Yao-Yi Chiang

A Survey of Digital Map Processing Techniques

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.

Automatic and Accurate Extraction of Road Intersections from Raster Maps

Since maps are widely available for many areas around the globe, they provide a valuable resource to help understand other geospatial sources such as to identify roads or to annotate buildings in imagery. To utilize the maps for understanding other geospatial sources, one of the most valuable types of information we need from the map is the road network, because the roads are common features used across different geospatial data sets. Specifically, the set of road intersections of the map provides key information about the road network, which includes the location of the road junctions, the number of roads that meet at the intersections (i.e., connectivity), and the orientations of these roads. The set of road intersections helps to identify roads on imagery by serving as initial seed templates to locate road pixels. Moreover, a conflation system can use the road intersections as reference features (i.e., control point set) to align the map with other geospatial sources, such as aerial imagery or vector data. In this paper, we present a framework for automatically and accurately extracting road intersections from raster maps. Identifying the road intersections is difficult because raster maps typically contain much information such as roads, symbols, characters, or even contour lines. We combine a variety of image processing and graphics recognition methods to automatically separate roads from the raster map and then extract the road intersections. The extracted information includes a set of road intersection positions, the road connectivity, and road orientations. For the problem of road intersection extraction, our approach achieves over 95% precision (correctness) with over 75% recall (completeness) on average on a set of 70 raster maps from a variety of sources.

Automatic extraction of road intersections from raster maps

Numerous raster maps are available on the Internet, but the geographic coordinates of the maps are often unknown. In order to determine the precise location of a raster map, we exploit the fact that the layout of the road intersections within a certain area can be used to determine the maps location. In this paper, we describe an approach to automatically extract road intersections from arbitrary raster maps. Identifying the road intersections is difficult because raster maps typically contain multiple layers that represent roads, buildings, symbols, street names, or even contour lines, and the road layer needs to be automatically separated from other layers before road intersections can be extracted. We combine a variety of image processing and graphics recognition methods to automatically eliminate the other layers and then extract the road intersection points. During the extraction process, we determine the intersection connectivity (i.e., number of roads that meet at an intersection) and the road orientations. This information helps in matching the extracted intersections with intersections from known sources (e.g., vector data or satellite imagery). For the problem of road intersection extraction, we applied the techniques to a set of 48 randomly selected raster maps from various sources and achieved over 90% precision with over 75% recall. These results are sufficient to automatically align raster maps with other geographic sources, which makes it possible to determine the precise coverage and scale of the raster maps.

A general approach for extracting road vector data from raster maps

Raster maps are easily accessible and contain rich road information; however, converting the road information to vector format is challenging because of varying image quality, overlapping features, and typical lack of metadata (e.g., map geocoordinates). Previous road vectorization approaches for raster maps typically handle a specific map series and require significant user effort. In this paper, we present a general road vectorization approach that exploits common geometric properties of roads in maps for processing heterogeneous raster maps while requiring minimal user intervention. In our experiments, we compared our approach to a widely used commercial product using 40 raster maps from 11 sources. We showed that overall our approach generated high-quality results with low redundancy with considerably less user input compared with competing approaches.

Recognition of Multi-oriented, Multi-sized, and Curved Text

Text recognition is difficult from documents that contain multi-oriented, curved text lines of various character sizes. This is because layout analysis techniques, which most optical character recognition (OCR) approaches rely on, do not work well on unstructured documents with non-homogeneous text. Previous work on recognizing non-homogeneous text typically handles specific cases, such as horizontal and/or straight text lines and single-sized characters. In this paper, we present a general text recognition technique to handle non-homogeneous text by exploiting dynamic character grouping criteria based on the character sizes and maximum desired string curvature. This technique can be easily integrated with classic OCR approaches to recognize non-homogeneous text. In our experiments, we compared our approach to a commercial OCR product using a variety of raster maps that contain multi-oriented, curved and straight text labels of multi-sized characters. Our evaluation showed that our approach produced accurate text recognition results and outperformed the commercial product at both the word and character level accuracy.

