构建全球森林类型遥感基准数据集，区分天然林、人工林和果树林。
Not Every Tree Is a Forest: Benchmarking Forest Types from Satellite Remote Sensing
- 融合多时相哨兵卫星与气候地形数据，构建20万条遥感时序样本。
- 提出新型多模态时序Transformer模型，在森林类型识别上超越现有方法。
- 适合关注林业监测、碳汇评估与欧盟禁伐法规落地的研究者。
构建准确可靠的森林类型制图模型对遏制毁林和生物多样性保护（如欧盟禁伐法规）至关重要。本文提出ForTy，一个基于多时相卫星数据的全球尺度森林类型基准数据集。该数据集包含20万条图像块的时间序列，每条包含哨兵-2、哨兵-1、气候与高程数据，时间分辨率达月或季节级。每个像素标注了森林类型及其他土地利用类别，支持图像分割任务。与多数将所有林地归为一类的产品不同，本基准区分三类森林：天然林、人工林和树种作物。通过整合多个公开数据源，实现全球覆盖。我们用多种基线模型（包括卷积神经网络与基于Transformer的模型）评估该数据集，并提出一种专为处理多模态、多时序卫星数据设计的新式Transformer模型。实验结果表明，所提模型性能优于基线模型。
原文摘要 · Abstract (English)
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.
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