GeoAI in terrain analysis: Enabling multi-source deep learning and data fusion for natural feature detection

Abstract

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.