OPPORTUNITIES FOR MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN NATIONAL MAPPING AGENCIES: ENHANCING ORDNANCE SURVEY WORKFLOW
| Autor: | J. Murray, I. Sargent, D. Holland, A. Gardiner, K. Dionysopoulou, S. Coupland, J. Hare, C. Zhang, P. M. Atkinson |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B5-2020, Pp 185-189 (2020) |
| Druh dokumentu: | article |
| ISSN: | 1682-1750 2194-9034 |
| DOI: | 10.5194/isprs-archives-XLIII-B5-2020-185-2020 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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