Abstract
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.
Highlights
National mapping agencies (NMA) of the world are typically tasked with producing geospatial data and topographic maps of their respective countries
Using Ordnance Survey (OS) as an example NMA, this paper briefly describes the development of past, present and future artificial intelligence (AI) projects that will achieve positive impacts on a typical workflow within an NMA
It is understood that to maximise the potential for AI use in NMAs, robust systems of network training and interrogation need to be developed to understand where AI supported discoveries are meaningful and to what end these could be applied in an operational sense (Sargent et al, 2019)
Summary
National mapping agencies (NMA) of the world are typically tasked with producing geospatial data and topographic maps of their respective countries. Remote sensing activities are focused predominantly on the acquisition of highly detailed aerial imagery This can be imagery at a pixel spatial resolution of 25cm resulting in several thousand rows and columns per image (Sargent et al, 2019). These images contain greater levels of detail and information than it is possible for NMAs to extract and make available to their customers using traditional, part-manual processing methods (Holland & Marshall, 2004, Cygan, 2019, Sargent et al, 2019). Using OS as an example NMA, this paper briefly describes the development of past, present and future AI projects that will achieve positive impacts on a typical workflow within an NMA
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