Opportunities for machine learning and artificial intelligence in national mapping agencies: enhancing ordnance survey workflow

J. Murray*, I. Sargent, D. Holland, A. Gardiner, K. Dionysopoulou, S. Coupland, J. Hare, Ce Zhang, Peter M. Atkinson

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

2 Citations (Scopus)

Abstract

National Mapping agencies (NMA) are frequently tasked with providing highly accurate geospatial data for a range of customers. Traditionally, this challenge has been met by combining the collection of remote sensing data with extensive field work, and the manual interpretation and processing of the combined data. Consequently, this task is a significant logistical undertaking which benefits the production of high quality output, but which is extremely expensive to deliver. Therefore, novel approaches that can automate feature extraction and classification from remotely sensed data, are of great potential interest to NMAs across the entire sector. Using research undertaken at Great Britain's NMA; Ordnance Survey (OS) as an example, this paper provides an overview of the recent advances at an NMA in the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) based applications. Examples of these approaches are in automating the process of feature extraction and classification from remotely sensed aerial imagery. In addition, recent OS research in applying deep (convolutional) neural network architectures to image classification are also described. This overview is intended to be useful to other NMAs who may be considering the adoption of similar approaches within their workflows.

Original languageEnglish
Pages (from-to)185-189
Number of pages5
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume43
Issue numberB5
DOIs
Publication statusPublished - 6 Aug 2020
Event2020 24th ISPRS Congress - Technical Commission V (TC-V) on Education and Outreach - Youth Forum - Nice, Virtual, France
Duration: 31 Aug 20202 Sept 2020

Bibliographical note

Funding Information:
The authors would like to thank the anonymous reviewers for their insightful comments in reshaping this paper. The research associated with National Physical Laboratories and Science and Technology Facilities Council is funded by Innovate UK under the A4I (Analysis for Innovators programme) project number: 36335. This research was jointly funded by the Engineering and Physical Sciences Research Council; Impact Acceleration Account and Ordnance Survey The lead author would also like to acknowledge the assistance of Miss B. Heap, Lancaster University, UK, for help given during the preparation of this manuscript.

Publisher Copyright:
© 2020 Authors.

Keywords

  • Artificial Intelligence
  • Deep Neural Networks
  • Machine Learning
  • National Mapping Agency
  • Ordnance Survey

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