Automatic extraction of buildings utilizing geometric features of a scanned topographic map

Miyoshi, Takaharu; Li, Weiqing; Kaneda, Kazufumi; Yamashita, Hideo; Nakamae, Eihachiro

doi:10.1109/icpr.2004.1334607

Cited by 8 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…by height lines. However, note that algorithms exist to perform this process automatically and to also identify additional features from the map [Arrighi and Soille 1999;Miyoshi et al 2004]. The resulting vegetation cover map contains values between 0 for non arboreous regions and 1 for regions identified as forests.…”

Section: Identification Of Arboreous Regionsmentioning

confidence: 99%

Interactive landscape visualization using GPU ray casting

Mantler

Jeschke

2006

Proceedings of the 4th International Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast As

View full text Add to dashboard Cite

Figure 1: Rendering a landscape with orthophotographic data. Left: textured and unlit. Center: textured and lighted. Right: The GPU ray casting approach presented in this paper. AbstractThis paper demonstrates the simple yet effective usage of height fields for interactive landscape visualizations using a ray casting approach implemented in the pixel shader of modern graphics cards. The rendering performance is output sensitive, i.e., it scales with the number of pixels rather than the complexity of the landscape. Given a height field of a terrain and a topographic map or similar data as input, the vegetation cover is extracted and stored on top of the height field in a preprocess, enhancing the terrain with forest canopies or other mesostructure. In addition, enhanced illumination models like shadowing and ambient occlusion can be calculated at runtime with reasonable computational cost, which greatly enhances the scene realism. Finally, including the presented technique into existing rendering systems is relatively simple, mainly consisting of data preparation and pixel shader programming.

show abstract

Section: Identification Of Arboreous Regionsmentioning

confidence: 99%

Interactive landscape visualization using GPU ray casting

Mantler

Jeschke

2006

Proceedings of the 4th International Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast As

View full text Add to dashboard Cite

show abstract

“…The application of recognition methods to map documents often faces specific challenges compared to traditional document analysis due to low graphical quality and complex, human-made map content (e.g., overlapping cartographic symbols) [19]. Example applications of map processing include the extraction of buildings [20]- [22], residential areas [23], road networks [24] contour lines [25], [26], composite forest symbols [27], text [28] as well as the digitization of cadastral maps [29]. Successful map processing requires georeferencing ( [30]- [35]) and the alignment of georeferenced maps and ancillary spatial data ( [36], [37], see [5], [38] for detailed overviews).…”

Section: Background a Map Processingmentioning

confidence: 99%

Automated Extraction of Human Settlement Patterns From Historical Topographic Map Series Using Weakly Supervised Convolutional Neural Networks

et al. 2020

View full text Add to dashboard Cite

Information extraction from historical maps represents a persistent challenge due to inferior graphical quality and the large data volume of digital map archives, which can hold thousands of digitized map sheets. Traditional map processing techniques typically rely on manually collected templates of the symbol of interest, and thus are not suitable for large-scale information extraction. In order to digitally preserve such large amounts of valuable retrospective geographic information, high levels of automation are required. Herein, we propose an automated machine-learning based framework to extract human settlement symbols, such as buildings and urban areas from historical topographic maps in the absence of training data, employing contemporary geospatial data as ancillary data to guide the collection of training samples. These samples are then used to train a convolutional neural network for semantic image segmentation, allowing for the extraction of human settlement patterns in an analysis-ready geospatial vector data format. We test our method on United States Geological Survey historical topographic maps published between 1893 and 1954. The results are promising, indicating high degrees of completeness in the extracted settlement features (i.e., recall of up to 0.96, F-measure of up to 0.79) and will guide the next steps to provide a fully automated operational approach for large-scale geographic feature extraction from a variety of historical map series. Moreover, the proposed framework provides a robust approach for the recognition of objects which are small in size, generalizable to many kinds of visual documents.INDEX TERMS Convolutional neural networks, digital humanities, digital preservation, document analysis, geospatial analysis, geospatial artificial intelligence, human settlement patterns, image analysis, weakly supervised learning.

show abstract

Road and Road Intersection Extraction

Henderson

2013

Analysis of Engineering Drawings and Raster Map Images

View full text Add to dashboard Cite

Automatic extraction of buildings utilizing geometric features of a scanned topographic map

Cited by 8 publications

References 3 publications

Interactive landscape visualization using GPU ray casting

Interactive landscape visualization using GPU ray casting

Automated Extraction of Human Settlement Patterns From Historical Topographic Map Series Using Weakly Supervised Convolutional Neural Networks

Road and Road Intersection Extraction

Contact Info

Product

Resources

About