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Geospatial jobs of the week: US Army, GE, Cesium are hiring

If your company is looking for new talent and you want to share the opportunity with our community, feel free to submit a job using the online form for us to review and include in our list! If you would like to know more about our Geospatial Job Portal, read about it here.

If you are enthusiastic about location data or anything geospatial, then this is the job portal for you!

Looking for more positions in GIS, academia, product, or data science roles? Go directly to our searchable Geospatial Job Portal!

Featured Jobs

US Army Corps of Engineers: Cartographic Technician
📍 Buffalo, NY, US
You will need to perform a variety of cartographic duties associated with the District’s navigation and dredging program. For example, executing technical tasks, including routine cartographic and drafting work at full production level and some cartographic work of moderate difficulty. You will also need to lay out multiple sheet sets of maps that would require knowledge of map purpose and appropriate level of detail. Another task could be to select routine details that will be shown on maps to assist project engineers, dredging contractors and using public, taking into consideration the intended map usage, and in few cases using judgment in placement of details.

Bureau of Land Management: MLRS GIS Team Lead
📍 Boise, ID, US

Tucson Electric Power: T&D Application Analyst (GIS Support)
📍 Tucson, AZ, US

Amazon: Sr. Real Estate Research Manager
📍 Seattle, WA, US

Cesium: Software Project Manager
📍 Philadelphia, PA, US

GE Renewable Energy: Project Management Specialist
📍 Jakarta, Indonesia

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Qatar is banking on AI to improve GPS navigation for 2022 FIFA World Cup

AI GPS navigation digital maps

Al Rayyan Stadium, Qatar

The 22nd edition of the world’s biggest football tournament has been awarded to Qatar, the richest country in the world. But, despite having the highest per capita income on the globe, the West Asian country is also the smallest nation by area ever to host a FIFA World Cup.

Qatar is aware that it is yet to attain preeminence in the eyes of the big geospatial companies that build digital maps, the torchbearers of modern-day navigation. So, as it prepares for the 2022 FIFA World Cup, the Arab Gulf nation is looking to leverage artificial intelligence in a quest to improve GPS navigation.

Qatar is constantly constructing new roads and improving old ones. And yet, when Massachusetts Institute of Technology Professor Samuel Madden visited the nation, he discovered that his Uber driver was unable to figure out how to get where he was going because the map was completely off.

It was then that he realized the importance of a machine learning model MIT was developing in collaboration with the Qatar Computing Research Institute (QCRI). This model can automatically tag road features in satellite imagery and even predict the number of lanes and road types (residential or highway) behind obstructions, such as trees.

AI GPS Navigation digital maps

An AI model developed at MIT and Qatar Computing Research Institute that uses only satellite imagery to automatically tag road features in digital maps could improve GPS navigation, especially in countries with limited map data. Courtesy: Google Maps/MIT News

Creating highly-accurate and up-to-date maps is both expensive and time-consuming. Only a handful of big companies, like Google and Apple, can afford to send mapping vehicles across the globe to capture video and images through cameras strapped to their hoods. Which is likely why some parts of the world get ignored in the process.

“If navigation apps don’t have the right information, for things such as lane merging, this could be frustrating or worse,” Madden says. “Most updated digital maps are from places that big companies care the most about. If you’re in places they don’t care about much, you’re at a disadvantage with respect to the quality of the map.”

Initial tests have shown that MIT and QCRI’s artificial intelligence model, dubbed RoadTagger, can count lane numbers with 77% accuracy and inferred road types with 93% accuracy. The researchers are working to improve the model and get it to predict other road features as well, for example, parking spots and bike lanes. “Our goal is to automate the process of generating high-quality digital maps, so they can be available in any country,” Madden sums up.

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