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Forests are an essential part of Earth's ecosystems and natural systems, aswell as providing services on which humanity depends, yet they are rapidlychanging as a result of land use decisions and climate change. Understandingand mitigating negative effects requires parsing data on forests at globalscale from a broad array of sensory modalities, and recently many such problemshave been approached using machine learning algorithms for remote sensing. Todate, forest-monitoring problems have largely been approached in isolation.Inspired by the rise of foundation models for computer vision and remotesensing, we here present the first unified Forest Monitoring Benchmark(FoMo-Bench). FoMo-Bench consists of 15 diverse datasets encompassingsatellite, aerial, and inventory data, covering a variety of geographicalregions, and including multispectral, red-green-blue, synthetic aperture radar(SAR) and LiDAR data with various temporal, spatial and spectral resolutions.FoMo-Bench includes multiple types of forest-monitoring tasks, spanningclassification, segmentation, and object detection. To further enhance thediversity of tasks and geographies represented in FoMo-Bench, we introduce anovel global dataset, TalloS, combining satellite imagery with ground-basedannotations for tree species classification, spanning 1,000+ hierarchicaltaxonomic levels (species, genus, family). Finally, we propose FoMo-Net, afoundation model baseline designed for forest monitoring with the flexibilityto process any combination of commonly used sensors in remote sensing. Thiswork aims to inspire research collaborations between machine learning andforest biology researchers in exploring scalable multi-modal and multi-taskmodels for forest monitoring. All code and data will be made publiclyavailable.

FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models