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

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Opportunities for machine learning and artificial intelligence in national mapping agencies : enhancing ordnance survey workflow. / Murray, Jon; Sargent, Isabel; Holland, David; Gardiner, Andy; Dionysopoulou, kyriaki; Coupland, Steve; Hare, Jonathon; Zhang, Ce; Atkinson, Peter.

In: International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 24.08.2020, p. 185–189.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Murray, J, Sargent, I, Holland, D, Gardiner, A, Dionysopoulou, K, Coupland, S, Hare, J, Zhang, C & Atkinson, P 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, pp. 185–189. https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-185-2020

APA

Murray, J., Sargent, I., Holland, D., Gardiner, A., Dionysopoulou, K., Coupland, S., Hare, J., Zhang, C., & Atkinson, P. (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. [XLIII-B5-2020]. https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-185-2020

Vancouver

Murray J, Sargent I, Holland D, Gardiner A, Dionysopoulou K, Coupland S et al. 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. 2020 Aug 24;185–189. XLIII-B5-2020. https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-185-2020

Author

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

Bibtex

@article{4febe63010584a469fee9b6642e3efb5,
title = "Opportunities for machine learning and artificial intelligence in national mapping agencies: enhancing ordnance survey workflow",
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{\textquoteright}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.",
keywords = "Ordnance Survey, Artificial Intelligence, Machine Learning, Deep Neural Networks, National Mapping Agency",
author = "Jon Murray and Isabel Sargent and David Holland and Andy Gardiner and kyriaki Dionysopoulou and Steve Coupland and Jonathon Hare and Ce Zhang and Peter Atkinson",
year = "2020",
month = aug,
day = "24",
doi = "10.5194/isprs-archives-XLIII-B5-2020-185-2020",
language = "English",
pages = "185–189",
journal = "International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences",

}

RIS

TY - JOUR

T1 - Opportunities for machine learning and artificial intelligence in national mapping agencies

T2 - enhancing ordnance survey workflow

AU - Murray, Jon

AU - Sargent, Isabel

AU - Holland, David

AU - Gardiner, Andy

AU - Dionysopoulou, kyriaki

AU - Coupland, Steve

AU - Hare, Jonathon

AU - Zhang, Ce

AU - Atkinson, Peter

PY - 2020/8/24

Y1 - 2020/8/24

N2 - 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.

AB - 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.

KW - Ordnance Survey

KW - Artificial Intelligence

KW - Machine Learning

KW - Deep Neural Networks

KW - National Mapping Agency

U2 - 10.5194/isprs-archives-XLIII-B5-2020-185-2020

DO - 10.5194/isprs-archives-XLIII-B5-2020-185-2020

M3 - Journal article

SP - 185

EP - 189

JO - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences

JF - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences

M1 - XLIII-B5-2020

ER -