Google’s Cloud-based Earth Builder

Maintaining and scaling a GIS server can be challenging. In today’s economy, state and local governments and non-governmental organizations struggle with shrinking public budgets and fewer resources. Rod Erickson of Next Tier Concepts provides provides a solution using Software-as-a Service (SaaS).

A GIS requires the following components: hardware, software, databases, and people. A GIS server facilitates the storing and sharing of geospatial information such as imagery and vector data. Maintaining and scaling a GIS server can be challenging. In today’s economy, state and local governments and non-governmental organizations struggle with shrinking public budgets and fewer resources available to support a GIS; while concurrently incurring a greater demand from an increased number of users requesting map data and imagery as a web-based service. Google’s Earth Builder eliminates multiple layers of infrastructure and allows GIS data provider’s to focus on data without maintaining hardware, software, or databases.

The Earth Builder is a cloud-based Software-as-a-Service (SaaS); it eliminates the need for highly-trained information technology staff to administer and maintain a GIS server. As a result, users of Earth Builder such as United States Forest Service (USFS) and the Department of Defense can focus on their missions and support their users without the concerns, costs and overhead of maintaining a traditional GIS. Earth Builder enables organizations to pool resources and collaborate, which facilitates data sharing and reduces duplication of data hosted on multiple servers.

The Google Earth Builder SaaS interface and approach is based on the successful, intuitive Google Applications model for services such as Gmail and Documents already familiar to many users. Many consumers of geospatial data and imagery take advantage of products like Google Earth, and as professionals we may encounter the need to leverage a common and reliable geospatial base map as a resource. As of October of 2011, over one billion users have downloaded Google Earth. Regardless of user type, affiliation or service needs we all consume Google Earth from the same, single web-based resource.

The Earth Builder solution by Google is an innovative approach to storing and sharing geospatial data. Using this alternative to a traditional GIS solution, data managers and stewards such as NIFC reduce time and budgets required to serve geospatial data, imagery and a common base map. Expensive middleware and complex administration processes are replaced by a simple, intuitive Google Applications interface. It is time to stop serving the same pixels and vectors over and over again. Serve once and share many, many times.

Written By:- Rod Erickson

Satellite images help doctors count people from space

For the first time, the population of an entire city has been estimated from space through a pioneering project to speed up medical and disaster relief efforts. By analysing satellite images of disaster zones and famine, war or disease-hit areas where census data is unreliable, or cities unreachable, researchers hope to make rapid and more accurate estimates of how many people might need help.

"Population numbers are crucial to everything we do," says Ruby Siddiqui of the global charity, Medicins Sans Frontières, a collaborator in the project to evaluate the satellite counting technique. "We need to plan the size, scale and mode of interventions, and without population numbers we can't do this," she says.

At present, groups like MSF rely on the "quadrat" method to estimate population size. Surveyors visit a sample of individual households to find out how many people live in each type of dwelling. From this, they estimate the population of the entire town or refugee camp. But the method is slow, it demands teams of up to a dozen samplers, and requires careful data analysis afterwards. Also, such operations can be hazardous or impossible to carry out in conflict zones such as Syria.

To overcome some of these obstacles, Chris Grundy of the London School of Hygiene and Tropical Medicine has led a project to estimate the population of Am Timan in Chad using satellite images.

The entire population is to receive meningitis vaccinations, and MSF wants to know how many doses to order.

Preliminary results presented last week in London at the annual research meeting of MSF revealed that the satellite method matched the quadrat method for accuracy. Also, it took about half the time to deliver an answer, although Grundy says that with refinement it has the potential to be much faster.

Surveyors, including Siddiqui, still had to visit households in Am Timan to estimate how many people typically lived in each kind of building, but they could then make a city-wide estimate by counting the total number of each type of dwelling in the satellite image, either through a computer automated analysis or by manual counting.

The quadrat method, which required sampling visits to 1160 dwellings, gave a population of 49,722. The satellite technique, which required sampling visits to only 348 dwellings, gave estimates of 46,625 for the manual and 45,400 for the automated method.

