# Deep Convolutional Neural Networks for Map-Type Classification

arXiv: https://arxiv.org/abs/1805.10402 · HTML: https://mlsanity.com/paper/1805.10402/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2018.

## Built on by (most cited first, 3 of 3)

- [Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics](https://mlsanity.com/paper/2312.07901/) (2023)
- [Evolving Thematic Map Design in Academic Cartography: A Thirty-Year Study Based on Multilingual Journals](https://mlsanity.com/paper/2604.22539/) (2026)
- [Detecting Omissions in Geographic Maps through Computer Vision](https://mlsanity.com/paper/2407.10709/) (2024)

## Abstract (arXiv)

Maps are an important medium that enable people to comprehensively understand the configuration of cultural activities and natural elements over different times and places. Although massive maps are available in the digital era, how to effectively and accurately access the required map remains a challenge today. Previous works partially related to map-type classification mainly focused on map comparison and map matching at the local scale. The features derived from local map areas might be insufficient to characterize map content. To facilitate establishing an automatic approach for accessing the needed map, this paper reports our investigation into using deep learning techniques to recognize seven types of map, including topographic map, terrain map, physical map, urban scene map, the National Map, 3D map, nighttime map, orthophoto map, and land cover classification map. Experimental results show that the state-of-the-art deep convolutional neural networks can support automatic map-type classification. Additionally, the classification accuracy varies according to different map-types. We hope our work can contribute to the implementation of deep learning techniques in cartographical community and advance the progress of Geographical Artificial Intelligence (GeoAI).