用遥感嵌入模型提升塞内加尔花生带作物分类精度
Embedding -based Crop Type Classification in the Groundnut Basin of Senegal
- 基于TESSERA和AlphaEarth的嵌入方法评估作物分类性能
- TESSERA方法在时序迁移中准确率高出28%
- 适合小农户地区且可推广的遥感分析方案
卫星遥感获取的作物类型图谱对全球小农户地区的粮食安全、生计支持和气候变化缓解至关重要，但现有卫星方法多不适用于小农户条件。为此，本文提出四项评价标准：性能、合理性、可迁移性与可及性，并评估基于地理空间基础模型（FM）嵌入的TESSERA与AlphaEarth方法在塞内加尔花生带区域的表现，对比当前基准方法。结果表明，TESSERA嵌入方法在土地覆盖与作物类型制图中表现最优，在一次时序迁移任务中准确率比次优方法高出28%。这表明TESSERA嵌入是塞内加尔作物分类与制图的有效技术方案。
原文摘要 · Abstract (English)
Crop type maps from satellite remote sensing are important tools for food security, local livelihood support and climate change mitigation in smallholder regions of the world, but most satellite-based methods are not well suited to smallholder conditions. To address this gap, we establish a four-part criteria for a useful embedding-based approach consisting of 1) performance, 2) plausibility, 3) transferability and 4) accessibility and evaluate geospatial foundation model (FM) embeddings -based approaches using TESSERA and AlphaEarth against current baseline methods for a region in the groundnut basin of Senegal. We find that the TESSERA -based approach to land cover and crop type mapping fulfills the selection criteria best, and in one temporal transfer example shows 28% higher accuracy compared to the next best method. These results indicate that TESSERA embeddings are an effective approach for crop type classification and mapping tasks in Senegal.
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