Geoinformatics for Urban Growth and Resilience

a. Urban growth modeling and prediction

• Using cellular automata to approximate urban growth
• Updating approximations through spatial and temporal calibration

Figure. Ground data and modelling results in three selected area of Indianapolis for 1987 (left) and 1992 (right).

b. Disaster mapping from space

2008 Southern Indiana Floods

• Estimating the potential damages or impact to major standing crops, roads and streets by flood mapping.
• Publishing results on the Web to visualize the geospatial distribution and extent of floods in a timely way.

Figure. (a) during flood Landsat image, (b) flood extent map, (c) flood affected crops and (d) flood affected roads for the southwestern Indiana June 2008 floods.

2010 Haiti Earthquake

• Performing an object-based one-class-at-a-time land cover classification of GeoEye-1 image and airborne lidar data.
• Mapping buildings and their rubble caused by Haiti earthquake.

Figure. (a) subset of the GeoEye image around the Presidential Palace, (b) mapped building footprints, (c) 3D building models, and (d) 3D building models draped over a Google Earth image.

Building-scale population mapping

• Using aerial imagery and GIS data to generate population mapping for individual buildings.

Figure. Estimated building populations of 2005 for Wea township with the weighted areametric model.