ACADIA 2017

Tangible immersion for ecological design

Payam Tabrizian, Brendan Harmon, Anna Petrasova, Vaclav Petras, Helena Mitasova

Tangible interfaces for geospatial modeling

Couple physical and digital geospatial models

Augmented architectural models

Urp, 1996-2001

John Underkoffler and Hiroshi Ishii. 1999. Urp: a luminous-tangible workbench for urban planning and design. In CHI ’99 Proceedings of the SIGCHI conference on Human Factors in Computing Systems. New York, New York, USA: ACM Press, 386–393. DOI:http://dx.doi.org/10.1145/302979.303114

Source: Tangible Media Group, MIT Media Lab

Augmented sandboxes

Sandscape & Illuminating Clay, 2002-2004

H. Ishii, C. Ratti, B. Piper, Y. Wang, A. Biderman, and E. Ben-Joseph. 2004. Bringing Clay and Sand into Digital Design — Continuous Tangible user Interfaces. BT Technol. J. 22, 4 (2004), 287–299. DOI:http://dx.doi.org/10.1023/B:BTTJ.0000047607.16164.16

Source: Tangible Media Group, MIT Media Lab

Actuated pin tables

XenoVision Mark III Dynamic Sand Table, 2004

Source: Xenotran

Augmented sandboxes

Tangible Geospatial Modeling System, 2006-2010

Laura Tateosian, Helena Mitasova, Brendan A. Harmon, Brent Fogleman, Katherine Weaver, and Russell S. Harmon. 2010. TanGeoMS: Tangible Geospatial Modeling System. IEEE Trans. Vis. Comput. Graph. 16, 6 (2010), 1605–12. DOI:http://dx.doi.org/10.1109/TVCG.2010.202

Source: NCSU GeoForAll Lab

Augmented architectural models

Collaborative Design Platform, 2011-present

Gerhard Schubert, Sebastian Riedel, and Frank Petzold. 2013. Seamfully connected: Real working models as tangible interfaces for architectural design. In Global Design and Local Materialization. Springer-Verlag Berlin Heidelberg, 210–221. DOI:http://dx.doi.org/10.1007/978-3-642-38974-0_20

Source: Dr.-Ing. Gerhard Schubert, Technische Universität München

Augmented sandboxes

Augmented Reality Sandbox, 2012-present

Source: Oliver Kreylos, UC Davis

Actuated pin tables

inFORM, 2013-present

Sean Follmer, Daniel Leithinger, Alex Olwal, Akimitsu Hogge, and Hiroshi Ishii. 2013. inFORM: dynamic physical affordances and constraints through shape and object actuation. In Proceedings of the 26th annual ACM symposium on User interface software and technology - UIST ’13. New York, New York, USA: ACM Press, 417–426. DOI:http://dx.doi.org/10.1145/2501988.2502032

Source: Tangible Media Group, MIT Media Lab

Augmented sandboxes

The Augmented REality Sandtable (ARES), 2015-present

Charles R. Amburn, Nathan L. Vey, Michael W. Boyce, and MAJ Jerry R. Mize. 2015. The Augmented REality Sandtable ( ARES ). US Army Research Laboratory. ARL-SR-0340. DOI:http://dx.doi.org/10.13140/RG.2.1.2685.0006

Source: US Army Research Laboratory

Tangible Landscape

A tangible user interface powered by open source GIS

2013-present

Tangible interaction with GIS

With Tangible Landscape you can hold a GIS in your hands - feeling the shape of the earth, sculpting its topography, and directing the flow of water.

How it works ?

GRASS GIS

Geographic Information System
Free and open source
Extensive Library
Easy scripting in Python, fast algorithms in C/C++

Blender

3D modeling, rendering, animation, physics, and game engine
Free and open source, easy scripting in Python
GIS and VR plugins
Real-time raytraced rendering

Software Architecture

Briefly describing the workflow, GRASS GIS and Blender are coupled either through loose file-based communication or with Sockets through network. As user interacts with the tangible model or objects, the depth and color information is scanned using kinenct or a similar 3D sensor. The scanned informartion is processed is used as input for geodpatial analysis in GRASS GIS. Examples include hyrodolgical simulations, erosion, sedimentation, landform detection, fire spread and so on. The analysis output is then projected as a map back onto the model or as numerical ouput displayed next to the model. In blender, We implemented a monitoring module that constantly watches the directory, identifies the type of incoming information, and applies relevant operations needed to update the 3d model. The input data can range from geospatial features like a raster or a point cloud, simple coordinates as a text file, or signals that prompt a command such as removing an object from the scene.

Interactions

surface

points

lines

areas

Landform and water

Vegetation

Users can design tree patches using colored felt pieces. They can either draw and cut their prefered shapes using scissors, or select from a library of cutouts with various shapes. Each color represents a landscape class based on National landcover datast classification, like decidous, evergreen etc. For instance in this example green denotes evergreen class and eastern pine trees, red denotes decidous and red maple and blue represents river birch which is suitable for soil remediation . Grass GIS performs image segmentation and classification to the scanned image to assign RGB values to their corresponding landscape classes. Using landscape structure analysis we compute and project various metrics related to landscape heterogeneity, biodiversity and complexity, which as you can see is projected below the landscape model. After importing, Blender applies a particle system modifier to populate corresponding species in each patch based on a predefined spacing and density,related to each specie. Some degree of randomness is applied to the size, rotation and sucsession of species to mimic the realworld representation of a patch.