PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Da

PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data

An automated machine learning framework for geospatial data named PAIRS AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform. The framework simplifies the development of industrial machine learning solutions leveraging geospatial data to the extent that the user inputs are minimized to merely a text file containing labeled GPS coordinates.PAIRS AutoGeo automatically gathers required data at the location coordinates, assembles the training data, performs quality check, and trains multiple machine learning models for subsequent deployment. The framework is validated using a realistic industrial use case of tree species classification. Open-source tree species data are used as the input to train a random forest classifier and a modified ResNet model for 10-way tree species classification based on aerial imagery, which leads to an accuracy of $59.8\%$ and $81.4\%$, respectively. This use case exemplifies how PAIRS AutoGeo enables users to leverage machine learning without extensive geospatial expertise.

PAIRS AutoGeo：用于海量地理空间数据的自动化机器学习框架

在IBM PAIRS Geoscope大数据和分析平台上引入了一个名为PAIRS AutoGeo的地理空间数据自动机器学习框架。该框架简化了利用地理空间数据的工业机器学习解决方案的开发，其程度是将用户输入最小化为仅包含标签GPS坐标的文本文件。.. PAIRS AutoGeo会在位置坐标处自动收集所需的数据，组合训练数据，执行质量检查，并训练多个机器学习模型以用于后续部署。该框架使用树种分类的实际工业用例进行了验证。开源树种数据被用作训练随机森林分类器和基于航空影像的10路树种分类的改良ResNet模型的输入，从而提高了精度。 59.8％ 和 81.4％ ， 分别。该用例说明了PAIRS AutoGeo如何使用户无需广泛的地理空间专业知识即可利用机器学习。 （阅读更多）