Weiwei Duan
Abstract
Historical maps store abundant and valuable information about the evolution of natural features and human activities, such as changes in hydrography, the development of the railroad networks, and the expansion of human settlements. Such knowledge represents a unique resource that can be extremely useful for researchers in the social and natural sciences to better understand how human and environment have evolved over time. Fortunately, a large amount of historical maps have been scanned in high resolution by many organizations. For example, the United States Geological Survey (USGS) has scanned and released more than 200,000 historical maps in the TIFF format.
- Y.-Y. Chiang and C.A Knoblock. 2013. A general approach for extracting road vector data from raster maps. IJDAR (2013), 1--27. Google Scholar
Digital Library
- Y.-Y. Chiang and C.A Knoblock. 2015. Recognizing text in raster maps. GeoInformatica 19, 1 (2015), 1--27. Google ScholarDigital Library
- E. Hancer, R. Samet, and D. Karaboga. 2014. A hybrid method to the reconstruction of contour lines from scanned topographic maps. In ISIE. IEEE, 930--933.Google Scholar
- T.C Henderson and T. Linton. 2009. Raster map image analysis. In ICDAR'09. IEEE, 376--380. Google ScholarDigital Library
- A. Krizhevsky, I. Sutskever, and G. E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In ANIPS. 1097--1105. Google ScholarDigital Library
- A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. 2014. CNN features off-the-shelf: an astounding baseline for recognition. In Proceedings of the IEEE conference on CVPR workshops. 806--813. Google ScholarDigital Library
- K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
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