Polygon consensus

Budig, Benedikt; Dijk, Thomas C. van; Feitsch, Fabian; Arteaga, Mauricio Giraldo

doi:10.1145/2996913.2996951

Cited by 11 publications

(1 citation statement)

References 11 publications

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“…As noted by [9], the manual approach is still a popular solution in digitizing maps when the dataset is small in coverage and time period. For larger datasets, collaborative approaches are often adopted with possibly many contributors-so-called crowdsourcing experiments, as in the works of [10,11]-to speed up the digitization process. However, manual processes are still limited in time and quality is highly fluctuating through different contributors which leads to non-reproducible and heterogeneous results.…”

Section: Map Digitizationmentioning

confidence: 99%

Automatic vectorization of historical maps: A benchmark

Chen,

Chazalon,

Carlinet

et al. 2024

PLoS ONE

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Shape vectorization is a key stage of the digitization of large-scale historical maps, especially city maps that exhibit complex and valuable details. Having access to digitized buildings, building blocks, street networks and other geographic content opens numerous new approaches for historical studies such as change tracking, morphological analysis and density estimations. In the context of the digitization of Paris atlases created in the 19th and early 20th centuries, we have designed a supervised pipeline that reliably extract closed shapes from historical maps. This pipeline is based on a supervised edge filtering stage using deep filters, and a closed shape extraction stage using a watershed transform. It relies on probable multiple suboptimal methodological choices that hamper the vectorization performances in terms of accuracy and completeness. Objectively investigating which solutions are the most adequate among the numerous possibilities is comprehensively addressed in this paper. The following contributions are subsequently introduced: (i) we propose an improved training protocol for map digitization; (ii) we introduce a joint optimization of the edge detection and shape extraction stages; (iii) we compare the performance of state-of-the-art deep edge filters with topology-preserving loss functions, including vision transformers; (iv) we evaluate the end-to-end deep learnable watershed against Meyer watershed. We subsequently design the critical path for a fully automatic extraction of key elements of historical maps. All the data, code, benchmark results are freely available at https://github.com/soduco/Benchmark_historical_map_vectorization.

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Section: Map Digitizationmentioning

confidence: 99%

Automatic vectorization of historical maps: A benchmark

Chen,

Chazalon,

Carlinet

et al. 2024

PLoS ONE

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Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction

Chen

Carlinet

Chazalon

et al. 2021

Lecture Notes in Computer Science

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Maps have been a unique source of knowledge for centuries. Such historical documents provide invaluable information for analyzing the complex spatial transformation of landscapes over important time frames. This is particularly true for urban areas that encompass multiple interleaved research domains (social sciences, economy, etc.). The large amount and significant diversity of map sources call for automatic image processing techniques in order to extract the relevant objects under a vectorial shape. 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. We propose a learnable, reproducible, and reusable solution for the automatic transformation of raster maps into vector objects (building blocks, streets, rivers). It is built upon the complementary strength of mathematical morphology and convolutional neural networks through efficient edge filtering. Evenmore, we modify ConnNet and combine with deep edge filtering architecture to make use of pixel connectivity information and built an end-to-end system without requiring any post-processing techniques. In this paper, we focus on the comprehensive benchmark on various architectures on multiple datasets coupled with a novel vectorization step. Our experimental results on a new public dataset using COCO Panoptic metric exhibit very encouraging results confirmed by a qualitative analysis of the success and failure cases of our approach. Code, dataset, results and extra illustrations are freely available at https://github.com/soduco/ICDAR-2021-Vectorization.

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