"These results are very good, and there's no doubt they'd be good enough for what MSF wants to do," says Grundy. But the team hopes to make it faster still, potentially eliminating the need to sample dwellings first.

Grundy's team is also evaluating the counting method in 11 refugee camps around the world, including in earthquake-hit Haiti. "We now know it works, that MSF can do it. The next step is to simplify it," he says.

Written By:- Andy Coghlan

GIS Cloud

Cloud computing is rapidly emerging as a technology almost every industry that provides or consumes software, hardware, and infrastructure can leverage. The technology and architecture that cloud service and deployment models offer are a key area of research and development for GIS technology.

What Is Cloud Computing? 

Although there are several variations on the definition of cloud computing, some basic tenets characterize this coming revolution. Cloud computing furnishes technological capabilities—commonly maintained off premises—that are delivered on demand as services via the Internet. Cloud GIS offerings can range from data storage to end-user Web applications to other focused computing services. Esri considers cloud computing and technology important in the development and vision of the ArcGIS platform.

Public versus Private Cloud

There are several types of cloud computing deployment scenarios.

The National Institute of Standards and Technology (NIST) is emerging as the preferred provider of the de facto definition of cloud computing and the distribution models, seen here with some Esri examples.

Public Cloud

The public cloud is the most commonly referenced regarding the topic of cloud computing, where the infrastructure and applications are owned by the organization selling cloud services.

Private Cloud

Since many traditional vendors and users are not quite ready to jump into public cloud computing or are restricted from doing so, the cloud service tiers are replicated within a private cloud environment, behind the firewall, and maintained within the parameters of the host organization.

Hybrid Cloud

Many believe that the sweet spot for cost optimization in an organization will rely on a delicate balance of public, or community, and private clouds. However, since this hybrid cloud solution is commonly bound together by proprietary technology, it will only be embraced by enterprise computing in the future as standards are developed.

Cloud Service Models

Three core options compose the service model within the cloud computing environment.

Each service category can be leveraged independently or consumed in combination with other service tiers.

Software as a Service (SaaS)

SaaS comprises end-user applications delivered as a service rather than as traditional, on-premises software. The most commonly referenced example of SaaS is Salesforce.com, which provides a customer relationship management (CRM) system accessible via the Internet.

Platform as a Service (PaaS)

PaaS provides an application platform, or middleware, as a service on which developers can build and deploy custom applications. Common solutions provided in this tier range from APIs and tools to database and business process management systems to security integration, allowing developers to build applications and run them on the infrastructure that the cloud vendor owns and maintains. Microsoft's Windows Azure platform services are often referenced as PaaS solutions at this middleware tier.

Infrastructure as a Service (IaaS)

IaaS primarily encompasses the hardware and technology for computing power, storage, operating systems, or other infrastructure, delivered as off-premise, on-demand services rather than as dedicated, on-site resources such as the Amazon Elastic Compute Cloud (Amazon EC2).

Source:- ESRI Technology Topics

3D Motion Data Capturing Device for GIS

3D ArcGIS Explorer Desktop globe

The Microsoft Xbox 360 Kinect is one of most powerful consumer-oriented “Natural User Interface” devices available today. Its near-infrared camera produces 3D motion data of anything in front of the it and coupled with a standard webcam and quadraphonic microphone, the device is jammed pack with input sensors. The Microsoft Education team promotes Kinect and has prepared over a 100 lessons and activities to promote “active” learning. Microsoft also claims the Kinect may be useful as an assistive technology device and in promoting collaboration.

What you might not know is that the Kinect can plug to your computer and be used as an interface device!

Think about young students actively controlling a 3D ArcGIS Explorer Desktop globe – investigating the Earth while moving arms, legs, and torso to direct navigation, display data, or conduct an analysis. What an interesting way to engage young, energetic learners.

Last week, I demonstrated this concept at the meeting of the Esri Education Team. I connected my Kinect to my Windows laptop and we took turns controlling ArcGIS Explorer Desktop! To get the environment setup, I used the USC’s Institute for Creative Technologies recommendations. This set-up requires installing a set of drivers and then running the FAAST toolkit. Basically, FAAST allows you to create a mapping between Kinect-detected body movements to keyboard strokes. So, when I raise my right arm, the World spins right!

