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GeoImageNet: a multi-source natural feature benchmark dataset for GeoAI and supervised machine learning

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A Correction to this article was published on 25 January 2023

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Abstract

The field of GeoAI or Geospatial Artificial Intelligence has undergone rapid development since 2017. It has been widely applied to address environmental and social science problems, from understanding climate change to tracking the spread of infectious disease. A foundational task in advancing GeoAI research is the creation of open, benchmark datasets to train and evaluate the performance of GeoAI models. While a number of datasets have been published, very few have centered on the natural terrain and its landforms. To bridge this gulf, this paper introduces a first-of-its-kind benchmark dataset, GeoImageNet, which supports natural feature detection in a supervised machine-learning paradigm. A distinctive feature of this dataset is the fusion of multi-source data, including both remote sensing imagery and DEM in depicting spatial objects of interest. This multi-source dataset allows a GeoAI model to extract rich spatio-contextual information to gain stronger confidence in high-precision object detection and recognition. The image dataset is tested with a multi-source GeoAI extension against two well-known object detection models, Faster-RCNN and RetinaNet. The results demonstrate the robustness of the dataset in aiding GeoAI models to achieve convergence and the superiority of multi-source data in yielding much higher prediction accuracy than the commonly used single data source.

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Data availability

The datasets generated during and/or analysed during the current study will be made available in the GitHub repository, https://github.com/ASUcicilab/GeoImageNet.

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Funding

This work is supported in part by the National Science Foundation under awards 2120943, 1853864, and 2230034.

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Authors and Affiliations

  1. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, 85287, USA

    Wenwen Li, Sizhe Wang & Chia-Yu Hsu

  2. United States Geological Survey, Center of Excellence for Geospatial Information Science (CEGIS), Rolla, MO, 65401, USA

    Samantha T. Arundel

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  1. Wenwen Li
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  2. Sizhe Wang
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  3. Samantha T. Arundel
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  4. Chia-Yu Hsu
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Correspondence to Wenwen Li.

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Li, W., Wang, S., Arundel, S.T. et al. GeoImageNet: a multi-source natural feature benchmark dataset for GeoAI and supervised machine learning. Geoinformatica 27, 619–640 (2023). https://doi.org/10.1007/s10707-022-00476-z

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  • DOI: https://doi.org/10.1007/s10707-022-00476-z

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