Home Exploiting Context in Cartographic Evolutionary Documents to Extract and Build Linked Spatial-Temporal Datasets

 CCSheaderCCS2016

CCS’16 satellite session “Complexity for History and History for Complexity”

Beurs Van Berlage, Amsterdam, The Netherlands. September 21, 2016

Abstract

chiangabstract

Millions of historical maps are in digital archives today. For example, the U.S. Geological Survey has created and scanned over 200,000 topographic maps covering a 125-year period. Maps are a form of “evolutionary visual documents” because they display landscape changes over long periods of time and across large areas. Such documents are of tremendous value because they provide a high-resolution window into the past at a continental scale. Unfortunately, without time-intensive manual digitization scanned maps are unusable for research purposes. Map features, such as wetlands and roads, while readable by humans, are only available as images. This project will develop a set of open-source technologies and tools that allow users to extract map features from a large number of map sheets and track changes of features between map editions in a Geographical Information System. The resulting open-source tools will enable exciting new forms of inquiry in history, demography, economics, sociology, ecology, and other disciplines. The data produced by this project will be made publically available and through case studies integrated with other historical archives. Spatially and temporally linked knowledge covering man-made and natural features over more than 125 years holds enormous potential for the physical and social sciences. The wealth of information contained in these maps is unique, especially for the time before the widespread use of aerial photography. The ability to automatically transform the scanned paper maps stored in large archives into spatio-temporally linked knowledge will create an important resource for social and natural scientists studying global change and other socio-geographic processes that play out over large areas and long periods of time. Publications, software, and datasets for this project will be made available on the project website:
http://spatial-computing.github.io/unlocking-spatiotemporal-map-data.

 

Speaker Bio

Chiang

Yao-Yi Chiang, PhD

Assistant Professor (Research) of Spatial Sciences and Data Informatics, Spatial Sciences Institute, David and Dana Dornsife College of Letters, Arts and Sciences, University of Southern California
Data Informatics, Viterbi School of Engineering, University of Southern California
(3616 Trousdale Parkway, University of Southern California, Los Angeles, CA 90089-0374)
E-mail: yaoyic@usc.edu

Yao-Yi Chiang is an Assistant Professor (Research) of both Spatial Sciences and Data Informatics at the University of Southern California (USC). He is also a Visiting Computer Scientist at the USC Information Sciences Institute. Dr. Chiang received his Master and Ph.D. degrees in Computer Science from the University of Southern California in 2004 and 2010, respectively; his Bachelor degree in Information Management from the National Taiwan University in 2000. His general area of research is in information integration with a focus on discovering, extracting, modeling, and linking spatiotemporal data from heterogeneous sources. In particular, Dr. Chiang is an expert in digital map processing and geospatial information system (GIS). Using algorithms and geospatial technology applications, he is pioneering novel techniques to unlock historical data from maps on a large scale. He is the first to study and implement these techniques, which enable efficient utilization of historical map content on a large scale. He has published many articles on automatic techniques for geospatial data extraction and integration. As evidence of the innovativeness of Dr. Chiang’s work, his publication “Querying Historical Maps as a Unified, Structured, and Linked Spatiotemporal Source” won first place as the best visionary award sponsored by the Computing Research Association’s Computing Community Consortium Blue Sky initiative at the 2015 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. This award recognizes “the quality of the visionary ideas, long-term challenges, and opportunities in research that are outside of the current mainstream topics of the field.” In addition to research, Dr. Chiang also teaches subjects in data mining, spatial databases, and GIS programming at USC. Prior to USC, Dr. Chiang worked as a research scientist for Geosemble Technologies (now TerraGo Technologies), which was founded based on a patent on geospatial data fusion techniques, and he was a co-inventor.