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

Murray, Jon and Sargent, Isabel and Holland, David and Gardiner, Andy and Dionysopoulou, kyriaki and Coupland, Steve and Hare, Jonathon and Zhang, Ce and Atkinson, Peter (2020) Opportunities for machine learning and artificial intelligence in national mapping agencies:enhancing ordnance survey workflow. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences. 185–189.

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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.

Item Type:
Journal Article
Journal or Publication Title:
International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences
Subjects:
ID Code:
146986
Deposited By:
Deposited On:
04 Sep 2020 14:10
Refereed?:
Yes
Published?:
Published
Last Modified:
25 Sep 2020 05:02