公开印尼油棕种植园地理数据集，助力可持续监测与模型训练。
An Open Benchmark Dataset for GeoAI Foundation Models for Oil Palm Mapping in Indonesia
- 专家标注2020-2024年高分辨率卫星影像，生成全境多阶段油棕地图。
- 覆盖多种生态区，含种植阶段分类与相似多年生作物区分。
- 支持遥感模型训练，符合开放共享与可重复研究标准。
油棕种植是印度尼西亚森林砍伐的主要原因。为更好追踪并应对这一挑战，需提供详细可靠的制图以支持可持续性努力和新兴监管框架。本文发布一个公开获取的印度尼西亚油棕种植园及关联地表覆盖类型的地理空间数据集，基于2020至2024年高分辨率卫星影像的专家标注。该数据集采用墙到墙（wall-to-wall）多尺度矢量标注，涵盖多种农业生态区，包含油棕种植阶段的分层分类体系以及相似多年生作物的区分。质量通过多标注者共识与实地验证保障。数据集适用于训练和评估传统卷积神经网络及新型地理空间基础模型。在CC-BY许可下发布，填补了遥感领域训练数据的关键空白，旨在提升地表覆盖类型制图精度。通过支持油棕扩张的透明监测，该资源有助于实现全球减少森林砍伐的目标，并遵循FAIR数据原则。
原文摘要 · Abstract (English)
Oil palm cultivation remains one of the leading causes of deforestation in Indonesia. To better track and address this challenge, detailed and reliable mapping is needed to support sustainability efforts and emerging regulatory frameworks. We present an open-access geospatial dataset of oil palm plantations and related land cover types in Indonesia, produced through expert labeling of high-resolution satellite imagery from 2020 to 2024. The dataset provides polygon-based, wall-to-wall annotations across a range of agro-ecological zones and includes a hierarchical typology that distinguishes oil palm planting stages as well as similar perennial crops. Quality was ensured through multi-interpreter consensus and field validation. The dataset was created using wall-to-wall digitization over large grids, making it suitable for training and benchmarking both conventional convolutional neural networks and newer geospatial foundation models. Released under a CC-BY license, it fills a key gap in training data for remote sensing and aims to improve the accuracy of land cover types mapping. By supporting transparent monitoring of oil palm expansion, the resource contributes to global deforestation reduction goals and follows FAIR data principles.
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