CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence

• URL: http://arxiv.org/abs/2312.08600v1
• Date: Thu, 14 Dec 2023 01:54:38 GMT
• Title: CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence
• Authors: Xiran Zhou, Yi Wen, Honghao Li, Kaiyuan Li, Zhenfeng Shao, Zhigang Yan, Xiao Xie
• Abstract summary: We develop a large-scale benchmark dataset for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. These well-labelled datasets would facilitate the state-of-the-art machine intelligence technologies to conduct map feature detection, map pattern recognition and map content retrieval.
• Score: 9.652629004863364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Maps are fundamental medium to visualize and represent the real word in a simple and 16 philosophical way. The emergence of the 3rd wave information has made a proportion of maps are available to be generated ubiquitously, which would significantly enrich the dimensions and perspectives to understand the characteristics of the real world. However, a majority of map dataset have never been discovered, acquired and effectively used, and the map data used in many applications might not be completely fitted for the authentic demands of these applications. This challenge is emerged due to the lack of numerous well-labelled benchmark datasets for implementing the deep learning approaches into identifying complicated map content. Thus, we develop a large-scale benchmark dataset that includes well-labelled dataset for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. Furthermore, these well-labelled datasets would facilitate the state-of-the-art machine intelligence technologies to conduct map feature detection, map pattern recognition and map content retrieval. We hope our efforts would be useful for AI-enhanced cartographical applications.

Related papers

• OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction [65.4151284975348]
  OpenSatMap is a fine-grained, high-resolution satellite dataset for large-scale map construction. By publishing and maintaining the dataset, we provide a high-quality benchmark for satellite-based map construction and downstream tasks like autonomous driving.
  arXiv  Detail & Related papers  (2024-10-30T17:56:02Z)
• A roadmap for generative mapping: unlocking the power of generative AI for map-making [1.128529637069462]
  This paper highlights the key applications of generative AI in map-making. It identifies the specific technologies required and the challenges of using current methods. It provides a roadmap for developing a generative mapping system (GMS) to make map-making more accessible.
  arXiv  Detail & Related papers  (2024-10-21T08:29:43Z)
• Detecting Omissions in Geographic Maps through Computer Vision [18.36056648425432]
  We develop and evaluate a method for automatically identifying maps that depict specific regions and feature landmarks with designated names. We address three main subtasks: differentiating maps from non-maps, verifying the accuracy of the region depicted, and confirming the presence or absence of particular landmark names. Experiments on this dataset demonstrate that our technique achieves F1-score of 85.51% for identifying maps excluding specific territorial landmarks.
  arXiv  Detail & Related papers  (2024-07-15T13:26:58Z)
• Mapping High-level Semantic Regions in Indoor Environments without Object Recognition [50.624970503498226]
  The present work proposes a method for semantic region mapping via embodied navigation in indoor environments. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location.
  arXiv  Detail & Related papers  (2024-03-11T18:09:50Z)
• Mind the map! Accounting for existing map information when estimating online HDMaps from sensor [15.275704436439012]
  Estimating HDMaps from sensors promises to significantly lighten costs. We propose to account for existing maps of the precise situation studied when estimating HDMaps. We introduce MapEX, a novel online HDMap estimation framework.
  arXiv  Detail & Related papers  (2023-11-17T13:40:10Z)
• The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps [7.209761597734092]
  mapKurator is an end-to-end system integrating machine learning models with a comprehensive data processing pipeline. We deployed the mapKurator system and enabled the processing of over 60,000 maps and over 100 million text/place names in the David Rumsey Historical Map collection.
  arXiv  Detail & Related papers  (2023-06-29T16:05:40Z)
• AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning [69.47585818994959]
  We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. We utilize the big geo-data platform IBM PAIRS to dynamically generate such labels in dense urban areas.
  arXiv  Detail & Related papers  (2022-01-31T20:02:22Z)
• Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection [5.872532529455414]
  We propose a method to automatically generate an unlimited amount of annotated historical map images for training text detection models. We show that the state-of-the-art text detection models can benefit from the synthetic historical maps.
  arXiv  Detail & Related papers  (2021-12-12T00:27:03Z)
• HDNET: Exploiting HD Maps for 3D Object Detection [99.49035895393934]
  We show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. We design a single stage detector that extracts geometric and semantic features from the HD maps. As maps might not be available everywhere, we also propose a map prediction module that estimates the map on the fly from raw LiDAR data.
  arXiv  Detail & Related papers  (2020-12-21T21:59:54Z)
• Structured Landmark Detection via Topology-Adapting Deep Graph Learning [75.20602712947016]
  We present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. Experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis)
  arXiv  Detail & Related papers  (2020-04-17T11:55:03Z)
• Weakly-Supervised Salient Object Detection via Scribble Annotations [54.40518383782725]
  We propose a weakly-supervised salient object detection model to learn saliency from scribble labels. We present a new metric, termed saliency structure measure, to measure the structure alignment of the predicted saliency maps. Our method not only outperforms existing weakly-supervised/unsupervised methods, but also is on par with several fully-supervised state-of-the-art models.
  arXiv  Detail & Related papers  (2020-03-17T12:59:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.