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

Sizhe Wang, Wenwen Li

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number101715
JournalComputers, Environment and Urban Systems
Volume90
DOIs
StatePublished - Nov 2021

Keywords

  • Data enrichment
  • Deep Learning
  • GeoAI
  • Multi-source data fusion
  • Object detection

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Ecological Modeling
  • Environmental Science(all)
  • Urban Studies

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