Not Every Tree Is a Forest: Benchmarking Forest Types from Satellite Remote Sensing
This work addresses the need for accurate forest type mapping to support deforestation and biodiversity conservation efforts, such as the EUDR, by providing a global-scale benchmark with detailed classifications.
The authors tackled the problem of mapping forest types globally by introducing ForTy, a benchmark with 200,000 time series of multi-modal satellite data, and proposed a novel transformer-based model that outperformed baseline models in performance.
Developing accurate and reliable models for forest types mapping is critical to support efforts for halting deforestation and for biodiversity conservation (such as European Union Deforestation Regulation (EUDR)). This work introduces ForTy, a benchmark for global-scale FORest TYpes mapping using multi-temporal satellite data1. The benchmark comprises 200,000 time series of image patches, each consisting of Sentinel-2, Sentinel-1, climate, and elevation data. Each time series captures variations at monthly or seasonal cadence. Per-pixel annotations, including forest types and other land use classes, support image segmentation tasks. Unlike most existing land use products that often categorize all forest areas into a single class, our benchmark differentiates between three forest types classes: natural forest, planted forest, and tree crops. By leveraging multiple public data sources, we achieve global coverage with this benchmark. We evaluate the forest types dataset using several baseline models, including convolution neural networks and transformer-based models. Additionally, we propose a novel transformer-based model specifically designed to handle multi-modal, multi-temporal satellite data for forest types mapping. Our experimental results demonstrate that the proposed model surpasses the baseline models in performance.