How to make the Kinect work for you:

• Acquire a stand-alone Xbox 360 Kinect or if you have a Kinect, just get an external USB power supply. Locate a computer with Windows 7, ArcGIS Explorer Desktop, and a free USB port. 
• Visit the USC Institute for Creative Technologies to install drivers and configure the Kinect. 
• Create your own mapping file or you can start with my simple mapping file.

Remember, these steps might require a little extra “tech-savvyness” and the FAAST toolkit from USC is an open source (neither a Microsoft nor Esri) project. Use at your own risk.

Post your comments and links using the Kinect to control ArcGIS Explorer Desktop below! Everyone should be able to create a fluid interaction with ArcGIS Explorer Desktop using the Kinect. Good luck!

Written By :-  Tom Baker,Esri Education Manager

Geospatial Technology Competency Model

The Department of Labor’s Geospatial Technology Competency Model (GTCM) is a milestone in the history of our field. Culminating a decade-long quest to define the U.S. geospatial industry and its workforce, the GTCM identifies the expertise that distinguishes, and binds together, successful geospatial professionals of all kinds. David DiBiase of Esri, the coordinator of the GTCM effort, lists the important facts about this endeavor, which was sanctioned by the U.S. Department of Labor.

It’s useful 

Students who aspire to careers in the geospatial industry can use the GTCM to assess what they know, what they need to learn and which educational programs fit their needs. Educators can use it to assess how well their curricula align with workforce needs. Workers can use it to guide their continuing professional development plans. Employers can use it for job descriptions and interviews. Certification and accreditation bodies can use it as a basis for their requirements.

To learn more about the GTCM and related efforts, see: 

•  DiBiase, D. and twelve others (2010). The New Geospatial Technology Competency Model: Bringing Workforce Needs into Focus. URISA Journal 22(2):55-72.
•  DOLETA (2010). Geospatial Technology Competency Model. Johnson, J. (2010). 
• What GIS Technicians Do: A Synthesis of DACUM Job Analyses. URISA Journal 22(2): 31-40.

Source:- Ten Things to Know about the Geospatial Technology Competency Model by David DiBiase Click here for details

Robots to map environment

The researchers used a PR2 robot,

developed by Willow Garage, with 

Microsoft's Kinect sensor to test

their system. 

Image: Hordur Johannsson

Robots could one day navigate through constantly changing surroundings with virtually no input from humans, thanks to a system that allows them to build and continuously update a three-dimensional map of their environment using a low-cost camera such as Microsoft’s Kinect.

The system, being developed by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), could also allow blind people to make their way unaided through crowded buildings such as hospitals and shopping malls.

To explore unknown environments, robots need to be able to map them as they move around — estimating the distance between themselves and nearby walls, for example — and to plan a route around any obstacles, says Maurice Fallon, a research scientist at CSAIL who is developing these systems alongside John J. Leonard, professor of mechanical and ocean engineering, and graduate student Hordur Johannsson.

But while a large amount of research has been devoted to developing one-off maps that robots can use to navigate around an area, these systems cannot adjust to changes in the surroundings over time, Fallon says: “If you see objects that were not there previously, it is difficult for a robot to incorporate that into its map.”

The new approach, based on a technique called Simultaneous Localization and Mapping (SLAM), will allow robots to constantly update a map as they learn new information over time, he says. The team has previously tested the approach on robots equipped with expensive laser-scanners, but in a paper to be presented this May at the International Conference on Robotics and Automation in St. Paul, Minn., they have now shown how a robot can locate itself in such a map with just a low-cost Kinect-like camera.

As the robot travels through an unexplored area, the Kinect sensor’s visible-light video camera and infrared depth sensor scan the surroundings, building up a 3-D model of the walls of the room and the objects within it. Then, when the robot passes through the same area again, the system compares the features of the new image it has created — including details such as the edges of walls, for example — with all the previous images it has taken until it finds a match.

