Multi-task deep learning strategy for map-type classification

ABSTRACT

The information contained in a map is always represented by text, symbols, and map-type. Among them, map-type is a critical element that denotes the category and theme of map content, which can support map content extraction, map retrieval, and other map data mining tasks. However, the representations of map-type are always so complex and diverse that relies on multiple descriptive labels. Traditional deep learning methods regarding map-type classification are developed by single label, which only supports single-task classification. This means these approaches might overlook the common features among multiple map-type. In this paper, we propose a framework of multi-task deep learning strategy for employing the state-of-the-art deep convolutional neural network models, including ResNet50, MobileNetV2, and Inception-v3, to conduct efficient multi-label map-type classification. Specifically, we develop the dedicated classification module and label selection layer, and integrate them into the backbone of the deep convolutional network model. The experiments revealed that our proposed multi-task classification strategy can achieve greater accuracy in map-type classification, with less processing time required compared to state-of-the-art deep learning regarding map-type classification. This proves that multi-task classification strategy could be competitive to recognize and discover the complex map-type information.

KEYWORDS:

• Deep learning
• multi-task deep learning
• map-type classification
• convolutional neural network
• multi-label learning

Acknowledgments

This research is funded in part by National Natural Science Foundation of China, under Grant 42201473, and 42271352, and Key Research and Development Program of Ningxia Hui Autonomous Region, under Grant 2023BEG02068.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The dataset used in this manuscript can be accessed by the following repositories:

• Harvard Dataverse: https://doi.org/10.7910/DVN/ZBXJD5

• Github: https://github.com/WenYi-cumt/CartoPatternRecognition/

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2024.2368574

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [41971370]; Key Research and Development Program of Ningxia Hui Autonomous Region [2023BEG02068].