More

Rendering overlapping lines


I am making a map containing information about public transportation: busses, trams, etc. The map will have a layer containing for example the tram lines. Each line has its own colour, and is represented by a line string geometry.

The problem is, many of the lines have overlapping parts, where more than one tram line cover the same section. To show this to the user, I'd rather want the lines to run parallel to each other instead of being drawn on top of each other. For an example, see how Google Maps show the New York subway lines.

I suspect this is a quite common problem in cartography, but don't know what terminology I should search for.

I am using PostGIS/GeoServer/OpenLayers as my stack, but any open source solution would be acceptable.


If you're interested in learning more about this area, the problem is named cartographic displacement, and its one aspect of cartographic generalization.

A couple of articles discussing displacement and approaches for handling the problem:

  • Bader, Matthias. 2001. Energy Minimization Methods for Feature Displacement in Map Generalization.

  • Steiniger, S Tefan S, and S Iegfried M Eier. Snakes: a technique for line smoothing and displacement in map generalisation: 1-11.

  • Ware, Mark J, and Christopher B Jones. 1998. Conflict Resolution in Map Generalization Using Iterative Improvement. GeoInformatica 2, no. 4: 383-407+.


This problem is a very typical one in cartographic generalisation. Automated methods exist for that, but no implementations are available yet.

Methods based on "Beams" and "Snakes" give efficient results to solve these cartographic conflicts of network data (see also the references given by scw). Here are some results of the beams algorithms on road data:

Before:

After:

See also this paper and this presentation that explain how to do this transformation.

EDIT: I have never tested it, but it seems there is something related to snakes in GRASS. See here.


I don't know of a solution, but I think the term you are looking for is "conflict resolution" - a topic of map generalization. A Google search on "map generalization conflicts" shows some interesting info - but I don't know there's much of practical use.


In current implementations, you can often find either "Offset" or "Dislocation". Using you're current stack, it looks a bit difficult to achieve parallel lines automatically:

  • Geoserver's SLD Geometry Transformations function "Offset" seems to not be an equal to the "Offset" you can specify in a UMN Mapserver mapfile.
  • PostGIS doesn't provide parallel lines - by default - either. (See related question: How to create one sided buffers or parallel lines in PostGIS?)
  • I'm not sure if you can achieve anything decent messing around with OpenLayers JavaScript.

To sum it up: The easiest way I know of would be to use UMN Mapserver and the Offset value there. Example style for your mapfile:

STYLE SYMBOL 7 OUTLINECOLOR 160 160 160 SIZE 5 OFFSET 2 -99 # <-- This will offset the line to the right. ANTIALIAS FALSE END # STYLE

If you are drawing line layers that overlap, you have several options to allow the viewer to see all of the lines.

  1. Width - you vary the line width between layers and put the wider lines on the bottom.
  2. Opacity/Transparency - you can make individual line layers partially opaque, so you can 'see through' individual layers.
  3. Offset - you can offset the line symbol representing the actual line feature to one side of the actual geometry of the line. Individual layers can be offset by different amounts and in different directions to allow all layers to be seen.
  4. Lines can be represented by a series of point symbols with a defined gap between the symbols. By varying the symbol, symbol color, symbol size, and gap between the different line layers, you should make each individual line layer distinguishable.

I don't use GeoServer, but I know that MapServer has the functionality to do all of these things. It is likely that GeoServer does as well.


Updated commands Description How changed AutoCAD AutoCAD LT
3DORBIT Rotates the view in 3D space, but constrained to horizontal and vertical orbit only. The Realistic visual style maintains the new Exposure setting of the active viewport when orbiting the model. X
ADCENTER Manages and inserts content such as blocks, xrefs, and hatch patterns. DesignCenter Online has been fully replaced by Autodesk Seek. X X
ANIPATH Saves an animation of a camera moving or panning in a 3D model. The Realistic visual style maintains the new Exposure setting of the active viewport when creating an animation. X
BACKGROUND Defines the type, color, effects and position of the background for a named view. Support for image-based lighting (IBL) was added. X
DIM Creates multiple types of dimensions within a single command session. In earlier releases, DIM was used to access the Dimensioning mode commands. X X
DISTANTLIGHT Creates a distant light. Generic lighting (LIGHTINGUNITS = 0) is no longer supported.

Light calculations performed by the new renderer might affect the intensity factor you specify when creating a new light.

Light calculations performed by the new renderer might affect the intensity factor you specify when creating a new light.

Light calculations performed by the new renderer might affect the intensity factor you specify when creating a new light.

