Maps depict natural and human-induced changes on earth at a fine resolution for large areas and over long periods of time. In addition, maps—especially historical maps—are often the only information source about the earth as surveyed using geodetic techniques. In order to preserve these unique documents, increasing numbers of digital map archives have been established, driven by advances in software and hardware technologies. Since the early 1980s, researchers from a variety of disciplines, including computer science and geography, have been working on computational methods for the extraction and recognition of geographic features from archived images of maps (digital map processing). The typical result from map processing is geographic information that can be used in spatial and spatiotemporal analyses in a Geographic Information System environment, which benefits numerous research fields in the spatial, social, environmental, and health sciences. However, map processing literature is spread across a broad range of disciplines in which maps are included as a special type of image. This article presents an overview of existing map processing techniques, with the goal of bringing together the past and current research efforts in this interdisciplinary field, to characterize the advances that have been made, and to identify future research directions and opportunities.

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Recent growth of the geospatial information on the web has made it possible to easily access various maps and orthoimagery. By integrating these maps and imagery, we can create intelligent images that combine the visual appeal and accuracy of imagery with the detailed attribution information often contained in diverse maps. However, accurately integrating maps and imagery from different data sources remains a challenging task. This is because spatial data obtained from various data sources may have different projections and different accuracy levels. Most of the existing algorithms only deal with vector to vector spatial data integration or require human intervention to accomplish imagery to map conflation. In this paper, we describe an information integration approach that utilizes common vector datasets as "glue" to automatically conflate imagery with street maps. We present efficient techniques to automatically extract road intersections from imagery and maps as control points. We also describe a specialized point pattern matching algorithm to align the two point sets and conflation techniques to align the imagery with maps. We show that these automatic conflation techniques can automatically and accurately align maps with images of the same area. In particular, using the approach described in this paper, our system automatically aligns a set of TIGER maps for an area in El Segundo, CA to the corresponding orthoimagery with an average error of 8.35 meters per pixel. This is a significant improvement considering that simply combining the TIGER maps with the corresponding imagery based on geographic coordinates provided by the sources results in error of 27 meters per pixel.

Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.