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Raster Map Image Analysis
Abstract
Raster map images (e.g., USGS) provide much information in digital form; however, the color assignments and pixel labels leave many serious ambiguities. A color histogram classification scheme is described, followed by the application of a tensor voting method to classify linear features in the map as well as intersections in linear feature networks. The major result is an excellent segmentation of roads, and road intersections are detected with about 93% recall and 66 % precision.
... Frequently, such data transformation process is based on computer vision algorithms (Chiang et al. 2005, Henderson and Linton 2009, Henderson 2014, however setting optimal parameters in such algorithms requires experience, and, therefore, using them is not an option for users who are non-experts in the field of computer vision (Ball et al. 2017, Uhl et al. 2018. Moreover, due to low quality of many historical maps and the high rate of overlap of graphical features, the accuracy of the conversion is often low (Chiang et al. 2009, Pezeshk andTutwiler 2011). ...
... According to Table 1 it can be concluded that the precision of Faster RCNN framework in detecting the intersections is higher than the method of Henderson and Linton (2009) and lower than the one described in Henderson (2014) for the maps with single line symbols of the roads. On the other hand, the recall value of the deep learning method shows the converse analogy. ...
Road intersections data have been used across different geospatial applications and analysis. The road network datasets dating from pre-GIS years are only available in the form of historical printed maps. Before they can be analyzed by a GIS software, they need to be scanned and transformed into the usable vector-based format. Due to the great bulk of scanned historical maps, automated methods of transforming them into digital datasets need to be employed. Frequently, this process is based on computer vision algorithms. However, low conversion accuracy for low quality and visually complex maps and setting optimal parameters are the two challenges of using those algorithms. In this paper, we employed the standard paradigm of using deep convolutional neural network for object detection task named region-based CNN for automatically identifying road intersections in scanned historical USGS maps of several U.S. cities. We have found that the algorithm showed higher conversion accuracy for the double line cartographic representations of the road maps than the single line ones. Also, compared to the majority of traditional computer vision algorithms RCNN provides more accurate extraction. Finally, the results show that the amount of errors in the detection outputs is sensitive to complexity and blurriness of the maps as well as the number of distinct RGB combinations within them.
... Gap filling in linear feature is an issue in digital image processing and computer vision. Gap filling is used in various fields such as medical image processing (Szymczak, 2005;Risser, 2008;Akhras, 2007), linear feature extraction from raster maps (Khotanzad and zink, 2003;Pouderoux and Spinello, 2007;Chiang, et al., 2005;Chiang, et al., 2008;Linton, 2009;Henderson and Linton, 2009) and remotely sensed data. Roads are the most important group of linear feature. ...
... There are some techniques which have widely used in gap filling process. Different shapes of operators of mathematical morphology (Zhang, et al., 1999;Chiang, et al., 2005;Chiang, et al., 2008;Mountrakis and Luo, 2011;Maurya, et al., 2011 ) Active contour (Rochery, et al., 2004;Rochery, et al., 2005;Rochery, et al., 2006;Rochery, et al., 2007) and tensor voting (Linton, 2009;Henderson and Linton, 2009) are among them. Also, several algorithms have been innovatively designed by researchers. ...
Road information has a key role in many applications such as transportation, automatic navigation, traffic management, crisis
management, and also to facilitate and accelerate updating databases in a GIS. Therefore in the past two decades, automatic road
extraction has become an important issue in remote sensing, photogrammetry and computer vision. An essential challenge in road
extraction process is filling the gaps which have appeared due to getting placed under trees, tunnels or any other reason. Connection
of roads is a momentous topological property that is necessity to perform most of the spatial analyses. Hence, Gap filling is an
important post-process. The main aim of this paper is to provide a method which is applicable in road extraction algorithms to
automatic fill the gaps. The proposed algorithm is based on Radon transformation and has four stags. In the first stage, detected road
are thinned insofar as one pixel width is achieved. Then endpoints are detected. In the second stage, regarding to some constraints
those endpoints which do not belong to any gaps are identified and deleted from endpoints list. In the third stage, the real gaps are
found using the road direction computed by used of Radon technique. In fourth stage, the selected endpoints are connected together
using Spline interpolation. This algorithm is applied on several datasets and also on a real detected road. The experimental results
show that the proposed algorithm has good performance on straight roads but it does not work well in intersections, due to being
direction-oriented.
