用卫星影像和主动学习，实现几内亚比绍全国级腰果园自动测绘。
A Remote Approach to Cashew Orchard Detection: Leveraging Active Learning with Satellite Imagery in Guinea-Bissau
- 基于哨兵-2影像与主动学习构建高效训练集
- 94.0%平衡准确率，完全离线完成全国制图
- 数据集与地图开源，适合环境监测与政策制定者
腰果生产是几内亚比绍及西非其他国家的重要经济活动，但无序种植可能导致区域森林砍伐、生物多样性下降和经济结构脆弱。目前缺乏全国范围的腰果园名录与地理定位信息，亟需遥感测绘。本文利用哨兵-2卫星影像与机器学习技术，开发了一种可扩展且低成本的远程检测方法，将区域性分析拓展至全国尺度。采用基于边缘的主动学习策略，优化训练样本数量与信息量，最终实现94.0%平衡准确率的腰果园分布图，全程无需现场采集。研究构建了两个数据集，并发布2021年10米分辨率全国腰果园地图，均开源共享于GitHub。结果表明该方法具备推广潜力，为环境管理提供新工具。
原文摘要 · Abstract (English)
Cashew production is a widespread economic activity in Guinea-Bissau, as well as other countries in West Africa. However, unregulated cashew production can be directly associated with increasing regionwide deforestation rates, biodiversity losses, and a fragile economic structure. There is no nationwide database for listing or georeferencing cashew orchards, so there is a clear need to remotely map their locations. In recent years, multiple methods for detecting orchards have been developed, though they have only been applied on a regional level. This work expands regional analyses to a nationwide scale. It develops a scalable and cost-effective remote approach, based on Sentinel-2 satellite imagery, using Machine Learning techniques to detect cashew orchards automatically. Margin-based Active Learning techniques were employed to develop an optimal training set in terms of the number of points and their informativeness, leading to a cashew map with 94.0% balanced accuracy obtained entirely off-site. We created two datasets and a 2021 cashew map with 10m spatial resolution that are openly accessible through GitHub. The results demonstrate the possibility of a broader cashew orchard mapping, creating a new stepping stone for this environmental application.
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