Not Every Tree Is a Forest: Benchmarking Forest Types from Satellite Remote Sensing

TL;DR

This paper introduces ForTy, a comprehensive benchmark dataset for classifying forest types globally using multi-temporal satellite data, and proposes a novel transformer-based model that outperforms baselines.

Contribution

The paper presents ForTy, a new large-scale benchmark dataset for forest type mapping, and introduces a specialized transformer model for multi-modal satellite data analysis.

Findings

The transformer-based model outperforms baseline models in accuracy.

ForTy provides global coverage with detailed forest type annotations.

Multi-temporal satellite data enhances forest classification performance.

Abstract

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.