... Mostly, the described systems were unable to process different types of maps and thus the focus was rather narrow (e.g., extraction of geographic-feature layers from USGS topographic maps [Henderson et al. 2009]). Most studies focus on particular features, symbols, or map layers; therefore, efforts to extract map contents have been highly map specific, not applicable to a broader range of map products. ...
... In addition, text objects are often overlapping with other features in the map (e.g., Kasturi and Alemany [1988], Luo and Kasturi [1998], Yin and Huang [2001], Cao and Tan [2002], Chiang and Knoblock [2011]). —Hydrographical map features (in most cases the blue layer) include streams that are commonly represented as linear or area features depending on the map scale [Ebi et al. 1994; Angulo and Serra 2003; Dhar and Chanda 2006; Henderson et al. 2009] or wetland areas often shown as a spatial distribution of single or clustered symbols [Leyk and Boesch 2010]. —Building symbols are in most cases small rectangular area features or can be elongated shapes (in urban areas with higher building densities); typically, they are part of the black map layer (e.g., Fayek and Wong [1996], Frischknecht and Kanani [1998], Liu [2002], Angulo and Serra [2003]). ...
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.
... In order to ensure stable and accurate navigation and prevent the loss of position information, the topological map [22] representation is excluded. After the above comprehensive consideration, the grid map [23] representation is used to build the robot environment map. ...
Staff safety is not assured due to the indoor substation’s high environmental risk factor. The Chinese State Grid Corporation has been engaged in the intelligentization of substations and the employment of robots for inspection tasks. The autonomous navigation and positioning system of the mobile chassis is the most important feature of this type of robot, as it allows the robot to perceive the surrounding environment information at the initial position using its own sensors and find a suitable path to move to the target point to complete the task. Automatic navigation is the basis for the intelligentization of indoor substation robots, which is of great significance to the efficient and safe inspection of indoor substations. Based on this, this paper formulates a new navigation system, and builds a chassis simulation environment in the Robot Operating System (ROS). To begin with, we develop a novel hardware and sensor-based chassis navigation system experimental platform. Secondly, to conduct the fusion of the odometer and inertial navigation data, the Extended Kalman Filter (EKF) is used. The map’s creation approach determines how the environmental map is created. The global path is scheduled with the A* algorithm, whereas the local path is scheduled with the Dynamic Window Method (DWA). Finally, the created robot navigation system is applied to an auxiliary operation robot chassis suited for power distribution cabinet switch and the navigation system’s experimental analysis is conducted using this platform, demonstrating the system’s efficacy and practicability.
... Such recent efforts include the mining of (historical) map collections by their content or associated metadata [32][33][34][35][36][37], automated georeferencing [18,[38][39][40] and alignment [41,42], text detection and recognition [43][44][45], and the extraction of thematic map content, often involving (deep) machine learning methods, focusing on specific geographic features such as forest [46], railroads [33,47], road network intersections [48,49] and road types [50], archeological content [51] and mining features [52], cadastral parcels boundaries [53,54], wetlands and other hydrographic features [55,56], linear features in general [57], land cover/land use [58], urban street networks and city blocks [34], building footprints [13,59,60], and historical human settlement patterns [61][62][63]. Other approaches use deep-learning-based computer vision for generic segmentation of historical maps [64,65], generative machine learning approaches for map style transfer [66,67], or attempt to mimic historical overhead imagery based on historical maps [68]. ...
Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.
... where and are visible in Figure 1a , is the scale of voting which determines the range within which tokens can influence each other and is a parameter which is a function of and controls the relative weight of path length and curvature. The interested reader can find more details of tensor voting in (Maboudi et al., 2016, Kang and Medioni, 2005, Henderson and Linton, 2009) . ...
... where and are visible in Figure 1a , is the scale of voting which determines the range within which tokens can influence each other and is a parameter which is a function of and controls the relative weight of path length and curvature. The interested reader can find more details of tensor voting in (Maboudi et al., 2016, Kang and Medioni, 2005, Henderson and Linton, 2009) . ...
High quality and updated road network maps provide important information for many domains. Many small segments appear on the road surface in VHR images. Most road extraction systems have problem in extraction of these small segments and usually they appear as gaps in the final extracted road networks. However, most approaches skip filling these gaps. This is on account of the fact that usually overall length of the missing parts of the road extraction results is very short relative to the total length of the whole road network. This leads to an indiscernible impact of filling these gaps on geometrical quality criteria. In this paper, using two different VHR satellite datasets and a gap-filling approach which is based on tensor voting, we show that utilizing an effective road gap filling can result in a quite tangible topological improvement in the final road network which is highly demanded in many applications.
