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

arXiv: https://arxiv.org/abs/2505.01805 · HTML: https://mlsanity.com/paper/2505.01805/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 3 May 2025.

## Builds on (most cited first, 7 of 7)

- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders](https://mlsanity.com/paper/2311.00566/) (2023)
- [Foundation Models for Generalist Geospatial Artificial Intelligence](https://mlsanity.com/paper/2310.18660/) (2023)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [ViTs for SITS: Vision Transformers for Satellite Image Time Series](https://mlsanity.com/paper/2301.04944/) (2023)
- [Planted: a dataset for planted forest identification from multi-satellite time series](https://mlsanity.com/paper/2406.18554/) (2024)
- [Satellite-based high-resolution maps of cocoa planted area for Cote d'Ivoire and Ghana](https://mlsanity.com/paper/2206.06119/) (2022)

## Built on by (most cited first, 3 of 3)

- [Uncertainty evaluation of segmentation models for Earth observation](https://mlsanity.com/paper/2510.19586/) (2025)
- [Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance](https://mlsanity.com/paper/2604.21104/) (2026)
- [Reconstructing Multi-Decadal Forest Disturbances: A Spatio-Temporal Transformer Approach](https://mlsanity.com/paper/2606.07249/) (2026)

## Abstract (arXiv)

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.