Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning

ABSTRACT

With large amounts of digital map archives becoming available, automatically extracting information from scanned historical maps is needed for many domains that require long-term historical geographic data. Convolutional Neural Networks (CNN) are powerful techniques that can be used for extracting locations of geographic features from scanned maps if sufficient representative training data are available. Existing spatial data can provide the approximate locations of corresponding geographic features in historical maps and thus be useful to annotate training data automatically. However, the feature representations, publication date, production scales, and spatial reference systems of contemporary vector data are typically very different from those of historical maps. Hence, such auxiliary data cannot be directly used for annotation of the precise locations of the features of interest in the scanned historical maps. This research introduces an automatic vector-to-raster alignment algorithm based on reinforcement learning to annotate precise locations of geographic features on scanned maps. This paper models the alignment problem using the reinforcement learning framework, which enables informed, efficient searches for matching features without pre-processing steps, such as extracting specific feature signatures (e.g. road intersections). The experimental results show that our algorithm can be applied to various features (roads, water lines, and railroads) and achieve high accuracy.     

A probabilistic relaxation approach for matching road networks

Geospatial data matching is an important prerequisite for data integration, change detection and data updating. At present, crowdsourcing geospatial data are attracting considerable attention with its significant potential for timely and cost-effective updating of geospatial data and Geographical Information Science (GIS) applications. To integrate the available and up-to-date information of multi-source geospatial data, this article proposes a heuristic probabilistic relaxation road network matching method. The proposed method starts with an initial probabilistic matrix according to the dissimilarities in the shapes and then integrates the relative compatibility coefficient of neighbouring candidate pairs to iteratively update the initial probabilistic matrix until the probabilistic matrix is globally consistent. Finally, the initial 1:1 matching pairs are selected on the basis of probabilities that are calculated and refined on the basis of the structural similarity of the selected matching pairs. A process of matching is then implemented to find M:N matching pairs. Matching between OpenStreetMap network data and professional road network data shows that our method is independent of matching direction, successfully matches 1:0 (Null), 1:1 and M:N pairs, and achieves a robust matching precision of above 95%.     

Integration of linear and areal hierarchies for continuous multi-scale representation of road networks

Spatial data can be represented at different scales, and this leads to the issue of multi-scale spatial representation. Multi-scale spatial representation has been widely applied to online mapping products (e.g., Google Maps and Yahoo Maps). However, in most current products, multi-scale representation can only be achieved through a series of maps at fixed scales, resulting in a discontinuity (i.e., with jumps) in the transformation between scales and a mismatch between the available scales and users' desired scales. Therefore, it is very desirable to achieve smoothly continuous multi-scale spatial representations. This article describes an integrated approach to build a hierarchical structure of a road network for continuous multi-scale representation purposes, especially continuous selective omission of roads in a network. In this hierarchical structure, the linear and areal hierarchies are constructed, respectively, using two existing approaches for the linear and areal patterns in a road network. Continuous multi-scale representation of a road network can be achieved by searching in these hierarchies. This approach is validated by applying it to two study areas, and the results are evaluated by both quantitative analysis with two measures (i.e., similarity and average connectivity) and visual inspection. Experimental results show that this integrated approach performs better than existing approaches, especially in terms of preservation of connectivity and patterns of a road network. With this approach, efficient and continuous multi-scale selective omission of road networks becomes feasible.     

Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks

ABSTRACT

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.     

Prediction of Anopheles minimus habitat in India—a tool for malaria management

This paper reports research to predict the distribution of An. minimus, a malaria vector in forest fringe areas using GIS to support precision surveys for malaria control. Because An. minimus is a forest‐associated species, generalized thematic maps (1:6?000?000) of forest cover, soil type, altitude, rainfall and temperature were used. Digitization, overlaying, integration and analysis of thematic maps were done using Arc/Info 8.1 NT and Arc/View 3.2 (GIS, ESRI) software. GIS delineated favourable areas for An. minimus where the species is likely to be found, and precision surveys can be conducted. Precision field surveys in selected locations of favourable/non‐favourable areas were carried out. The species could be found in all locations designated as a favourable area and was absent in non‐favourable areas. In two districts, one where the species is reported to have disappeared in the early 1950s and the other where it was not reported in earlier surveys, GIS helped in precision surveys, and An. minimus was found. The technique can quickly cover vast and inaccessible areas and is easy to duplicate in other parts of the world to assist cost‐effective control of malaria. It can also delineate areas favourable for any species of flora and fauna to help precision surveys.     