... Moreover, heatmap recognition has not been extensively researched. Relevant work in the related area of map recognition includes the use of knowledge of the colourisation schemes in maps for automatically segmenting them based on their semantic contents (e.g., roads) [9], and the development of techniques for improving segmentation quality of text and graphics in colour maps through the cleaning up of possible errors (e.g., dashed lines) [2]. Map recognition has also been investigated at TRECVID (http://trecvid. ...
This work proposes a framework for the discovery of environmental Web resources providing air quality measurements and forecasts. Motivated by the frequent occurrence of heatmaps in such Web resources, it exploits multimedia evidence at different stages of the discovery process. Domain-specific queries generated using empirical information and machine learning driven query expansion are submitted both to the Web and Image search services of a general-purpose search engine. Post-retrieval filtering is performed by combining textual and visual (heatmap-related) evidence in a supervised machine learning framework. Our experimental results indicate improvements in the effectiveness when performing heatmap recognition based on SURF and SIFT descriptors using VLAD encoding and when combining multimedia evidence in the discovery process.
... Raster maps often contain a high quantity of elements overlapping with the roads, hindering the growth of the tree and it would require a prohibitive amount of seeds. Hai et al. 18) and Herderson et al. 19) are using predetermined color patterns to identify the roads. Even if those two methods have the advantage of requiring no user input during the online phase, there is a limitation in the variety of usable maps. ...
This paper presents a method to detect and track a paper map and overlay virtual data over it. First, we take a picture of the map and identify the road colors using our road area extraction method. Using the color information we can then extract in real time the roads from a video and use the position of the intersections to identify the map from a database without any further user input. The main issue is to get reliable intersection positions in real time even though the map can contain a lot of overlapping data. Finally, using the location of the intersections we can track and augment the map with virtual information. Our method enables the real time matching, 3D tracking and augmentation of a paper map based on a database that can contain several thousands of intersections coordinates.
... Finally, the authors use GEOPPM, an algorithm for automatically determining the geocoordinates and scale of the maps. In another similar work (Henderson and Linton, 2009), the authors use the specific knowledge of the known colorization in USGS maps, to automatically segment these maps based on their semantic contents (e.g. roads, rivers). ...
Environmental data analysis and information provision are considered of great importance for people, since environmental conditions are strongly related to health issues and directly affect a variety of everyday activities. Nowadays, there are several free web-based services that provide environmental information in several formats with map images being the most commonly used to present air quality and pollen forecasts. This format, despite being intuitive for humans, complicates the extraction and processing of the underlying data. Typical examples of this case are the chemical weather forecasts, which are usually encoded heatmaps (i.e. graphical representation of matrix data with colors), while the forecasted numerical pollutant concentrations are commonly unavailable. This work presents a model for the semi-automatic extraction of such information based on a template configuration tool, on methodologies for data reconstruction from images, as well as on text processing and Optical Character Recognition (OCR). The aforementioned modules are integrated in a standalone framework, which is extensively evaluated by comparing data extracted from a variety of chemical weather heat maps against the real numerical values produced by chemical weather forecasting models. The results demonstrate a satisfactory performance in terms of data recovery and positional accuracy.
... Khotanzad et al. in [6] utilize color segmentation with user input to extract contour lines in USGS topographic maps. Henderson et al. in [7] have a method using a preset color index to produce a set of rules for classification of the pixels. Or later, Chiang et al. in [1] are also using a color segmentation followed by K-mean algorithm to reduce the number of different colors to 15. ...
Raster maps are widely available in the everyday life, and can contain a
huge amount of information of any kind using labels, pictograms, or
color code e.g. However, it is not an easy task to extract roads from
those maps due to those overlapping features. In this paper, we focus on
an automated method to extract roads by using linear features detection
to search for seed points having a high probability to belong to roads.
Those linear features are lines of pixels of homogenous color in each
direction around each pixel. After that, the seeds are then expanded
before choosing to keep or to discard the extracted element. Because
this method is not mainly based on color segmentation, it is also
suitable for handwritten maps for example. The experimental results
demonstrate that in most cases our method gives results similar to usual
methods without needing any previous data or user input, but do need
some knowledge on the target maps; and does work with handwritten maps
if drawn following some basic rules whereas usual methods fail.