Light calculations performed by the new renderer might affect the intensity factor you specify when creating a new light.

Displays the Render Presets Manager palette and manages the render presets of a drawing. Now displays the Render Presets Manager palette instead of the Render Presets Manager dialog box.

-RENDERPRESETS doesn't allow you to manage the render presets created in earlier releases only render presets created in AutoCAD 2016.

Light calculations performed by the new renderer might affect the intensity factor you specify when creating a new light.

Light calculations performed by the new renderer might affect the intensity factor you specify when creating a new light.


2 Answers 2

  1. Enable and clear the stencil buffer.
  2. Draw the objects, setting the stencil buffer. Objects can be semi-transparent etc.
  3. Now set the stencil mode to only write pixels where the stencil is not set.
  4. And draw each object again, slightly scaled up, in the desired border colour and without textures.
  5. Disable the stencil buffer.

Here is the code adapted from some webGL stencil code that I have working:

I think I've used this approach in RTS games to draw halos around selected units, but it was a long time ago and I don't recall if there are any gotchas and all the nuances.

Start by finding all groups of objects, where a group of objects is a collection of objects which overlap. Standard collision detection should do the job. Assign to each group a unique colour. Any colour would do.

Render all your objects as solid colours, using the group colour, to a texture.

Create a new outline texture with the same dimensions as the render target. Scan through each texel of the render target and determine if it's a different colour to any surrounding texels. If it is, change the corresponding texel in the outline texture to the line colour you want.

Finally, take this outline texture and render it over the top of the image you want to draw on the screen (you could of course do this at the same time as the edge detection in a fragment shader and avoid creating the edge texture in the first place).

If you perform this step on the cpu by using a for loop to go through the render target's texels, then this will be pretty slow, but probably good enough to test and even use in some cases. To use this in real time you would be best to handle this in a shader.

A fragment shader to do this edge detection might look like this

Where the second value in the texture2D look up is a 2d coordinate relative to v_texCoord. You would apply this by rendering the first render target as the texture on a full screen quad. This is similar to how you would apply full screen blurring effects such as a guassian blur.

The reason to use the first render target with solid colours is simply to make sure that there is no perceived edge between different objects that overlap. If you simply performed edge detection on the screen image you would probably find that it detects edges at the overlaps as well (assuming the objects have different colours/textures/lighting).


Rendering overlapping lines - Geographic Information Systems

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited.

Feature Papers represent the most advanced research with significant potential for high impact in the field. Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication.

The Feature Paper can be either an original research article, a substantial novel research study that often involves several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest progress in the field that systematically reviews the most exciting advances in scientific literature. This type of paper provides an outlook on future directions of research or possible applications.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.


Polygon rendering

Blender (3D software)

Blender allows you to import SVG files and manipulate them as curve objects. Here is the imported graphic shown in the 3D viewport. (By default material will be affected by lights in the scene. Checking the shadeless property in the material property panel will allow the shapes to be rendered with their original colors.)

Here is a render made of the SVG inside Blender.

It does not have any gap between the triangles. Other 3D softwares are very likely to work the same way. So Blender behaves just like Illustrator, or does it? Maybe it is the other way around?


Global Mapper

SMC Synergy was accepted as an affiliate of Blue Marble Geographics during May 2013 and will be the preferred distributor of Global Mapper GIS software in Africa.
Global Mapper brochure (320 KB)

Global Mapper is an affordable and easy-to-use GIS Data processing application that offers access to an unparalleled variety of spatial datasets and provides just the right level of GIS functionality to satisfy both experienced GIS professionals and mapping novices. Equally well suited as a standalone spatial data management tool and as an integral component of an enterprise-wide GIS, Global Mapper is a must-have for anyone who deals with maps or spatial data.

  • Unmatched spatial data format support
  • Low cost and easy-to-use
  • Just the right level of GIS functionality
  • Unmatched and complimentary support

Global Mapper's intuitive user interface and logical layout helps smooth the learning curve and ensures that you will be up-and-running in no time. Your company will quickly see a significant return on investment brought about by efficient data processing, accurate map creation and optimized spatial data management. By providing a complete GIS translation solution out-of-the-box, Global Mapper simplifies the deployment of spatial technology in your company or organization. There's no need to juggle extensions or costly add-ons to gain access to the functionality that you need. Global Mapper's aggressive development and release cycle ensures that the product grows with you as your needs and requirements change. Now you can unblock the GIS dataflow logjam by providing a workable GIS software tool for everyone who needs access to this critical data. At a fraction of the cost of traditional GIS alternatives and with free set-up and general use support, as well as flexible licenses including single seat, network and USB Dongle licensing, there's no reason not to add Global Mapper to your GIS toolkit.

Global Mapper Free Extensions:

  • COAST: Coastal Adaptation to Sea Level Rise Tool: Learn how to model cost/benefit analysis for adaptation strategies for sea level rise and coastal flooding from storm events like hurricanes.
  • Overview Map Window: The overview map window extension allows users to preview data in small in-set window in the main map interface. This tool is a great way to manage your active data analysis while keeping track of a localized area view.

The Global Mapper LiDAR Module is an optional enhancement to the software that provides numerous advanced LiDAR processing tools, including automatic point cloud classification, feature extraction, cross-sectional viewing and editing, dramatically faster surface generation, and much more. At a fraction of the cost of comparable applications, it is a must-have for anyone using or managing LiDAR data.

The Global Mapper LiDAR Module is embedded in the standard version of the software and is activated using an appropriate license file or order number. As with the full version of Global Mapper, the LiDAR Module can be activated on a trial basis by requesting a temporary license.

Additional functionality offered in the LiDAR Module includes:

  • A convenient LiDAR Toolbar for easy access to key editing and analysis functions
  • Pixels-to-Points tool for creating a high density point cloud from overlapping imagery
  • Multiple gridding options for faster DSM or DTM generation
  • Access to point cloud files containing a billion points or more
  • Automatic point classification tools that automatically distinguish building, ground, and vegetation points in unclassified layers
  • Feature extraction functionality to automatically create 3D building footprints and trees
  • Cross-sectional rendering using Global Mapper's Path Profile tool for viewing and editing the point cloud in a vertical perspective
  • Advanced filtering options to efficiently remove erroneous or unneeded points
  • LiDAR scripting commands for streamlining workflow
  • Point colorization from underlying imagery offering photo-realistic point cloud rendering in Global Mapper's 3D Viewer
  • Support for reporting LiDAR statistics
  • Support for importing and exporting most common LiDAR formats

The LiDAR Module was first released with Global Mapper v15 and a host of new LiDAR tools have since been added with the release of version 16. The Global Mapper LiDAR Module is an optional enhancement to the software that provides numerous advanced LiDAR processing tools, including Pixels-to-Points™ for photogrammetric point cloud creation from an array of images, automatic point cloud classification, automatic extraction of buildings, trees, and powerlines, cross-sectional viewing and point editing, custom digitizing or extraction of 3D line and area features, dramatically faster surface generation, LiDAR quality control, and much more.

Global Mapper Mobile is a powerful GIS data viewing and field data collection application for iOS and Android devices that utilizes the device's GPS capability to provide situational awareness and locational intelligence for remote mapping projects.

Global Mapper 2.0 Mobile Overview

The second generation of Global Mapper Mobile includes new and improved functionality throughout the application:

  • Provides field access to all of your GIS data
  • Offers straightforward, GPS-based field data collection
  • Includes an array of digitizing or drawing tools
  • Enables the assignment of attribute data
  • A new Pro edition of the app with numerous professional-grade tools
  • Streaming Open Street Map data

Used in conjunction with the desktop version of Global Mapper, Global Mapper Mobile is capable of displaying over 250 formats of raster and vector spatial data without the need for a continual data connection. Data is transferred to the device via iTunes or email in the form of Global Mapper Mobile Package Files which efficiently compress multiple layers into a single file and allow large amounts of data to be stored and displayed. Want to see Global Mapper Mobile in action? Download a free copy from the App Store or Google Play store today and upgrade to the Pro version at any time.


Auxiliary use and detail optimization of computer VR technology in landscape design

Computer VR technology has been widely used in landscape design. This paper analyzes the auxiliary use mode and detail optimization process of this technology in landscape design, and gives the comparative experiment of indoor and outdoor landscape design. Firstly, landscape design planning and design based on VR technology secondly, the detailed optimization process of the combination of VR and VR-GIS is analyzed finally, aiming at the problem of poor energy-saving effect of traditional methods, the energy-saving optimization design of data acquisition system is carried out. The data collector in the data acquisition system uses the method of contrast image of visual image to collect data, so it consumes a lot of power in the process of using. Therefore, the data collector and control power system are optimized, which can effectively reduce the energy consumption. In the process of virtual image establishment, edge correlation operator is used to calculate correlation, so as to achieve low power consumption. On this basis, the auxiliary use and detail optimization in landscape design are given by using Lumion virtual reality (VR) platform. The experimental results show that the visual modeling error of this method is stable between 1 and 3%, the error is small and the stability is good the average rendering time of batch objects is about 8.9 s, which is an efficient auxiliary use and detail optimization method for VR landscape design.

This is a preview of subscription content, access via your institution.


The GeoSat Project: Using Remote Sensing to Keep Pace with Urban Dynamics

T. Santos, S. Freire, J.A. Tenedório
e-GEO, Research Centre for Geography and Regional Planning, Faculdade de Ciências Sociais e Humanas, FCSH, Universidade Nova de Lisboa, Portugal
([email protected], [email protected], [email protected])
A. Fonseca, N. Afonso
National Laboratory for Civil Engineering (LNEC), Lisbon, Portugal
([email protected], [email protected])
A. Navarro, F. Soares
University of Lisbon, Faculty of Sciences, LATTEX-IDL, Portugal
([email protected], [email protected])

I. INTRODUCTION

This work was conducted in the framework of project GeoSat – Methodologies to extract large scale GEOgraphical information from very high resolution SATellite images. The purpose of the project was to develop methods to expedite the production of geographic information for municipal planning and land monitoring.

A. The Problem – Motivation for GeoSat

Urban dynamics are induced by such activities as new construction (buildings and roads), demolition of unwanted structures, tree plantings along roads, vacant lots being transformed into urban agricultural sites or green areas becoming parking lots. Maintaining updated cartographic datasets in such environments is a challenging task.

The characterization of urban dynamics considers two key elements – the spatial, or the available space in which growth occurs, and the temporal, or the period during which this development takes place [1].

The temporal dynamics can be characterized as fast, medium or slow, depending on the time frame of the transformation. Temporal urban databases have immediate applications in the monitoring of urban sprawl, watershed analysis, environmental assessment, hydrologic modeling, land surface degradation and the development of predictive modeling techniques to forecast future areas of urban growth.

The spatial dynamics can occur in two ways – through the addition of new areas, e.g., formerly rural areas converted to new urban uses, or through the more intensive, but same use of a site, e.g., changing a residential area from a lower to a higher density. The former leads to urban expansion, and the latter to intensification.

The urban environment is a physical representation of human activities and as such is subject to measurement. A number of data sources help characterize this environment and describe its variability. Census data identifies buildings and their uses, cadastral maps show properties subject to different taxes and maps of urban infrastructure identify water, sewer or high voltage power lines. Geographic information for urban planning is formalized through cartographic representations such as topographic and thematic maps.

Presently in Portugal, the cartographic framework for municipal land planning requires compliance with demanding and complex technical specifications that are mandatory for the production of detailed maps of the required quality. To obtain such large-scale topographic maps, municipalities must devote the necessary human and financial resources.

Consequently, detailed spatial information regarding municipal land use is only produced when the Master Plan is prepared. In the Portuguese land planning system, plan revision takes place every 10 years (the legal term for local plans), but longer periods are common.
Such update periodicity does not reflect the dynamic nature of the land use, and hampers the work of the municipal departments that deal with geographic information on a daily basis.

In fact, many situations that occur in the municipal context – such as updating cadastral databases, management of urban areas, street maintenance and construction or planning for such potential disasters as earthquakes or floods – require expedited production of digital maps at large scale.

B. The Solution – Using Earth Observations to Support the Planning Process

Aerial imagery has been the most common data source for mapping human activities in the urban environment [2]. Only recently, satellite images have gained interest as alternative data sources for mapping urban areas, mainly due to their higher spatial detail. This development coincides with the emergence of new integrated methodologies (e.g., GEographic Object-Based Image Analysis – GEOBIA) and new application fields that had previously been the domain of airborne remote sensing and can now be tackled by satellite remote sensing [3].

Very High Resolution (VHR) satellite imagery (i.e., images with spatial resolution equal or greater than 1 m) can now be used as a data source for extracting geographic information at the local scale and on a regular basis. Its acquisition and processing is much easier and quicker when compared with the process based on aerial imagery. The detail and quality of the extracted geographic information, however, is still inferior to that obtained by photogrammetric methods. Nevertheless, for local applications where temporal resolution is paramount, the use of satellite imagery allows users to monitor changes in the urban status, making urban remote sensing a valuable contribution to research in urban geography and planning. Mapping land-use change provides an historical perspective and an assessment of the spatial patterns, rates, correlation, trends and impacts of that change.

C. Direct Mapping vs. Updating of Existing Outdated Cartography

Land status can be assessed through direct mapping or by updating already existing cartography. In fact when cartographic information already exists, but is outdated, a change-detection procedure using recent geographic data can be applied for map updating. The aim of this analysis is to highlight those areas where changes have most likely occurred. Effort is focused in those changed areas, and the remaining ones, i.e., the unchanged areas, keep the geometry and attribute stored in the database.

Large-scale topographic mapping, however, usually has to conform to legal technical specifications and quality standards. Buildings are a major urban element and one of the main feature classes of interest for a municipality, and the “correct” automatic extraction of buildings information from imagery remains a challenging task, even with the advent of high spatial resolution [4]. Difficulties include scene complexity, building occlusions (trees, shadows) and the internal heterogeneity of the feature class [5], and these increase with refinement of image resolution [6].

The scientific literature on the use of VHR images data for mapping purposes suggests that this kind of image can potentially be used for feature extraction and large-scale cartographic updating [7]. Regarding the extraction of specific topographic features from VHR images, several tests on road networks (e.g., [8]) and buildings detection (e.g., [9]) have been reported in the literature, but very few compare the mapping accuracy that can be obtained from high-resolution satellite images to the actual requirements for large-scale mapping in well-mapped countries. In fact, most of the challenge in obtaining a cartographic product from VHR imagery using feature extraction results from the interplay of several factors – the object and its context, the nature of the imagery and the mapping requirements and constraints [4]. Despite the many methodologies proposed for feature extraction, none has so far proved to be effective in all conditions and for all types of data. At present, the quality assessment of extracted buildings is still a complex endeavor for which there is no optimum, consensus or standard approach [4].

Under the GeoSat framework, the potential of VHR satellite imagery and GEOBIA for detection and mapping of urban features and their integration into operational urban planning and management activities was investigated. Santos et al. [10] have explored and proposed detailed vector-based metrics for accuracy assessment of QuickBird-derived buildings, but without taking map standards into account. Recently, Freire et al. [4] presented a methodology that incorporates existing scale-based mapping constraints from official specifications in the process of quality assessment of building polygons extracted semi-automatically from VHR imagery.

D. The City of Lisbon

As of 2001, the city of Lisbon, Portugal, had 556,797 residents, and occupied an area of 84 km 2 . Lisbon is a typical European capital city, with very diverse land use dynamics, varying from consolidated historical neighborhoods where the street network is dense and most of the area is built up, to modern residential areas with ongoing construction of roads and multi-family buildings. Between these two situations, there are more heterogeneous places with varied land uses such as residential, parks, agriculture, vacant land, industrial, utilities and schools.

Because a new Master Plan has not yet been approved, Lisbon’s official cartography in use in 2011 dates from 1998. In areas subject to strong urban pressures, the frequency of map updating is not compatible with the high rate of change. In these cases, the municipal cartography fails to represent the current reality, thus hindering decision-making on land planning, land-use management and land conservation, as well as compromising policy delineation of human and economic activities, or even limiting efficient law enforcement.

The GeoSat project, which took place between 2008 and 2010 and involved the Lisbon City Hall, developed several sample applications based on VHR imagery to expedite the production of geographic information for municipal planning and land monitoring.

Thematic mapping was addressed in several works. Freire et al. [11] investigated the potential of VHR satellite imagery and GEOBIA for detecting, mapping and characterizing agricultural areas in Lisbon. Dinis et al. [12] applied a methodology based on a multi-temporal satellite imagery dataset and LiDAR (Light Detection And Ranging) data to overcome the problem of shadows in urban areas. Santos et al. [13] tested the contribution of LiDAR data when extracting urban features using VHR imagery. Freire et al. [5] tested the semi-automated extraction of different building types from areas with diverse characteristics, and investigated the impact of the heterogeneity of these features and the urban context in the extraction process.

Land planning also requires information for analytical purposes. Santos et al. [14] analyzed the solar potential of rooftops in an urban context. Santos et al. [15] demonstrated that VHR imagery can be used for quick updating of detailed land-cover information. Based on this recent information several applications can be implemented. Indicators of land-sealing areas and the quantification of green areas and available vacant land in the city are ecological measures that can be used as tools for cities to assess and communicate different environmental risks, and to promote strategies and measures of sustainable urban development and disaster risk management.

II. SAMPLE APPLICATIONS

This section presents three major GeoSat project applications – the updating of existing maps, the mapping of impervious surfaces and the analysis of rooftop solar potential using LiDAR data.

Figure 1 – Study area location in the city of Lisbon.

A. Updating of Existing Maps

A multi-temporal strategy for updating a map using existing cartography, a satellite image and an altimetric dataset was applied in a study area located in the eastern part part of Lisbon [16]. The aim of this analysis is to highlight those areas where changes have most likely occurred, thus rendering the existing map outdated.

The selected area occupies 64 ha (800 m X 800 m), and is characterized by several building typologies including industrial properties, schools, apartments and single-family housing (Figure 1).

The spatial database explored in this case study included cartography, satellite imagery and altimetric data. The map to be updated is the Lisbon’s Municipal Cartography from 1998, at a 1:1 000 scale. The altimetric data are compiled by the normalized Digital Surface Model (nDSM). An nDSM is a spatial dataset that depicts the elevation of all objects above the ground.

The nDSM of the study area is from 2006, and has 1 m resolution. The imagery includes a pansharp QuickBird image acquired in 2005, with a 0.6 m pixel size.

Figure 2 – Map updated to 2005 showing the type of changes occurring in the study area.

The goal of this application is to produce updated information for the following classes present in the 1998 Municipal Cartography: “Buildings”, “Annexes” and “Shacks”. The extraction methodology was applied to this dataset using a feature extraction software to produce a map of the buildings present in the image. After building extraction, a post-processing stage was conducted to enhance the geometric quality of the elements.

Once the 2005 building map was developed, the next step was to produce a changed map using the 1998 municipal map at the 1:1 000 scale. The change detection process was able to identify missing structures and to detect new ones. For objects with an area larger than 20 m 2 , the detection quality had an overall accuracy of 99%. Figure 2 shows the updated map with detected changes and their type. In the period under analysis, the main types of change identified in the study area were shack eradication and building demolitions (industrial properties), newly built industrial sites (e.g., the wastewater treatment plant, located in the bottom left corner of the map) and new residential housing (e.g., two multi-family buildings).

Municipal technicians can use this new product to decide, based on analysis of the image and related information, if the marked spot is in fact a change area (a new urbanization or a built-up object that was demolished), and if so, to update the map by adding new buildings or eliminating demolished buildings in the old cartography. Technicians can eliminate any spots considered to be false detections.

Such methodology can be used by the municipality to keep its cartographic database of urban areas up to date in the period between the development of official maps, and can achieve high thematic and positional accuracy.

Figure 3 – The city of Lisbon and the IKONOS-2 image used for imperviousness mapping.

B. Mapping of Impervious Surfaces

Impervious surfaces can generally be defined as anthropogenic features, such as roads, buildings, sidewalks and parking lots, through which water cannot infiltrate into the soil. The artificial surface coverage can be used to evaluate the quality of urban streams, and to study the effects of runoff. Impervious surface is increasingly recognized as a key indicator for assessing the sustainability of land-use changes due to urban growth [17].
An updated and detailed map of imperviousness for the whole city of Lisbon was produced using IKONOS-2 satellite imagery and the nDSM from 2006 [15] (Figure 3).

The imagery classification aims at extracting the three main components of land cover: “Vegetation”, “Impervious Surfaces” and “Soil” [18]. The map of impervious areas includes a wide range of materials, some of which have very different spectral properties (e.g., pavement, concrete and roof tiles). The first level class, “Impervious Surfaces”, corresponds to the land surface after the “Vegetation”, “Soil” and “Shadow and Water” classes are masked out. Based on the pansharp image and the nDSM, six classes were distinguished on the second level of the nomenclature: “Trees”, “Low Vegetation”, “Buildings”, “Roads”, “Other impervious surfaces”, “Soil” and “Shadows and Water”, (Figure 4). The thematic accuracy of the map was investigated and returned an overall accuracy of 89%.

Figure 4 – Land cover map of 2008 derived from IKONOS imagery from 2008 and LiDAR data for the city of Lisbon.

This application demonstrates that an automated classification of VHR images can produce fast updating of detailed land cover information and can be used to support land planning decisions or to aid in the response to a crisis situation where official maps are generally outdated.

C. Analysis of Rooftop Solar Potential Using LiDAR Data

This application consists of a methodology that applies altimetric data to the evaluation of the potential for incorporating solar power systems into buildings in a city neighborhood. The use of LiDAR data can play an important role in analyzing the suitability of buildings for receiving solar systems. Solar mapping takes advantage of Geographic Information System (GIS) and visualization technologies, and offers a solid knowledge base on solar resources and best practices. Solar maps also offer a comprehensive planning tool to evaluate energy reduction opportunities for new and existing buildings, to plan future energy consumption and supply or to monitor compliance with energy and greenhouse gas goals.

This work, conducted in an area of 625 ha located in the heart of the city, analyzed the suitability of rooftop areas for the installation of solar energy systems [14], and performed a brief technical analysis that considered the optimal location for solar Photovoltaic (PV) systems (Figure 5).
Identifying the incoming solar energy at rooftop level entails the modeling of solar radiation incident in each location. Two inputs are required – a DSM and the buildings’ footprints. With these data, modeling the solar radiation can be done in a GIS environment.

Figure 5 – Study area for solar potential analysis in Lisbon and the Digital Surface Model from 2006.

The dataset used in this application thus included cartographic and altimetric data. The cartographic data that represent the buildings’ footprints are the Buildings layer of the Municipal Cartography. To characterize the altimetry, the DSM for 2006 was used.

A four-step methodology was applied: 1) calculating the solar energy for the whole surface 2) assessing the solar energy at the rooftop level 3) locating the best sites for the installation of PV panels and 4) quantifying the energy that could be produced (Figure 6). A map of the solar potential of rooftops located in the study area was produced.

This LiDAR-based solar resource map helps rate buildings by the solar resources available, and provides unique information on which parts of the buildings’ roofs are more suitable for solar applications when all critical factors are considered. This information can be used to develop detailed solar generation potential maps.

Figure 6 – Solar potential analysis in the city of Lisbon.

III. CONCLUSIONS

Detailed and updated geographic information is essential to effective urban planning and monitoring. Our understanding of nearly every aspect of the changing environment depends upon regular updates of land use/cover status and land-cover conditions, and we need new sources of spatial data and innovative approaches to understand and manage dynamic urban areas. In the remote sensing of cities, VHR satellite imagery offers the opportunity to characterize and monitor the intra-urban environment by enabling discrimination among the land-cover objects that compose this environment.

We have distinguished three areas of application – large-scale map updating, imperviousness mapping and developing indicators of rooftop potential for solar systems – each of which requires its own level of accuracy of geographical information.

For municipal planning according to the technical specifications of large-scale cartography (1: 5 000 scale and higher), map production based on VHR images and photogrammetry is still necessary to guarantee that each uniquely identified feature is well delineated and stored in the database as a geometric entity with a list of attributes. For large-scale analytical applications, however, the current VHR images constitute a valuable source of geographic information, and can play an important role in municipal planning. The three applications presented are a good demonstration of these capabilities.

Based on the premise that a product derived from less accurate images can be effective for land monitoring, the first application proposed an alarm system obtained through satellite and altimetric data processing. The goal was not to provide cartographic data ready for integration into the municipal databases but to assist the process of map updating.

The second application demonstrated that the automatic classification of remote sensing data can expedite the creation of useful spatial knowledge that can support decision-making. The mapping methodology ensures that urban planners have updated land cover data on a regular basis. This tool can be used to monitor the incidence of land cover change within the city, to decide on areas of priority intervention or to assess natural resource sites for preservation and restoration.

Premised on the idea that the wide adoption of solar technologies will depend upon detailed solar suitability information on every building in a community, a map of the solar potential of rooftops located in a study area was produced. This LiDAR-based solar resource map helps rate buildings by the solar resources available, and provides unique information on which parts of the buildings’ roofs are more suitable for solar applications when all critical factors are considered. This information can be used to develop detailed solar generation potential maps. The next step will be making solar maps publicly available. In fact, interactive Web-based urban solar maps are already available [19].

Very High Resolution remote sensing data can contribute to better monitoring, modeling and understanding of urban dynamics and their impacts on the urban and suburban environment, and can enhance the analytical tools available for land-use planners. Our experience, however, suggests that extracting features for large-scale applications still requires much human intervention. Nevertheless, new VHR sensors with high-spectral resolution constitute a new opportunity for urban mapping. The development of object-based algorithms allow the introduction of information such as color, shape, adjacency or context in the classification process, and improve the mapping of urban elements.


2.37.2 Boundaries and Relationships Questionnaires differ from Lists because Lists group existing resources, while Questionnaires group arbitrary questions. Questionnaires are distinct from Observations and DiagnosticReports in that both of these resources are intended to capture only certain types of information (lab, imaging, vitals, etc.) and should not be used to capture the full breadth of healthcare information (allergies, medications, care plans, etc.), while Questionnaires are able to capture any information at all. More importantly, Observation and DiagnosticReport focus on capturing the discrete information in a standardized form so that the information can be used consistently regardless of where or how it is captured. Questionnaire focuses on information capture. The same information can be captured using a wide variety of questionnaires with differently phrased questions organized in different manners. As such, the Questionnaire resource provides a means to standardize the information gathering process (how information is captured), but not how data is interoperably compared, analyzed or computed upon (typically managed using Observation, DiagnosticReport as well as other resources). Questionnaire supports data-collection workflow to a limited extent, in that - once triggered - a Questionnaire can guide a user through a data collection process that ensures appropriate information is collected based on answers to particular questions. However, Questionnaire doesn't provide support for capturing sets of information at different times or highly interactive data capture. Broader workflow is typically managed using PlanDefinition and Task or using other mechanisms such as CDSHooks. Questionnaires are similar to the notion of "logical models" supported by the StructureDefinition resource. Both support the representation of a collection of data points with labels, data types and hierarchy. It will be common to find the two of them mapped together. The primary difference is that Questionnaire is focused on user-facing data collection. It describes specific questions and includes information such as what number/label should be displayed beside each question, conditions in which questions should be displayed (or not), what instructions should be provided to the user, etc. StructureDefinition, on the other hand, merely defines a data structure with no guidance on display or rules around capture mechanism, only what data should exist in the end. As well, logical models are not intended to capture data directly. Rather, they provide a basis for mapping between data capture structures. Geographic Variation in Condom Availability and Accessibility

Identifying predictors that contribute to geographic disparities in sexually transmitted infections (STIs) is necessary in order to reduce disparities. This study assesses the spatial relationship condom availability and accessibility in order to better identify determinants of geographic disparities in STIs. We conducted a telephone-based audit among potential-condom selling establishments. Descriptive analyses were conducted to detect differences in condom-selling characteristics by stores and by store type. Geocoding, mapping, and spatial analysis were conducted to measure the availability of condoms. A total of 850 potential condom-selling establishments participated in the condom availability and accessibility audit in St. Louis city 29 % sold condoms. There were several significant geographic clusters of stores identified across the study area. The first consisted of fewer convenience stores and gas stations that sold condoms in the northern section of the city, whereas condoms were less likely to be sold in non-convenience store settings in the southwestern and central parts of the city. Additionally, locations that distributed free condoms clustered significantly in city center. However, there was a dearth of businesses that were neither convenience stores nor gas stations in the northern region of the city, which also had the highest concentration of condoms sold. This initial study was conducted to provide evidence that condom availability and accessibility differ by geographic region, and likely are a determinant of social norms surrounding condom use and ultimately impact STI rates.

This is a preview of subscription content, access via your institution.


Frequently Asked Questions

✅ Why should you choose Syncfusion ASP.NET Core Maps?

  • Render geometric or custom shapes using the GeoJson data.
  • Render maps from the map providers like Bing, OSM, and Google maps.
  • Add markers on maps at the specified latitude and longitude.
  • Fast-paced performance on zooming and panning with elegant animation.
  • One of the best ASP.NET Core Maps in the market that offers feature-rich UI to interact with the software.
  • Simple configuration and API.
  • Supports all modern browsers.
  • Mobile-touch friendly and responsive.
  • Expansive learning resources such as demos and documentation to learn quickly and get started with ASP.NET Core Maps.

✅ What is the price for Syncfusion ASP.NET Core Maps?

We do not sell the ASP.NET Core Maps separately. It is only available for purchase as part of the Syncfusion ASP.NET Core suite, which contains over 70+ ASP.NET Core components, including the Maps. A single developer license for the Syncfusion Essential Studio for ASP.NET Core suite costs $995.00 USD, including one year of support and updates. On top of this, we might be able to offer additional discounts based on currently active promotions. Please contact our sales team today to see if you qualify for any additional discounts.

✅ Where can I find the Syncfusion ASP.NET Core Maps demo?

You can find our ASP.NET Core Maps demo here.

✅ Can I purchase the Syncfusion ASP.NET Core Maps component separately?

No, our 70+ ASP.NET Core components, including Maps, are not sold individually, only as a single package. However, we have competitively priced the product so it only costs a little bit more than what some other vendors charge for their Maps alone. We have also found that, in our experience, our customers usually start off using one of our products and then expand to several products quickly, so we felt it was best to offer all 70+ ASP.NET Core components for a flat fee of $995/developer. On top of this, we might be able to offer additional discounts based on currently active promotions. Please contact our sales team today to see if you qualify for any additional discounts.

✅ Can I download and utilize the Syncfusion ASP.NET Core Maps for free?

No, this is a commercial product and requires a paid license. However, a free community license is also available for companies and individuals whose organizations have less than $1 million USD in annual gross revenue and five or fewer developers.

✅ How do I get started with Syncfusion ASP.NET Core Maps?

A good place to start would be our comprehensive getting started documentation.


Watch the video: 1904 - Freestyle Linesets for more detailed linework (October 2021).