Automatic extraction of road intersection position, connectivity, and orientations from raster maps

The road network is one of the most important types of information on raster maps. In particular, the set of road intersection templates, which consists of the road intersection positions, the road connectivities, and the road orientations, represents an abstraction of the road network and is more accurate and easier to extract than the extraction of the entire road network. To extract the road intersection templates from raster maps, the thinning operator is commonly used to find the basic structure of the road lines (i.e., to extract the skeletons of the lines). However, the thinning operator produces distorted lines near line intersections, especially at the T-shaped intersections. Therefore, the extracted position of the road intersection and the road orientations are not accurate. In this paper, we utilize our previous work on automatically extracting road intersection positions to identify the road lines that intersect at the intersections and then trace the road orientations and refine the positions of the road intersections. We compare the proposed approach with the usage of the thinning operator and show that our proposed approach extracts more accurate road intersection positions and road orientations than the previous approach.

Recognizing text in raster maps

Text labels in maps provide valuable geographic information by associating place names with locations. This information from historical maps is especially important since historical maps are very often the only source of past information about the earth. Recognizing the text labels is challenging because heterogeneous raster maps have varying image quality and complex map contents. In addition, the labels within a map do not follow a fixed orientation and can have various font types and sizes. Previous approaches typically handle a specific type of map or require intensive manual work. This paper presents a general approach that requires a small amount of user effort to semi-automatically recognize text labels in heterogeneous raster maps. Our approach exploits a few examples of text areas to extract text pixels and employs cartographic labeling principles to locate individual text labels. Each text label is then rotated automatically to horizontal and processed by conventional OCR software for character recognition. We compared our approach to a state-of-art commercial OCR product using 15 raster maps from 10 sources. Our evaluation shows that our approach enabled the commercial OCR product to handle raster maps and together produced significant higher text recognition accuracy than using the commercial OCR alone.

Geodec: Enabling Geospatial Decision Making

The rapid increase in the availability of geospatial data has motivated the effort to seamlessly integrate this information into an information-rich and realistic 3D environment. However, heterogeneous data sources with varying degrees of consistency and accuracy pose a challenge to such efforts. We describe the geospatial decision making (GeoDec) system, which accurately integrates satellite imagery, three-dimensional models, textures and video streams, road data, maps, point data and temporal data. The system also includes a glove-based user interface

A Method for Automatically Extracting Road Layers from Raster Maps

To exploit the road network in raster maps, the first step is to extract the pixels that constitute the roads and then vectorize the road pixels. Identifying colors that represent roads in raster maps for extracting road pixels is difficult since raster maps often contain numerous colors due to the noise introduced during the processes of image compression and scanning. In this paper, we present an approach that minimizes the required user input for identifying the road colors representing the road network in a raster map. We can then use the identified road colors to extract road pixels from the map. Our approach can be used on scanned and compressed maps that are otherwise difficult to process automatically and tedious to process manually. We tested our approach with 100 maps from a variety of sources, which include 90 scanned maps with various compression levels and 10 computer generated maps. We successfully identified the road colors and extracted the road pixels from all test maps with fewer than four user labels per map on average.

Classification of Line and Character Pixels on Raster Maps Using Discrete Cosine Transformation Coefficients and Support Vector Machine

Raster maps are widely available on the Internet. Valuable information such as street lines and labels, however, are all hidden in the raster format. To utilize the information, it is important to recognize the line and character pixels for further processing. This paper presents a novel algorithm using 2D discrete cosine transformation (DCT) coefficients and support vector machines (SVM) to classify the pixels of lines and characters on raster maps. The experiment results show that our algorithm achieves 98% precision and 85% recall in classifying the line pixels and 83% precision and 96% recall in classifying the character pixels on a variety of raster map sources