... This is still a difficult problem, although various techniques have been proposed in the past [1,2,6]. We have worked on road segmentation and road intersection detection [4,5]. ...
The automatic classication of semantic classes (background, vegetation, roads, water, political bound- aries, iso-contours) in raster map images still poses signicant challenges. We describe and compare the results of three unsupervised classication algorithms: (1) k-means, (2) graph theoretic (GT), and (3) ex- pectation maximization (EM). These are applied to USGS raster map images, and performance is measured in terms of the recall and precision as well as the cluster quality on a set of map images for which the ground truth is available. Across the six classes studied here, k-means achieves good clusters and an average of 78% recall and 70% precision; GT clustering achieves good clusters and 83% recall with 74% precision, Finally, EM forms very good clusters and has an average 86% recall and 71% precision.
... Only the lines are processed. The work in [11] makes use of the specific knowledge of the known colorization in USGS maps, to have the ability to automatically segment these maps based on their semantic contents (e.g. roads, rivers). ...
Analysis of environmental information is considered of utmost importance for humans, since environmental conditions are strongly related to health issues and to a variety of everyday activities. Despite the fact that there are already many free on-line services providing environmental information, there are several cases, in which the presentation format complicates the extraction and processing of such data. A very characteristic example is the air quality forecasts, which are usually encoded in image maps of heterogeneous formats, while the initial (numerical) pollutant concentrations, calculated and predicted by a relevant model, remain unavailable. This work addresses the task of semi-automatic extraction of such information based on a template configuration tool, on methodologies for data reconstruction from images, as well as on Optical Character Recognition (OCR) techniques. The framework is tested with a number of air quality forecast heatmaps demonstrating satisfactory results.
... In [5] the authors use the specific knowledge of the known colorization in USGS maps, to automatically segment these maps based on their semantic contents (e.g. roads, rivers). ...
Extraction and analysis of environmental information is very important, since it strongly affects everyday life. Nowadays there are already many free services providing environmental information in several formats including multimedia (e.g. map images). Although such presentation formats might be very informative for humans, they complicate the automatic extraction and processing of the underlying data. A characteristic example is the air quality and pollen forecasts, which are usually encoded in image maps, while the initial (numerical) pollutant concentrations remain unavailable. This work proposes a framework for the semi-automatic extraction of such information based on a template configuration tool, on Optical Character Recognition (OCR) techniques and on methodologies for data reconstruction from images. The system is tested with a different air quality and pollen forecast heatmaps demonstrating promising results.
... Land map images are typically encoded in such a way that semantic features are difficult to extract due to noise, error or overlapping features. A color histogram classification scheme was described in [9], followed by the application of a tensor voting method to classify linear features in the map as well as intersections in linear feature networks. ...
Paper land maps are useful documents for collecting geographical information. Land map images provide information like place names, country or states borders etc. A land map image can be considered as a complex document image as the texts may have the myriad background consists of various intensity values and different orientations. Text orientations, fonts and sizes may not unique for all place names within same map images. Text segmentation from such images is not an easy task. This paper presents an approach for text segmentation from scanned land map images using Radon transform based projection profile. Using this projection profile, localization of the text positions are done. Then Euler number of each component is computed for eliminating the non-text components. The proposed approach is tested on a set of collected map images (containing texts in Indian regional scripts; like Bangla) and it gives an encouraging result.
... Image denoising is therefore needed for accurate conversion of these older maps into raster format. This preprocess can be crucial for the later raster map analysis step, when extracting the semantic content (roads, contours, river) on a map [31]–[34] . Image denoising can also be applied as a preprocessing when converting a raster map into a vector format. ...
Filtering of raster map images is chosen as a case study of a more general class of palette-indexed images for the denoising problem of images with a discrete number of output colors. Statistical features of local context are analyzed to avoid damage to pixel-level patterns, which is frequently caused by conventional filters. We apply a universal statistical filter using context-tree modeling via a selective context expansion cap- turing those pixel combinations that are present in the image. The selective context expansion makes it possible to use a much larger spatial neighborhood, with a feasible time and memory complexity, than fixed-size templates. We improve the existing context-tree approaches in two aspects: Firstly, in order to cir- cumvent the context contamination problem, a context-merging strategy is applied where multiple similar contexts are considered in the conditional probability estimation procedure. Secondly, we study a specific continuous-input-finite-output problem in which the map images are corrupted by additive Gaussian noise. Perfor- mance comparisons with competitive filters demonstrate that the proposed algorithm provides robust noise filtering performance and good structure preservation in all test cases without any a priori information on the statistical properties of the noise.