Personalizing map content to improve task completion efficiency

Significant interaction challenges arise in both developing and using interactive map applications. Users encounter problems of information overload in using interactive maps to complete tasks. This is further exacerbated by device limitations and interaction constraints in increasingly popular mobile platforms. Application developers must then address restrictions related to screen size and limited bandwidth in order to effectively display maps on mobile devices. In order to address issues of user information overload and application efficiency in interactive map applications, we have developed a novel approach for delivering personalized vector maps. Ongoing task interactions between users and maps are monitored and captured implicitly in order to infer individual and group preferences related to specific map feature content. Personalized interactive maps that contain spatial feature content tailored specifically to users' individual preferences are then generated. Our approach addresses spatial information overload by providing only the map information necessary and sufficient to suit user interaction preferences, thus simplifying the completion of tasks performed with interactive maps. In turn, tailoring map content to specific user preferences considerably reduces the size of vector data sets necessary to transmit and render maps on mobile devices. We have developed a geographic information system prototype, MAPPER (MAP PERsonalization), that implements our approach. Experimental evaluations show that the use of personalized maps helps users complete their tasks more efficiently and can reduce information overload.     

Progressive vector compression for high-accuracy vector map data

With the increase in the number of applications using digital vector maps and the development of surveying techniques, a large volume of GIS (geographic information system) vector maps having high accuracy and precision is being produced. However, to achieve their effective transmission while preserving their high positional quality, these large amounts of vector map data need to be compressed. This paper presents a compression method based on a bin space partitioning data structure, which preserves a high-level accuracy and exact precision of spatial data. To achieve this, the proposed method a priori divides a map into rectangular local regions and classifies the bits of each object in the local regions to three types of bins, defined as category bin (CB), direction bin (DB), and accuracy bin (AB). Then, it encodes objects progressively using the properties of the classified bins, such as adjacency and orientation, to obtain the optimum compression ratio. Experimental results verify that our method can encode vector map data constituting less than 20% of the original map data at a 1-cm accuracy degree and that constituting less than 9% at a 1-m accuracy degree. In addition, its compression efficiency is greater than that of previous methods, whereas its complexity is lower for close to real-time applications.     

Hierarchical polygonization for generating and updating lane-based road network information for navigation from road markings

Lane-based road network information, such as lane geometry, destination, lane changing, and turning information, is important in vehicle navigation, driving assistance system, and autonomous driving. Such information, when available, is mainly input manually. However, manual methods for creating and updating data are not only costly but also time-consuming, labor-intensive, and prone to long delays. This paper proposes a hierarchical polygonization method for automatic generation and updating of lane-level road network data for navigation from a road marking database that is managed by government transport department created by digitizing or extraction from aerial images. The proposed method extends the hierarchy of a road structure from ‘road–carriageway–lane’ to ‘road–carriageway–lane–basic lane’. Basic lane polygons are constructed from longitudinal road markings, and their associated navigational attributes, such as turning information and speed limit, are obtained from transverse road markings by a feature-in-polygon overlay approach. A hierarchical road network model and detailed algorithms are also illustrated in this paper. The proposed method can accelerate the process of generating and updating lane-level navigation information and can be an important component of a road marking information system for road management.     

