Drone Imagery Classification Training Dataset for Crop Types in Rwanda

RTI International (RTI) generated 2,611 labeled point locations representing 19 different land cover types, clustered in 5 distinct agroecological zones within Rwanda. These land cover types were reduced to three crop types (Banana, Maize, and Legume), two additional non-crop land cover types (Forest and Structure), and a catch-all Other land cover type to provide training/evaluation data for a crop classification model. Each point is attributed with its latitude and longitude, the land cover type, and the degree of confidence the labeler had when classifying the point location. For each location there are also three corresponding image chips (4.5 m x 4.5 m in size) with the point id as part of the image name. Each image contains a P1, P2, or P3 designation in the name, indicating the time period. P1 corresponds to December 2018, P2 corresponds to January 2019, and P3 corresponds to February 2019. These data were used in the development of research documented in greater detail in “Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images” (Chew et al., 2020).

RTI国际公司（RTI International，RTI）生成了2611个带标注的点位坐标，涵盖19种不同土地覆盖类型，分布在卢旺达境内的5个不同农业生态区（agroecological zones）中。上述土地覆盖类型被归并为3类作物类型（香蕉、玉米与豆科作物）、2类非作物土地覆盖类型（林地与建筑用地），以及一个兜底的"其他"土地覆盖类别，以构建作物分类模型的训练与评估数据集。每个点位均附带经纬度坐标、土地覆盖类型标签，以及标注人员对该点位分类的置信度。每个点位还对应三张尺寸为4.5米×4.5米的图像切片（image chips），图像文件名中包含点位ID。文件名带有P1、P2或P3标识，用以区分拍摄时段：P1对应2018年12月，P2对应2019年1月，P3对应2019年2月。该数据集曾用于支撑《深度神经网络与迁移学习实现无人机影像（UAV Images）中的粮食作物识别》（"Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images"，Chew等，2020）一文中详述的相关研究开发工作。