The occurrence of extreme hydrological flood events has become frequent recently, especially in India. Flood analysis methods need to capture variations in trends and identify key factors causing this variation. Non-stationary flood frequency analysis is one of the few methods which can account for these variations. In this study, the non-stationarity of Periyar river discharge was investigated through the flood frequency analysis of one stationary and 12 non-stationary models, produced using Generalized Extreme Value distribution with annual precipitation, urban extent and time as a linear function of its location and scale parameter. The analysis explained that both, climate change and anthropogenic activities are accountable for the non-stationarity of the Periyar river discharge. Also, the 50-year return level of the stationary model was found to be equal to the 39-year return level of the optimal non-stationary model, thus, the assumption of stationarity may lead to the unsafe design of structures.
Maps contain abundant geospatial information, such as roads, settlements, and river networks, to name a few. The need to access this information to carry out analyses (e.g., in transportation, landscape planning, or ecology), as well as advances in software and hardware technologies, have driven the development of workflows to efficiently extract features from maps. The aim of this article is to provide a comprehensive overview of such methods to extract road features from raster maps. The methods are categorized based on the classes of techniques they employ (e.g., line extraction), as well as their subclasses (e.g., line tracing, Hough transform), the amount of user intervention required (e.g., interactive, automatic), the required data (e.g., scanned maps, contemporary vector data) and the produced results (e.g., raster‐based predictions, vector‐based results, attributes). Additionally, recent road extraction methods from overhead imagery, together with evaluation methods that will possibly benefit road extraction from raster maps, are reviewed. Furthermore, the evolution of this research field is analyzed over the past 35 years and the limitations of the current techniques, as well as possible future directions, are discussed.
Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems, e.g., the dynamics of the wildland-urban interface. Herein, we propose a framework that jointly uses remote sensing derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach for two U.S. study sites against historical settlement extents derived from the Historical Settlement Data Compilation for the US, HISDAC-US, achieving Area-under-the-Curve values >0.9. Our results are largely in agreement with model-based urban areas from the HYDE database, and demonstrate that the integration of remote sensing derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization, and long-term land cover change in countries where historical maps are available.
Road intersection data have been used across a range of geospatial analyses. However, many datasets dating from before the advent of GIS are only available as historical printed maps. To be analyzed by GIS software, they need to be scanned and transformed into a usable (vector-based) format. Because the number of scanned historical maps is voluminous, automated methods of digitization and transformation are needed. Frequently, these processes are based on computer vision algorithms. However, the key challenges to this are (1) the low conversion accuracy for low quality and visually complex maps, and (2) the selection of optimal parameters. In this paper, we used a region-based deep convolutional neural network-based framework (RCNN) for object detection, in order to automatically identify road intersections in historical maps of several cities in the United States of America. We found that the RCNN approach is more accurate than traditional computer vision algorithms for double-line cartographic representation of the roads, though its accuracy does not surpass all traditional methods used for single-line symbols. The results suggest that the number of errors in the outputs is sensitive to complexity and blurriness of the maps, and to the number of distinct red-green-blue (RGB) combinations within them.
Historical maps store abundant and valuable information about the evolution of natural features and human activities, such as changes in hydrography, the development of the railroad networks, and the expansion of human settlements. Such knowledge represents a unique resource that can be extremely useful for researchers in the social and natural sciences to better understand how human and environment have evolved over time. Fortunately, a large amount of historical maps have been scanned in high resolution by many organizations. For example, the United States Geological Survey (USGS) has scanned and released more than 200,000 historical maps in the TIFF format.
