# Ai2 — A crop-mapping benchmark for the tropics

- Company: Ai2 (allenai.org)
- Announced: 2026-10-22
- Category: research-paper
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://allenai.org/blog/planteur
- Record: https://forck.live/items/26680-a-crop-mapping-benchmark-for-the-tropics
- Subject: Olmo / Molmo / Asta

Ai2 has released PLANTEUR, a crop-mapping benchmark that extends the PASTIS dataset from metropolitan France to six tropical regions, so teams building geospatial models can test them on smallholder, tropical, topographically complex farmland. PASTIS is widely trusted for its high-quality labels and geospatial metadata, but its coverage stopped at France, while crop-type mapping underpins food security planning, water allocation and tracking farmland change. PLANTEUR draws on France's Land Parcel Identification System and spans Réunion, Guadeloupe, Martinique, Guyane, Mayotte and Saint-Martin. Its format closely resembles PASTIS, with 2,750 aligned Sentinel-2, Sentinel-1 and Landsat-8 time series and 4,506 km² of parcel-derived labels across seven crop classes, including sugarcane, banana, pineapple and tropical tubers such as cassava, yam and taro. Ai2 reports baseline numbers for linear probes and supervised methods on a standard split, a leave-one-island-out split, a few-shot setup, and one that includes PASTIS samples in training. Every method evaluated, including Ai2's own, scores substantially lower on PLANTEUR than on PASTIS, and each model loses roughly half its accuracy on an island it never saw in training. PLANTEUR is available on Hugging Face and GitHub.

## Evidence

Verbatim from https://allenai.org/blog/planteur:

> Every method we evaluated, ours included, scores substantially lower on PLANTEUR than on PASTIS, and every model loses roughly half its accuracy when tested on an island it never saw in training.

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