Estimating the number and size of irrigation center pivot systems (CPS) from remotely sensed data, using artificial intelligence (AI), is a potential information source for assessing agricultural water use. In this study, we identified two technical challenges in the neural-network-based classification: Firstly, an effective reduction of the feature space of the remote sensing data to shorten training times and increase classification accuracy is required. Secondly, the geographical transferability of the AI algorithms is a pressing issue if AI is to replace human mapping efforts one day. Therefore, we trained the semantic image segmentation algorithm U-NET on four spectral channels (U-NET SPECS) and the first three principal components (U-NET principal component analysis (PCA)) of ESA/Copernicus Sentinel-2 images on a study area in Texas, USA, and assessed the geographic transferability of the trained models to two other sites: the Duero basin, in Spain, and South Africa. U-NET SPECS outperformed U-NET PCA at all three study areas, with the highest f1-score at Texas (0.87, U-NET PCA: 0.83), and a value of 0.68 (U-NET PCA: 0.43) in South Africa. At the Duero, both models showed poor classification accuracy (f1-score U-NET PCA: 0.08; U-NET SPECS: 0.16) and segmentation quality, which was particularly evident in the incomplete representation of the center pivot geometries. In South Africa and at the Duero site, a high rate of false positive and false negative was observed, which made the model less useful, especially at the Duero test site. Thus, geographical invariance is not an inherent model property and seems to be mainly driven by the complexity of land-use pattern. We do not consider PCA a suited spectral dimensionality reduction measure in this. However, shorter training times and a more stable training process indicate promising prospects for reducing computational burdens. We therefore conclude that effective dimensionality reduction and geographic transferability are important prospects for further research towards the operational usage of deep learning algorithms, not only regarding the mapping of CPS.

中文翻译：

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映射灌溉中心枢轴的Sentinel-2图像的语义分割

使用人工智能（AI）从遥感数据估算灌溉中心枢纽系统（CPS）的数量和规模，是评估农业用水的潜在信息来源。在这项研究中，我们确定了基于神经网络的分类中的两个技术挑战：首先，需要有效减少遥感数据的特征空间以缩短训练时间并提高分类精度。其次，如果AI一天要取代人类的地图绘制工作，那么AI算法的地理可移植性就成为一个紧迫的问题。因此，我们研究了ESA / Copernicus Sentinel-2图像的四个光谱通道（U-NET SPECS）和前三个主要成分（U-NET主成分分析（PCA））的语义图像分割算法U-NET。美国德克萨斯州，并评估了经过训练的模型到其他两个地点的地理转移性：西班牙的杜罗盆地和南非。在所有三个研究领域中，U-NET SPECS均优于U-NET PCA，在德克萨斯州的f1-得分最高（0.87，U-NET PCA：0.83），在南非的值为0.68（U-NET PCA：0.43）。 。在Duero，这两个模型均显示出较差的分类精度（f1分数U-NET PCA：0.08； U-NET SPECS：0.16）和分割质量，这在中心枢轴几何图形的不完整表示中尤为明显。在南非和Duero站点，观察到较高的假阳性和假阴性率，这使得该模型的使用率降低，尤其是在Duero测试站点。因此，地理不变性不是固有的模型属性，似乎主要是由土地利用模式的复杂性驱动的。在这种情况下，我们认为PCA不适合作为降低光谱维数的措施。但是，较短的训练时间和更稳定的训练过程表明减轻计算负担的前景广阔。因此，我们得出结论，有效的降维和地理可迁移性是进一步研究深度学习算法的操作用途的重要前景，而不仅限于CPS的映射。