With large amounts of digital map archives becoming available, the capability to automatically extracting information from historical maps is important for many domains that require long-term geographic data, such as understanding the development of the landscape and human activities. In the previous work, we built a system to automatically recognize geographic features in historical maps using Convolutional Neural Networks (CNN). Our system uses contemporary vector data to automatically label examples of the geographic feature of interest in historical maps as training samples for the CNN model. The alignment between the vector data and geographic features in maps controls if the system can generate representative training samples, which has a significant impact on recognition performance of the system. Due to the large number of training data that the CNN model needs and tens of thousands of maps needed to be processed in an archive, manually aligning the vector data to each map in an archive is not practical. In this paper, we present an algorithm that automatically aligns vector data with geographic features in historical maps. Existing alignment approaches focus on road features and imagery and are difficult to generalize for other geographic features. Our algorithm aligns various types of geographic features in document images with the corresponding vector data. In the experiment, our alignment algorithm increased the correctness and completeness of the extracted railroad and river vector data for about 100% and 20%, respectively. For the performance of feature recognition, the aligned vector data had a 100% improvement on the precision while maintained a similar recall.
Converting geographic features (e.g., place names) in map images into a vector format is the first step for incorporating cartographic information into a geographic information system (GIS). With the advancement in computational power and algorithm design, map processing systems have been considerably improved over the last decade. However, the fundamental map processing techniques such as color image segmentation, (map) layer separation, and object recognition are sensitive to minor variations in graphical properties of the input image (e.g., scanning resolution). As a result, most map processing results would not meet user expectations if the user does not “properly” scan the map of interest, pre-process the map image (e.g., using compression or not), and train the processing system, accordingly. These issues could slow down the further advancement of map processing techniques as such unsuccessful attempts create a discouraged user community, and less sophisticated tools would be perceived as more viable solutions. Thus, it is important to understand what kinds of maps are suitable for automatic map processing and what types of results and process-related errors can be expected. In this paper, we shed light on these questions by using a typical map processing task, text recognition, to discuss a number of map instances that vary in suitability for automatic processing. We also present an extensive experiment on a diverse set of scanned historical maps to provide measures of baseline performance of a standard text recognition tool under varying map conditions (graphical quality) and text representations (that can vary even within the same map sheet). Our experimental results help the user understand what to expect when a fully or semi-automatic map processing system is used to process a scanned map with certain (varying) graphical properties and complexities in map content.
Beginning in 1879 the United States Geological Survey (USGS) began surveying land in the United States. Since then they have developed over 55, 000 1 : 24, 000-scale topographic maps covering the 48 coterminous states in a standard, detailed manner. The result is a wealth of data contained in physical documents. Unfortunately many of these documents over the years have begun to deteriorate.
The automatic classification of semantic classes (background, vegetation, roads, water, political boundaries, iso-contours) in raster map images still poses significant challenges. We describe and compare the results of three unsupervised classification algorithms: (1) k-means, (2) graph theoretic (GT), and (3) expectation maximization (EM). These are applied to USGS raster map images, and performance is measured in terms of the recall and precision as well as the cluster quality on a set of map images for which the ground truth is available. Across the six classes studied here, k-means achieves good clusters and an average of 78% recall and 70% precision; GT clustering achieves good clusters and 83% recall with 74% precision. Finally, EM forms very good clusters and has an average 86% recall and 71% precision.
Raster maps are widely available in the everyday life, and can contain a huge amount of information of any kind using labels, pictograms, or color code e.g. But due to those overlapping features, it's not an easy task to extract roads from those maps. Many methods use user input to achieve this goal. In this paper we focus on an automated method to extract roads by using color segmentation and linear features detection to search for seed points having a high probability to belong to roads. Those seeds are then expanded before choosing to keep or to discard the extracted element.
In this chapter, we describe an automatic procedure to capture features on old maps. Early maps contain specific informations which allow us to reconstruct trajectories over time and space for land use/cover studies or urban area development. The most commonly used approach to extract these elements requires a user intervention for digitizing which widely limits its utilization. Therefore, it is essential to propose automatic methods in order to establish reproducible procedures. Capturing features automatically on scanned paper maps is a major challenge in GIS for many reasons: (1) many planimetric elements can be overlapped, (2) scanning procedure may conduct to a poor image quality, (3) lack of colors complicates the distinction of the elements. Based on a state of art, we propose a method based on color image segmentation and unsupervised classification (K-means algorithm) to extract forest features on the historical 'Map of France'. The first part of the procedure conducts to clean maps and eliminate elevation contour lines with filtering techniques. Then, we perform a color space conversion from RGB to L*a*b color space to improve uniformity of the image. To finish, a post processing step based on morphological operators and contextual rules is applied to clean-up features. Results show a high global accuracy of the proposed scheme for different excerpt of this historical map.
