The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B5-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 185–189, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-185-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 185–189, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-185-2020

  24 Aug 2020

24 Aug 2020

OPPORTUNITIES FOR MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN NATIONAL MAPPING AGENCIES: ENHANCING ORDNANCE SURVEY WORKFLOW

J. Murray1,2, I. Sargent1,3, D. Holland1, A. Gardiner1, K. Dionysopoulou1, S. Coupland1, J. Hare3, C. Zhang2, and P. M. Atkinson2,3,4 J. Murray et al.
  • 1Ordnance Survey, Explorer House, Adanac Drive, Nursling, Southampton, SO16 0AS, UK
  • 2Lancaster University, Bailrigg, Lancaster, LA1 4YQ, UK
  • 3University of Southampton, University Road, Southampton, SO17 1BJ, UK
  • 4Chinese Academy of Sciences, 11A Datun Road, Beijing 100101, China

Keywords: Ordnance Survey, Artificial Intelligence, Machine Learning, Deep Neural Networks, National Mapping Agency

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.