Using Road Maps in the Junior High School

Abstract

The activities described in this paper were developed to familiarize junior high school students with the variety of information available on road maps and to provide a means for practice of map skills. At Legg Junior High School in Coldwater, Michigan, students were presented with an introduction to road maps, followed by a series of worksheets to use in conjunction with Michigan and Ontario highway maps. After completing the worksheets, students participated in road rally games which required them to use maps and follow instructions and clues to find a specific location. The activities can be adapted for elementary school classes or for students with different levels of ability. Teachers might also want to increase the number of worksheets or road rallies to give students additional practice in skills in which they particularly need improvement.     

认知地图的空间句法研究

将意识层面的空间意象外化为可被度量及分析的对象,一直是行为学、地理学、旅游学等学科关注的重要课题。本文主要从构建空间意象的结构而非意义入手,对该领域研究中的两种重要方法,即认知地图法和空间句法进行了比较,意图找到两种方法在空间结构性认知方面的相关性。基于句法空间结构的表达模型,分别就长沙太平街历史街区集成度及其认知地图的调研结果进行了比对,通过真实地图句法集成度与579 份认知地图意象要素出现频率等指标的比较,发现集成度指标与意象出现频率等指标存在较好的相关性。在对认知地图样本进行轴线化转换并比照真实地图的轴线索引后,对真实地图句法集成度与579 份认知地图平均句法集成度进行比较,发现认知地图与真实地图的句法分析在结果上仍有较好的相关性,且节点、标志物两要素的出现频次等指标也与所在街道的集成度存在较为明显的对应性。结论说明空间构形对人们结构性意象的形成有着重要的影响力,认知地图的句法分析对揭示人们空间知识的形成机制具有一定的参考价值,两种方法有相互补充的可能性。     

An effective heuristic for computing many shortest path alternatives in road networks

We propose a simple and effective heuristic that allows fast generation of a large set of shortest path alternatives in weighted directed graphs. The heuristic is based on existing deviation path algorithms for exact k shortest paths. It precalculates a backward shortest path tree and thus avoids doing many shortest path computations, but as a result it does not necessarily find the exact set of k shortest paths.

Computational results on real-world road networks are reported. Our tests show that the quality of the paths produced by the heuristic is most satisfactory: typically, the kth path found by the heuristic is less than 1% longer than the exact kth shortest path, for values of k up to 10,000. Moreover, the heuristic runs very fast.

We also show how the heuristic can be enhanced to an exact k shortest paths algorithm, which performs well in comparison with the existing exact k shortest path algorithms.     

城市三维地理信息系统中三维模型的快速构建方法

大规模三维模型的快速构建一直是影响城市三维地理信息系统(3DUGIS)发展的一个重要因素.基于3DUGIS中景观模型的表达原则与分类,分别针对抽象的点、线、面状对象提出符号匹配和三角剖分的批量三维模型构建方法.这种建模方法方便、快捷、自动化程度高,可广泛应用于城市景观中地面、河流、道路、绿地、规则建筑物、地下管线等地物的三维建模.通过对上述三维模型快速构建方法的实现,验证了三维模型生成的效果和效率.     

Fast map matching,an algorithm integrating hidden Markov model with precomputation

Wide deployment of global positioning system (GPS) sensors has generated a large amount of data with numerous applications in transportation research. Due to the observation error, a map matching (MM) process is commonly performed to infer a path on a road network from a noisy GPS trajectory. The increasing data volume calls for the design of efficient and scalable MM algorithms. This article presents fast map matching (FMM), an algorithm integrating hidden Markov model with precomputation, and provides an open-source implementation. An upper bounded origin-destination table is precomputed to store all pairs of shortest paths within a certain length in the road network. As a benefit, repeated routing queries known as the bottleneck of MM are replaced with hash table search. Additionally, several degenerate cases and a problem of reverse movement are identified and addressed in FMM. Experiments on a large collection of real-world taxi trip trajectories demonstrate that FMM has achieved a considerable single-processor MM speed of 25,000–45,000 points/second varying with the output mode. Investigation on the running time of different steps in FMM reveals that after precomputation is employed, the new bottleneck is located in candidate search, and more specifically, the projection of a GPS point to the polyline of a road edge. Reverse movement in the result is also effectively reduced by applying a penalty.     

