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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -