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Up until now, the process of creating a mosaic of hundreds if not thousands of orthorectified aerial images is extremely time-consuming. A conventional computer program has to analyze each image taken and figure out its exact angle and camera position in order to build a model of a piece of land. Computer software does this by looking at common features in adjacent photos and marking them with tie points. In an image of a cornfield, for example, it might use a corn plant that shows up in two photos. The computer then adjusts its calculation of the camera positions for all the photos at once so that the different images match up to produce one coherent projection of a tie point. From there, all the tie points can be projected onto a 3D model and a mosaic is created. The problem is that current software is only equipped to handle a few hundred images. Once the number of images is more than a thousand, the typical amount gathered by a drone, the process can take well over 1000 hours. To remedy the situation, computer scientist Mark Pritt, along with colleagues at Lockheed Martin in Gaithersburg, Maryland, developed a new algorithm to handle thousands of images and speed up the process. Their computer program takes the tie points from each photo and projects then directly onto a 3D space. This is without knowing the exact shape of the land or the position of the camera. Consequently, tie points do not always match up. The same corn plant could have two projections on the model. When two tie points don’t match up, though, the computer program instinctively takes the middle point between the two and adjusts the camera position accordingly. It does this one image at a time, and once the software tweaks the camera position for all the photos, it repeats the entire process again to fix any errors that occurred. Overall, the result is that the process of analyzing images is sped up dramatically. What would have taken over a thousand hours can be done within 24 hours or less. Now that this new software can handle a higher amount of images, drones could see considerable action in mapping. They can be used to produce high-resolution images of crops that could inform farmers’ decisions of how to take care of their fields. They could also be utilized for disaster relief. If an earthquake strikes, drones could collect images of the affected areas for relief workers. This could be done all at a cheaper price than the costs of using planes or purchasing satellite images. The next step, according to researchers, is improving this process so that maps from drones could be produced in a matter of minutes. http://www.aipr-workshop.org/AIPR%202014/AIPR%202014%20Final%20Program.pdf http://news.sciencemag.org/technology/2014/10/drones-could-3d-map-scores-hectares-land-just-few-hours

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D3 is a JavaScript data visualization library using HTML, SVG and CSS. D3 stands for Data Driven Documents and was developed by Mike Bostock, Jeff Heer and Vadim Ogievetsky as a successor to the Protovis framework. The 3.0 release of D3 now includes a geographic projection system. Common geographic projections are included in the default build of D3 such as Albers, Gnomic, and Mercator. Additional geographic projections can be accessed via the extended geographic projections plugin and the polyhedral projection plugin. Over forty different projections are support by the default D3 build and geographic projection plugin. The Geo Projections page has a listing of projections with links to sample pages and code. Tutorial : The tutorials page on the D3 wiki provides access to a range of instructions on getting started with D3. Mike Bostock has a tutorial on basic map making using D3 and TopoJSON in his tutorial, “Let’s Make a Map.” The tutorial steps you through finding GIS data, loading the data, and symbolizing the polygons and adding labels. EJ Fox has a tutorial on the Visual.ly blog about how to make a choropleth map in D3 using Google’s JSON data of the 2008 presidential election. as the example. Alex Rothenberg also uses 2008 Presidential Election data to create an interactive map by combining D3 with Ember. Example sites : There are a few pages interested users can browse to see examples of D3 in use showing a range of data visualization techniques. Mike Bostock, who now works for the NY Times has several sample pages. To see in use examples by the NY Times, visit Mike Bostock’s launch page for some of his D3 work. Included are some of the amazing post election results maps from the 2012 presidential election such as the Counties Blue and Red, Moving Right and Left map. mbostock’s blocks also has pointers to a wide range of data visualization examples as does the D3 Gallery. Jens Finnäs, a Finnish journalist has some interesting D3 mapping examples on his dataist blog such as his map of the Occupy Wallstreet movement over time. source : http://www.gislounge.com

OpenGeoPDF is the next-generation of GeoPDF® technology. More than an extension of GeoPDF, OpenGeoPDF is a collection of IT and geospatial standards and technologies. It combines spatial database features and application logic, which leverage Geospatial Information Systems (GIS) to enable radically new customer workflows and deliver map and data-driven applications to anyone, anywhere. One of the many benefits of the OpenGeoPDF approach is that GeoPDF maps with embedded feature attributes can be accessed, searched and extracted as an OGC (Open Geospatial Consortium) GeoPackage, using any PDF-compatible software. OpenGeoPDF offers limitless, new possibilities for geospatial data interchange and creates much richer applications for end users. These applications leverage potentially hundreds of thousands of features and their attributes by simply using TerraGo Toolbar for Adobe Reader®. source : http://www.terragotech.com/gis-products

The Build Elevation Mosaic Tools toolbox Building mosaic dataset makes it simple to publish collections of elevation data files as elevation, slope, aspect, and hillshade layer services, so they do not have to be re-created for each use. This approach reduces data duplication because the source files do not need to be copied to be used in the mosaic dataset. They can be used from their existing locations as long as ArcGIS for Server can access them as a data store. When publishing the results, you can reduce the number of services by applying the raster function templates included with the tools in this toolbox. These function templates modify pixel values on the fly to show derived views of the elevation data, such as percent slope, aspect, and hillshade. The client simply chooses which function to view from the DTM service. DOWNLOAD https://github.com/Esri/solutions-geoprocessing-toolbox/releases/tag/v10.2.2.1