At the same time, the system constantly estimates the robot’s motion, using on-board sensors that measure the distance its wheels have rotated. By combining the visual information with this motion data, it can determine where within the building the robot is positioned. Combining the two sources of information allows the system to eliminate errors that might creep in if it relied on the robot’s on-board sensors alone, Fallon says.

Once the system is certain of its location, any new features that have appeared since the previous picture was taken can be incorporated into the map by combining the old and new images of the scene, Fallon says.

The team tested the system on a robotic wheelchair, a PR2 robot developed by Willow Garage in Menlo Park, Calif., and in a portable sensor suit worn by a human volunteer. They found it could locate itself within a 3-D map of its surroundings while traveling at up to 1.5 meters per second.

Ultimately, the algorithm could allow robots to travel around office or hospital buildings, planning their own routes with little or no input from humans, Fallon says.

It could also be used as a wearable visual aid for blind people, allowing them to move around even large and crowded buildings independently, says Seth Teller, head of the Robotics, Vision and Sensor Networks group at CSAIL and principal investigator of the human-portable mapping project. “There are also a lot of military applications, like mapping a bunker or cave network to enable a quick exit or re-entry when needed,” he says. “Or a HazMat team could enter a biological or chemical weapons site and quickly map it on foot, while marking any hazardous spots or objects for handling by a remediation team coming later. These teams wear so much equipment that time is of the essence, making efficient mapping and navigation critical.”

While a great deal of research is focused on developing algorithms to allow robots to create maps of places they have visited, the work of Fallon and his colleagues takes these efforts to a new level, says Radu Rusu, a research scientist at Willow Garage who was not involved in this project. That is because the team is using the Microsoft Kinect sensor to map the entire 3-D space, not just viewing everything in two dimensions.

“This opens up exciting new possibilities in robot research and engineering, as the old-school ‘flatland’ assumption that the scientific community has been using for many years is fundamentally flawed,” he says. “Robots that fly or navigate in environments with stairs, ramps and all sorts of other indoor architectural elements are getting one step closer to actually doing something useful. And it all starts with being able to navigate.”

SeqSLAM: a visual-based algorithm for navigation

Dr Michael Milford from Queensland University of Technology's (QUT) Science and Engineering Faculty said his research into making more reliable Global Positioning Systems (GPS) using camera technology and mathematical algorithms would make navigating a far cheaper and simpler task.

"At the moment you need three satellites in order to get a decent GPS signal and even then it can take a minute or more to get a lock on your location," he said.

"There are some places geographically where you just can't get satellite signals and even in big cities we have issues with signals being scrambled because of tall buildings or losing them altogether in tunnels."

The world-first approach to visual navigation algorithms, which has been dubbed SeqSLAM (Sequence Simultaneous Localisation and Mapping), uses local best match and sequence recognition components to lock in locations.

"SeqSLAM uses the assumption that you are already in a specific location and tests that assumption over and over again.

"For example if I am in a kitchen in an office block, the algorithm makes the assumption I'm in the office block, looks around and identifies signs that match a kitchen. Then if I stepped out into the corridor it would test to see if the corridor matches the corridor in the existing data of the office block lay out.

"If you keep moving around and repeat the sequence for long enough you are able to uniquely identify where in the world you are using those images and simple mathematical algorithms."

However, the challenge was making those streets recognisable in a variety of different conditions and to differentiate between streets that were visually similar.

The research, which utilises low resolution cameras, was inspired by Dr Milford's background in the navigational patterns of small mammals such as rats.

"My core background is based on how small mammals manage incredible feats of navigation despite their eyesight being quite poor," he said.

"As we develop more and more sophisticated navigation systems they depend on more and more maths and more powerful computers.

"But no one's actually stepped back and thought 'do we actually need all this stuff or can we use a very simple set of algorithms which don't require expensive cameras or satellites or big computers to achieve the same outcome?'" 

 Dr Milford will present his paper SeqSLAM: Visual Route-Based Navigation for Sunny Summer Days and Stormy Winter Nights at the International Conference on Robotics and Automation in America later this year. 

The research has been funded for three years by an Australian Research Council $375,000 Discovery Early Career Researcher Award (DECRA) fellowship.