This book covers up-to-date methods and algorithms for the automated analysis of engineering drawings and digital cartographic maps. The Non-Deterministic Agent System (NDAS) offers a parallel computational approach to such image analysis. The book describes techniques suitable for persistent and explicit knowledge representation for engineering drawings and digital maps. It also highlights more specific techniques, e.g., applying robot navigation and mapping methods to this problem. Also included are more detailed accounts of the use of unsupervised segmentation algorithms to map images. Finally, all these threads are woven together in two related systems: NDAS and AMAM (Automatic Map Analysis Module).
Environmental data are considered of utmost importance for human life, since weather conditions, air quality and pollen are strongly related to health issues and affect everyday activities. This paper addresses the problem of discovery of air quality and pollen forecast Web resources, which are usually presented in the form of heatmaps (i.e. graphical representation of matrix data with colors). Towards the solution of this problem, we propose a discovery methodology, which builds upon a general purpose search engine and a novel post processing heatmap recognition layer. The first step involves generation of domain-specific queries, which are submitted to the search engine, while the second involves an image classification step based on visual low level features to identify Web sites including heatmaps. Experimental results comparing various visual features combinations show that relevant environmental sites can be efficiently recognized and retrieved.
Focussed crawlers enable the automatic discovery of Web resources about a given topic by automatically navigating the Web link structure and selecting the hyperlinks to follow by estimating their relevance to the topic based on evidence obtained from the already downloaded pages. This work proposes a classifier-guided focussed crawling approach that estimates the relevance of a hyperlink to an unvisited Web resource based on the combination of textual evidence representing its local context, namely the textual content appearing in its vicinity in the parent page, with visual evidence associated with its global context, namely the presence of images relevant to the topic within the parent page. The proposed focussed crawling approach is applied towards the discovery of environmental Web resources that provide air quality measurements and forecasts, since such measurements (and particularly the forecasts) are not only provided in textual form, but are also commonly encoded as multimedia, mainly in the form of heatmaps. Our evaluation experiments indicate the effectiveness of incorporating visual evidence in the link selection process applied by the focussed crawler over the use of textual features alone, particularly in conjunction with hyperlink exploration strategies that allow for the discovery of highly relevant pages that lie behind apparently irrelevant ones.
Sketch maps are an intuitive way to display and communicate geographic data and an automatic processing is of great benefit for human-computer interaction. This paper presents a method for segmentation of sketch map objects as part of the sketch map understanding process. We use region-based segmentation that is robust to gaps in the drawing and can even handle open-ended streets. To evaluate this approach, we manually generated a ground truth for 20 maps and conducted a preliminary quantitative performance study.
The information in a document comes in many forms; this includes texts, tables, charts, symbols, maps, logos, stamps, photographs etc. All these forms may be combined in suitable forms to create a document. Paper land maps are useful documents for collection of geographical information. A land map image can be considered as a complex document image as the texts contained there may have a complex background consists of various intensity values and varied orientations. This paper presents two methods for text extraction from scanned land map images. In the first method we have used mathematical morphological operations for making the symbols of text be connected. Then mean and standard deviations are used for text non-text separation. In the second method we have extracted texts based on intensity analysis. The approaches are tested on a collected dataset of map images and experimental results are encouraging.
In this paper we describe a method for predicting the subjective quality of a new mountain bike route for a particular subject based on routes previously ridden and ranked by the subject. GPS tracks of the previously ridden routes are over laid on rasterized topographic maps and topographic features are extracted in the vicinity of the routes using image processing techniques. The subject ranks each previously ridden route segment on four subjective qualities. The extracted topographic features and the subjective rankings are used as input vectors and target vectors to train a series of decision trees. The decision trees are then tested on a series of route segments not used in the decision tree training. The decision trees were able to exactly predict the subjective rankings with over 60% accuracy vs. 20% accuracy for random selection. When close matches are allowed in the prediction of subjective ranking (plus or minus one point vs. actual) the accuracy of the decision trees increased to 90% and above.
The extraction of semantic features from images of geographic maps is a difficult and interesting problem. Such features may be robustly segmented through the use of Gestalt principles such as similarity and continuity as realized through the use of tensor voting methods and color histogram analysis, respectively. A framework is developed implementing these Gestalt principles through various algorithms. Linear feature segmentation and intersection detection methods are given, and their performance is demonstrated on a set of real and synthetic map images. Master of Science;
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