Modern cartography and the 3D Map Revolution | Rachel Hwang | TEDxPenn

Rachel Hwang is an engineer and an artist. As such, she leads a global effort to develop powerful new platforms for visualizing geospatial data. Through Cesium, an open-source software platform, Rachel engages the global community to develop and optimize 3D maps in a way that is applicable to a variety of fields, from designing buildings to studying asteroids millions of miles away.
A graduate from Penn’s Computer Graphics and Game Technology Masters program, Rachel is now a software developer on the Cesium team at Analytical Graphics Inc, which has provided 3D dynamic-data visualization for the space and defense industries. She has also designed and now leads a procedural graphics course at Penn, in which she teaches the power of using programming as a creative medium at the intersection of art and technology.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at www.ted.com/tedx

[@edwardmartin6052]

Real-time 3D cartography will be the next advancement!

[@arnoldasl52]

Great Info, thanks...

[@verenairenewimmerriedokule4039]

we work on the insturction the gis-browser as a member

[@nulu1565]

Interesting

[@wondimagegnteshome275]

waw!!! this is a better for those who get with free service and accesseabl easily, but for those as me living in third world is too difficalt.

[@recemarkou3223]

3rd?

[@fredrikedler5456]

5??

[@raanobrega]

Back in 2007, the opening section of the American Society for Photogrammetry and Remote Sensing Conference in Tampa-FL, Google presented the already developed Google Earth, and Microsoft introduced the Virtual Earth, an ambitious project that planned to map the 3,000 major big cities of the world in just 5 years. Later on in the conference, I had the opportunity to I spoke to the Microsoft project leader and told him that there is much more in geospatial information and mapping than simply collecting and processing data remotely. I told him that the Virtual Earth project, besides all computational support, was faded to fail due to the lack of geodata, ground-collected data, and local authorization for mapping. Three months later, I received a call from Microsoft offering me a position. Unfortunately, we never move it on.

[@jorisboelens3305]

1st

[@SahitiSeemakurti]

Idk, most of it felt like common sense

[@rajabannisaairowahyuni2950]

tolong dong gua ga ngerti dia ngomong apa :(

[@makmudinid4580]

:D

[@mutiaralutfiah5037]

Raraaaa😭😭😭

[@adityaanandapriatna4276]

Sama

[@henryqng]

2nd :)

[@ad2181]

Is their hair over my eye?

[@j.macjordan9779]

Creating robust enough software capable of stitching images of all kinds is desirable, but I see limitations based on copyright slowing this down. Using Intelligent Systems to crawl the web for images, even if they are open for everyone to see, people will cry foul. Even if an image is scanned and only 10% of the total image yields useful data, is stitching that in problematic? I think it would be. But having the software and reconciling data usage, instead of relying on proactive, open source contributions by a minority, could really allow something incredible to emerge. Indeed, I would say that this would be one major component for the emergence of A.I. (GOFAI). A computer that is just a computer, with no interaction with the real world other than a keyboard input and a screen as output suddenly can jump forward with massive real world data. The simulation that outstrips reality as humans experience reality is one piece sufficient for A.I. to surpass human intelligence. It's an instant answer to Searle's counter to Turing, and renders the Robot Argument in response to Searle unnecessary. But, with mm resolution scans via Synthetic Aperture Radar, perhaps these models can produce good enough simulations. Once A.I. emerges, copyright data is a non issue - once it emerges, it can build iteration after iteration, pulling all the data it can access, to build an understanding of the world beyond any human. In a sense, we've all been setting the stage for A.I. - all the images and image data on social media alone, a fast enough computer with the software for self editing, self improvement is all that's needed. And that doesn't consider all the other data on the web that will further enhance A.I.'s self-evolution.