In 2023, 58.0% of the African population experienced moderate to severe food insecurity, with 21.6% facing severe food insecurity. Land-use and land-cover maps provide crucial insights for addressing food insecurity by improving agricultural efforts, including mapping and monitoring crop types and estimating yield. The development of global land-cover maps has been facilitated by the increasing availability of earth observation data and advancements in geospatial machine learning. However, these global maps exhibit lower accuracy and inconsistencies in Africa, partly due to the lack of representative training data. To address this issue, we propose a data-centric framework with a teacher-student model setup, which uses diverse data sources of satellite images and label examples to produce local land-cover maps. Our method trains a high-resolution teacher model on images with a resolution of 0.331 m/pixel and a low-resolution student model on publicly available images with a resolution of 10 m/pixel. The student model also utilizes the teacher model's output as its weak label examples through knowledge transfer. We evaluated our framework using Murang'a county in Kenya, renowned for its agricultural productivity, as a use case. Our local models achieved higher quality maps, with improvements of 0.14 in the F1 score and 0.21 in Intersection-over-Union, compared to the best global model. Our evaluation also revealed inconsistencies in existing global maps, with a maximum agreement rate of 0.30 among themselves. Our work provides valuable guidance to decision-makers for driving informed decisions to enhance food security.

翻译：2023年，58.0%的非洲人口经历了中度至重度的粮食不安全状况，其中21.6%面临重度粮食不安全。土地利用与土地覆盖地图通过改进农业工作（包括测绘和监测作物类型以及估算产量），为解决粮食不安全问题提供了关键见解。地球观测数据的日益普及和地理空间机器学习的进步促进了全球土地覆盖地图的发展。然而，这些全球地图在非洲表现出较低的准确性和不一致性，部分原因是缺乏代表性的训练数据。为解决这一问题，我们提出了一个以数据为中心的框架，采用师生模型设置，利用多样化的卫星图像和标签示例数据源来生成局部土地覆盖地图。我们的方法在分辨率为0.331米/像素的图像上训练一个高分辨率教师模型，并在公开可用的分辨率为10米/像素的图像上训练一个低分辨率学生模型。学生模型还通过知识迁移，利用教师模型的输出作为其弱标签示例。我们以肯尼亚穆兰加县（以其农业生产力闻名）作为用例评估了我们的框架。与最佳全局模型相比，我们的局部模型获得了更高质量的地图，F1分数提高了0.14，交并比提高了0.21。我们的评估还揭示了现有全球地图的不一致性，它们之间的最大一致率仅为0.30。我们的工作为决策者提供了宝贵的指导，以推动基于信息的决策来加强粮食安全。