基于局部相似的地籍宗地图斑匹配方法

地籍管理工作中,不同来源的宗地数据通常存在着诸多差异,识别和匹配不同数据源中的同名宗地图斑是提高数据更新精度与速度的关键,对地籍数据库的集成与信息共享有着非常重要的意义。该文针对地籍数据中宗地图斑的形状及变化特点,提出了一种基于局部相似的简单而有效的匹配方法来提高匹配率,基本原理是通过搜索两个面实体间最优的点对应或特征对应关系判断两个面实体是否匹配,采用形状相似度作为局部相似的判断指标。     

Automatic change detection in lane-level road networks using GPS trajectories

Lane-level road network updating is crucial for urban traffic applications that use geographic information systems contributing to, for example, intelligent driving, route planning and traffic control. Researchers have developed various algorithms to update road networks using sensor data, such as high-definition images or GPS data; however, approaches that involve change detection for road networks at lane level using GPS data are less common. This paper presents a novel method for automatic change detection of lane-level road networks based on GPS trajectories of vehicles. The proposed method includes two steps: map matching at lane level and lane-level change recognition. To integrate the most up-to-date GPS data with a lane-level road network, this research uses a fuzzy logic road network matching method. The proposed map-matching method starts with a confirmation of candidate lane-level road segments that use error ellipses derived from the GPS data, and then computes the membership degree between GPS data and candidate lane-level segments. The GPS trajectory data is classified into successful or unsuccessful matches using a set of defuzzification rules. Any topological and geometrical changes to road networks are detected by analysing the two kinds of matching results and comparing their relationships with the original road network. Change detection results for road networks in Wuhan, China using collected GPS trajectories show that these methods can be successfully applied to detect lane-level road changes including added lanes, closed lanes and lane-changing and turning rules, while achieving a robust detection precision of above 80%.     

Identifying minimal sets of survey techniques for multi-species monitoring across landscapes: an approach utilising species distribution models

Monitoring for species occupancy is often carried out at local scales, reflecting specific targets, available logistics, and funding. Problematically, conservation planning and management operate at broader scales and use information inventories with good scale coverage. Translating information between local and landscape scales is commonly treated in an ad hoc manner, but conservation decision-making can benefit from quantifying spatial-knowledge relationships. Fauna occupancy monitoring, in particular, suffers from this issue of scale, as there are many different survey methods employed for different purposes. Rather than ignoring how informative these methods are when predicting regional distributions, we describe a statistical approach that identifies survey combinations that provide the greatest additive value in mammal detection across different scales. We identified minimal sets of survey methods for 53 terrestrial mammal species across a large area in Australia (New South Wales (NSW), 800,000 km²) and for each of the 18 bioregions it encompasses. Utility of survey methods varied considerably at a landscape scale. Unplanned opportunistic sightings were the single largest source of species information (35%). The utility of other survey methods varied spatially; some were retained in minimal sets for many bioregions, while others were spatially restricted or unimportant. Predator scats, Elliot and pitfall trapping, spotlighting, and diurnal herpetofauna surveys were the most frequently included survey methods at a landscape scale. Use of our approach can guide identification of efficient combinations of survey methods, maximising detection and returns for monitoring. Findings and methodologies are easily transferable and are globally applicable across any taxa. They provide guidelines for managing scarce resources for regional ?monitoring programs, and improving regional strategic ?conservation planning.     

Geometric-based approach for integrating VGI POIs and road networks

Integrating heterogeneous spatial data is a crucial problem for geographical information systems (GIS) applications. Previous studies mainly focus on the matching of heterogeneous road networks or heterogeneous polygonal data sets. Few literatures attempt to approach the problem of integrating the point of interest (POI) from volunteered geographic information (VGI) and professional road networks from official mapping agencies. Hence, the article proposes an approach for integrating VGI POIs and professional road networks. The proposed method first generates a POI connectivity graph by mining the linear cluster patterns from POIs. Secondly, the matching nodes between the POI connectivity graph and the associated road network are fulfilled by probabilistic relaxation and refined by a vector median filtering (VMF). Finally, POIs are aligned to the road network by an affine transformation according to the matching nodes. Experiments demonstrate that the proposed method integrates both the POIs from VGI and the POIs from official mapping agencies with the associated road networks effectively and validly, providing a promising solution for enriching professional road networks by integrating VGI POIs.     

