Some thoughts on deep learning empowering cartography

Ai, Tinghua

doi:10.24294/jgc.v5i2.1670

Cited by 8 publications

(8 citation statements)

References 24 publications

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“…(4) The use of deep learning for map generalization lacked proper explanation. It is essential to improve the interpretability of intelligent map generalization by integrating knowledge of map generalization in the architecture design, hyperparameter adjustment, and feature selection of deep learning (Ai, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…The complexity of map generalization and the limited availability of typical generalized samples have hindered the application of deep learning. However, generative adversarial networks and graph neural networks have sparked renewed interest among cartographers in deep learning as a tool for map generalization (Ai, 2021; Touya et al, 2019). Subsequently, deep learning techniques were employed to implement map generalization operators through rasterization.…”

Section: Related Workmentioning

confidence: 99%

“…Deep learning, the most popular means for building artificial intelligence, has been successful in various fields, such as language processing, image recognition, and video processing. It offers a new opportunity for map generalization (Ai, 2021; Touya et al, 2019). Supported by remote‐sensing big data, deep learning has solved many geospatial problems, including ground object interpretation (Zhang et al, 2019), change detection (Li et al, 2021), and terrain detection (Du et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Integrating domain knowledge and graph convolutional neural networks to support river network selection

Yu,

Ai,

Yang

et al. 2023

Transactions in GIS

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Deep learning is increasingly being used to improve the intelligence of map generalization. Vector‐based map generalization, utilizing deep learning, is an important avenue for research. However, there are three questions: (1) transforming vector data into a deep learning data paradigm; (2) overcoming the limitation of the number of samples; and (3) determining whether existing knowledge can accelerate deep learning. To address these questions, taking river network selection as an example, this study presents a framework integrating hydrological knowledge into graph convolutional neural networks (GCNNs). This framework consists of the following steps: constructing a dual graph of river networks (DG_RN), extracting domain knowledge as node attributes of DG_RN, developing an architecture of GCNNs for the selection, and designing a fine‐tuning rule to refine the GCNN results. Experiments show that our framework outperforms existing machine learning and traditional feature sorting methods using different datasets and achieves good morphological consistency after the selection. Furthermore, these results indicate that DG_RN meets the data paradigm of graph deep learning, and the framework integrating existing characteristics (i.e., Strahler coding, the number of tributaries, the distance between proximity rivers, and upstream drainage area) mitigates the dependence of GCNNs on plenty of samples and enhance its performance.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating domain knowledge and graph convolutional neural networks to support river network selection

Yu,

Ai,

Yang

et al. 2023

Transactions in GIS

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“…This poses challenges for interpreting UMIs. (2) UMIs play a crucial role in exploring geo‐science characteristics, revealing mechanisms behind geographic processes, and uncovering spatial patterns (Ai, 2021). Consequently, spatial query becomes an important aspect of UMIs semantic retrieval (Walter et al., 2013).…”

Section: Related Workmentioning

confidence: 99%

An ontology‐based semantic description model of ubiquitous map images

Jia,

Yang,

Ding

et al. 2024

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Map images with various themes and cartographic representations have become ubiquitous on the Internet. Such ubiquitously and openly accessible data, named ubiquitous map images in this study, are a potential resource for many geographic information applications such as cartographic design. However, there is a semantic gap between the simple physical form and the complex connotation of ubiquitous map images, which hinders their further applications. To mitigate such barrier, this article develops an ontology‐based semantic description model for ubiquitous map images. First, we discuss the design concerns and principles of the semantic description model of ubiquitous map images. Second, three semantic layers of the semantic description model are proposed, that is, image semantic description layer, cognitive tool layer, and information source layer, and detailed semantic description items are defined for each layer. Furthermore, a formalized semantic description model for ubiquitous map images is developed using ontology construction tools, which lays the foundation for automated and fine‐grained reasoning with the information embedded in map images. We construct a small test dataset consisting of weather maps, and use three types of constraints, namely “time‐topic,” “region‐topic,” and “map auxiliary elements” for the semantic retrieval experiments. The experiments show that the proposed semantic ontology model can enable complex semantic retrieval of ubiquitous map images. Finally, the scalability of the model is discussed from three perspectives: the depth of description, the combination with intelligent methods, and the integration with other open knowledge bases. The proposed model provides a semantic label system for applying data‐driven approaches to decode ubiquitous map images, which also paves the path to the development of cartographic theory in the era of information and communications technologies.

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“…This has been shown by Chen et al (2021) for satellite imagery and in combination with image-to-image diffusion, or by Kang et al (2023) for direct map generation from prompts using text-to-image. Similarly, neural style transfer approaches (Ai 2022) can be used to generate maps with specific looks. Common to these approaches is a single-step generation process.…”

Section: Introductionmentioning

confidence: 99%

Generative text-to-image diffusion for automated map production based on geosocial media data

Dunkel,

Burghardt,

Gugulica

2023

Preprint

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The state of generative AI has taken a leap forward with the availability of open source diffusion models. Here, we demonstrate an integrated workflow that uses text-to-image Stable Diffusion at its core to automatically generate icon maps such as for the area of the Großer Garten, a tourist hotspot in Dresden, Germany. The provided workflow is based on the aggregation of geosocial media data from Twitter, Flickr, Instagram, and iNaturalist. This data is used to create diffusion prompts, to account for the collective attribution of meaning and importance by the population in map generation. Specifically, we contribute methods for simplifying the variety of contexts communicated on social media, through spatial clustering and semantic filtering, for use in prompts, and then demonstrate how this human-contributed baseline data can be used in prompt engineering to automatically generate icon maps. Replacing labels on maps with expressive graphics has the general advantage of reaching a broader audience, such as children and other illiterate groups. For example, the resulting maps can be used to inform tourists of all backgrounds about important activities, points of interest, and landmarks without the need for translation. Several challenges are identified and possible future optimizations are described for different steps of the process. The code and data are fully provided and shared in several Jupyter notebooks, allowing for transparent replication of the workflow and adoption to other areas or datasets.

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Some thoughts on deep learning empowering cartography

Cited by 8 publications

References 24 publications

Integrating domain knowledge and graph convolutional neural networks to support river network selection

Integrating domain knowledge and graph convolutional neural networks to support river network selection

An ontology‐based semantic description model of ubiquitous map images

Generative text-to-image diffusion for automated map production based on geosocial media data

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