CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels
CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels
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DOI：
10.1109/bigdata52589.2021.9671569
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Rahul Ghosh;Praveen Ravirathinam;X. Jia;A. Khandelwal;D. Mulla;Vipin Kumar
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作者：
Rahul Ghosh;Praveen Ravirathinam;X. Jia;A. Khandelwal;D. Mulla;Vipin Kumar
测绘和监测作物是实现农业可持续集约化和解决全球粮食安全问题的关键一步。像ImageNet这样的数据集给计算机视觉应用带来了革命性的变化，可以加快新作物测绘技术的发展。目前，美国农业部(USDA)每年发布农田数据层(CDL)，其中包含整个美利坚合众国30M分辨率的作物标签。虽然CDL是最先进的，被广泛用于许多农业应用，但它也有一些局限性(例如，像素化错误、前几年遗留下来的标签以及对小作物分类的错误)。在这项工作中，我们使用基于Google Earth引擎的健壮图像处理流水线和一种新的基于关注度的时空语义分割算法STATT，为加利福尼亚州中央山谷地区的不同作物创建了一个新的语义分割基准数据集，称为CalCROP21，空间分辨率为10M。STATT使用重新采样(内插)的CDL标签进行训练，但通过利用Sentinel2多光谱图像系列中的空间和时间模式来有效捕捉作物之间的物候差异，并使用注意力来减少云和其他大气干扰的影响，能够生成比CDL更好的预测。我们还提出了一项综合评估，表明与重新采样的CDL标记相比，STATT具有显著更好的结果。我们已经发布了用于生成基准数据集的数据集和处理流水线代码。
Mapping and monitoring crops is a key step to-wards sustainable intensification of agriculture and addressing global food security. A dataset like ImageNet that revolutionized computer vision applications can accelerate development of novel crop mapping techniques. Currently, the United States Department of Agriculture (USDA) annually releases the Cropland Data Layer (CDL) which contains crop labels at 30m resolution for the entire United States of America. While CDL is state of the art and is widely used for a number of agricultural applications, it has a number of limitations (e.g., pixelated errors, labels carried over from previous years and errors in classification of minor crops). In this work, we create a new semantic segmentation benchmark dataset, which we call CalCROP21, for the diverse crops in the Central Valley region of California at 10m spatial resolution using a Google Earth Engine based robust image processing pipeline and a novel attention based spatio-temporal semantic segmentation algorithm STATT. STATT uses re-sampled (interpolated) CDL labels for training, but is able to generate a better prediction than CDL by leveraging spatial and temporal patterns in Sentinel2 multi-spectral image series to effectively capture phenologic differences amongst crops and uses attention to reduce the impact of clouds and other atmospheric disturbances. We also present a comprehensive evaluation to show that STATT has significantly better results when compared to the resampled CDL labels. We have released the dataset and the processing pipeline code for generating the benchmark dataset.