Identifying regions based on flexible user-defined constraints

The identification of regions is both a computational and conceptual challenge. Even with growing computational power, regionalization algorithms must rely on heuristic approaches in order to find solutions. Therefore, the constraints and evaluation criteria that define a region must be translated into an algorithm that can efficiently and effectively navigate the solution space to find the best solution. One limitation of many existing regionalization algorithms is a requirement that the number of regions be selected a priori. The recently introduced max-p algorithm does not have this requirement, and thus the number of regions is an output of, not an input to, the algorithm. In this paper, we extend the max-p algorithm to allow for greater flexibility in the constraints available to define a feasible region, placing the focus squarely on the multidimensional characteristics of the region. We also modify technical aspects of the algorithm to provide greater flexibility in its ability to search the solution space. Using synthetic spatial and attribute data, we are able to show the algorithm’s broad ability to identify regions in maps of varying complexity. We also conduct a large-scale computational experiment to identify parameter settings that result in the greatest solution accuracy under various scenarios. The rules of thumb identified from the experiment produce maps that correctly assign areas to their ‘true’ region with 94% average accuracy, with nearly 50% of the simulations reaching 100% accuracy.     

An Integrated Mapping Approach to Petroleum Exploration on the Pratt Anticline,Pratt County,Kansas

A quantitative map comparison/integration technique to aid in petroleum exploration was applied to an area in south-central Kansas. The visual comparison and integration of maps has become increasingly difficult with the large number and different types of maps necessary to interpret the geology and assess the petroleum potential of an area; therefore, it is desirable to quantify these relationships. The algebraic algorithm used in this application is based on a point-by-point comparison of any number and type of spatial data represented in map form. Ten geological and geophysical maps were compared and integrated, utilizing data from 900 wells located in a nine-township area on the Pratt Anticline in Pratt County, Kansas. Five structure maps, including top of the Lansing Group (Pennsylvanian), Mississippian chert, Mississippian limestone, Viola Limestone (Ordovician), and Arbuckle Group (Cambro-Ordovician), two isopachous maps from top of Mississippian chert to Viola and Lansing to Arbuckle, a Mississippian chert porosity map, Bouguer gravity map, and an aeromagnetic map were processed and interpreted. Before processing, each map was standardized and assigned a relative degree of importance, depending on knowledge of the geology of the area. Once a combination of weights was obtained that most closely resembled the pattern of proved oil fields (target map), a favorability map was constructed based on a coincidence of similarity values and of geological properties of petroleum reservoirs. The resulting favorability maps for the study area indicate location of likely Mississippian chert and lower Paleozoic production.     

Modeling uncertainty of moving objects on road networks via space–time prisms

Moving objects produce trajectories, which are typically observed in a finite sample of time‐stamped locations. Between sample points, we are uncertain about the moving objects's location. When we assume extra information about an object, for instance, a (possibly location‐dependent) speed limit, we can use space–time prisms to model the uncertainty of an object's location.

Until now, space–time prisms have been studied for unconstrained movement in the 2D plane. In this paper, we study space–time prisms for objects that are constrained to travel on a road network. Movement on a road network can be viewed as essentially one‐dimensional. We describe the geometry of a space–time prism on a road network and give an algorithm to compute and visualize space–time prisms. For experiments and illustration, we have implemented this algorithm in MATHEMATICA.

Furthermore, we study the alibi query, which asks whether two moving objects could have possibly met or not. This comes down to deciding if the chains of space–time prisms produced by these moving objects intersect. We give an efficient algorithm to answer the alibi query for moving objects on a road network. This algorithm also determines where and when two moving objects may have met.