GeoAI in terrain analysis: Enabling multi-source deep learning and data fusion for natural feature detection
GeoAI in terrain analysis: Enabling multi-source deep learning and data fusion for natural feature detection
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DOI：
10.1016/j.compenvurbsys.2021.101715
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Sizhe Wang;Wenwen Li
中科院分区：
文献类型：
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作者：
Sizhe Wang;Wenwen Li
在本文中，我们报告了一种新的GeoAI研究方法，该方法可以从多源地理空间数据中进行深度机器学习，以进行自然特征检测。特别是，开发了一个多源、基于深度学习的对象检测流水线。该流水线引入了三个新特征：首先，数据级融合（即，在物体检测模型中集成了多个特征级融合（即卷积神经网络上的通道扩展）和特征级融合，以便能够从多源数据（包括遥感图像和数字高程模型数据）中同时进行机器学习。其次，提出了一种新的数据融合策略，将DEM数据及其衍生数据融合，形成一个新的、融合的数据源，具有丰富的信息内容和图像特征。该模型还通过结合所提出的数据融合和特征级融合策略来实现深度学习，从而大大改善了检测结果。第三，将两组不同的数据增强技术应用于多源训练数据，以进一步提高模型性能。通过一系列实验验证了所提策略在多源深度学习中的有效性。
In this paper we report on a new GeoAI research method which enables deep machine learning from multi-source geospatial data for natural feature detection. In particular, a multi-source, deep learning-based object detection pipeline was developed. This pipeline introduces three new features: First, strategies of both data-level fusion (i.e., channel expansion on convolutional neural networks) and feature-level fusion were integrated into the object detection model to allow simultaneous machine learning from multi-source data, including remote sensing imagery and Digital Elevation Model (DEM) data. Second, a new data fusion strategy was developed to blend DEM data and its derivatives to create a new, fused data source with enriched information content and image features. The model has also enabled deep learning by combining both the proposed data fusion and feature-level fusion strategies to yield a much-improved detection result. Third, two different sets of data augmentation techniques were applied to the multi-source training data to further improve the model performance. A series of experiments were conducted to verify the effectiveness of the proposed strategies in multi-source deep learning.