Modern vectorization and alignement of historical maps: An application to Paris atlas (1789-1950)

Authors

Yizi Chen

Place

Saint-Mandé, France

Type

phdthesis

Projects

Olena, SoDUCo"SoDUCo" is not in the list (Vaucanson, Spot, URBI, Olena, APMC, Tiger, Climb, Speaker ID, Transformers, Bison, ...) of allowed values for the "Related project" property.

Keywords

Image, Historical Maps, Vectorization, Instance Segmentation, Alignment, Deep Edge Filtering, WatershedClosed Shape Extraction

Date

2023-03-22

Bibtex (lrde.bib)

@PhDThesis{	  chen.23.phd,
  author	= {Yizi Chen},
  title		= {Modern vectorization and alignement of historical maps: An
		  application to Paris atlas (1789-1950)},
  school	= {Gustave Eiffel University},
  year		= {2023},
  type		= {phdthesis},
  address	= {Saint-Mand{\'e}, France},
  month		= mar,
  doi		= {FIXME},
  review	= {Maps have been a unique source of knowledge for centuries.
		  Such historical documents provide invaluable information
		  for analyzing complex spatial transformations over
		  important time frames. This is particularly true for urban
		  areas that encompass multiple interleaved research domains:
		  humanities, social sciences, etc. The large amount and
		  significant diversity of map sources call for automatic
		  image processing techniques in order to extract the
		  relevant objects as vector features. The complexity of maps
		  (text, noise, digitization artifacts, etc.) has hindered
		  the capacity of proposing a versatile and efficient
		  raster-to-vector approaches for decades. In this thesis, we
		  propose a learnable, reproducible, and reusable solution
		  for the automatic transformation of raster maps into vector
		  objects (building blocks, streets, rivers), focusing on the
		  extraction of closed shapes. Our approach is built upon the
		  complementary strengths of convolutional neural networks
		  which excel at filtering edges while presenting poor
		  topological properties for their outputs, and mathematical
		  morphology, which offers solid guarantees regarding closed
		  shape extraction while being very sensitive to noise. In
		  order to improve the robustness of deep edge filters to
		  noise, we review several, and propose new
		  topology-preserving loss functions which enable to improve
		  the topological properties of the results. We also
		  introduce a new contrast convolution (CConv) layer to
		  investigate how architectural changes can impact such
		  properties. Finally, we investigate the different
		  approaches which can be used to implement each stage, and
		  how to combine them in the most efficient way. Thanks to a
		  shape extraction pipeline, we propose a new alignment
		  procedure for historical map images, and start to leverage
		  the redundancies contained in map sheets with similar
		  contents to propagate annotations, improve vectorization
		  quality, and eventually detect evolution patterns for later
		  analysis or to automatically assess vectorization quality.
		  To evaluate the performance of all methods mentioned above,
		  we released a new dataset of annotated historical map
		  images. It is the first public and open dataset targeting
		  the task of historical map vectorization. We hope that
		  thanks to our publications, public and open releases of
		  datasets, codes and results, our work will benefit a wide
		  range of historical map-related applications.}
}