points a unique feature set combining a VHR satellite-derived canopy height product and open-source multispectral and synthetic aperture radar data, to fuse high spatial resolution with multitemporal, multiscale, and multimodal features. Our dataset has continental coverage for Africa's plantation-growing nations across a diverse range of climate zones, with more delineation for species and geographical region than any other related tree dataset. Samples are structured with different domains to account for real-world distribution shifts to study the interplay of domain generalization with various feature attributes of remotely sensed data. We test how pre-trained models can perform on the task of tree plantation mapping, and benchmark performance at scale and across different distributions.