mlsanityData to 7 Oct 2026

Tree crop mapping of South America reveals links to deforestation and conservation

arXiv 2602.17372 ↗First posted 19 Feb 2026cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Monitoring tree crop expansion is vital for zero-deforestation policies like the European Union's Regulation on Deforestation-free Products (EUDR). However, these efforts are hindered by a lack of highresolution data distinguishing diverse agricultural systems from forests. Here, we present the first 10m-resolution tree crop map for South America, generated using a multi-modal, spatio-temporal deep learning model trained on Sentinel-1 and Sentinel-2 satellite imagery time series. The map identifies approximately 11 million hectares of tree crops, 23% of which is linked to 2000-2020 forest cover loss. Critically, our analysis reveals that existing regulatory maps supporting the EUDR often classify established agriculture, particularly smallholder agroforestry, as "forest". This discrepancy risks false deforestation alerts and unfair penalties for small-scale farmers. Our work mitigates this risk by providing a high-resolution baseline, supporting conservation policies that are effective, inclusive, and equitable.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 57 references that are in the map. First posted on arXiv 19 Feb 2026.

Builds on

5 of 57 references.

References · 5

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  3. 2021Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networkscited by 141
  4. 2023ViTs for SITS: Vision Transformers for Satellite Image Time Seriescited by 55
  5. 2024Planted: a dataset for planted forest identification from multi-satellite time seriescited by 9

Built on by

Not cited yet.