用卫星影像生成2020-2024年马印油棕高分辨率地图，无需人工标注。
From Noisy Historical Maps to Time-Series Oil Palm Mapping Without Annotation in Malaysia and Indonesia (2020-2024)
- 基于U-Net与DMI机制，从低分辨率历史标签推演10米级油棕分布。
- 2020、2022、2024年准确率分别为70.64%、63.53%、60.06%。
- 揭示2022年油棕面积达峰值后下降，且向湿地扩张趋势加剧。
精准监测油棕种植园对平衡东南亚经济发展与生态保护至关重要。现有种植园地图常因空间分辨率低、时间覆盖不足，难以有效追踪快速土地利用变化。本研究提出一种深度学习框架，利用哨兵-2遥感影像，无须新标注即可生成2020至2024年马来西亚与印度尼西亚10米分辨率的油棕种植园地图。针对粗粒度100米历史标签与10米影像间的分辨率不匹配问题，采用优化的确定性互信息（DMI）U-Net架构，有效缓解标签噪声影响。通过2,058个手动验证点验证，2020、2022、2024年整体准确率分别为70.64%、63.53%、60.06%。综合分析显示，该区域油棕覆盖率于2022年达峰值后在2024年回落；土地覆被转变分析表明，尽管与其他作物轮作趋于稳定，但油棕仍持续扩张至洪泛植被区。高分辨率地图为评估可持续承诺与森林砍伐动态提供关键数据，生成数据集已公开发布于https://doi.org/10.5281/zenodo.17768444。
原文摘要 · Abstract (English)
Accurate monitoring of oil palm plantations is critical for balancing economic development with environmental conservation in Southeast Asia. However, existing plantation maps often suffer from low spatial resolution and a lack of recent temporal coverage, impeding effective surveillance of rapid land-use changes. In this study, we propose a deep learning framework to generate 10-meter resolution oil palm plantation maps for Indonesia and Malaysia from 2020 to 2024, utilizing Sentinel-2 imagery without requiring new manual annotations. To address the resolution mismatch between coarse 100-meter historical labels and 10-meter imagery, we employ a U-Net architecture optimized with Determinant-based Mutual Information (DMI). This approach effectively mitigates the influence of label noise. We validated our method against 2,058 manually verified points, achieving overall accuracies of 70.64%, 63.53%, and 60.06% for the years 2020, 2022, and 2024, respectively. Our comprehensive analysis reveals that oil palm coverage in the region peaked in 2022 before experiencing a decline in 2024. Furthermore, land cover transition analysis highlights a concerning trajectory of plantation expansion into flooded vegetation areas, despite a general stabilization in rotations with other crop types. These high-resolution maps provide essential data for monitoring sustainability commitments and deforestation dynamics in the region, and the generated datasets are made publicly available at https://doi.org/10.5281/zenodo.17768444.
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