What graphical classes should be annoted on a cadastral map to automatise their processing ?

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We propose a minimalistic ontology containing five classes that are shared between all historical cadasters :

  1. Buildings, including city blocks, detached houses, walls, etc. ;
  2. Road network, including roads, streets, railroads, bridges, etc.
  3. Water, i.e. rivers, canals, bodies of water, reservoirs, lakes, fountains, and sea;
  4. Non-built, which includes all unbuilt land except water and road network, including courtyards, parks, enclosed squares or inner courtyards, crops, forests, wasteland, meadows, etc. ;
  5. Contours, i.e. parcels or any object delimitation, even when the shape is not strictly closed.

The background, which is not counted, includes all the non-cartographic content of the image, such as the background of the scanner, the map frame, the title, the legend or potential illuminations. Each of the five classes in the ontology is clearly distinct. Together, they also constitute the essential building blocks for urban analysis and historical 4D reconstruction. Moreover, their colors (see below) can be mixed easily when a multilabel annotation is needed, for example to annotate a portico (buildings and road network), a bridge (road network and water), a marsh (water and non-built), or a building on piles (buildings and water).

Class
Color
HEX code
Wikidata

Buildings
Magenta
#FF00FF
Q41176

Road network
Yellow
#FFFF00
Q358078

Water
Dark blue
#0000FF
Q283

Non-built
Cyan
#00FFFF

Contours
White
#FFFFFF

Background
Black
#000000
Q13217555

Other classes, such as forest, field type or road network could be hierarchically subordinated by means of wikidata tags, for the needs of specific researches, without altering the interoperability.

This annotation ontology is conceived in order to prevent the limitations potentially caused by the difficult distinction of the graphical elements, such as parcel boundaries or nature of the unbuilt areas, as discussed above. Indeed, by proposing to handle all contours as a single class, for instance, we prevent the arduous work to differentiate all types of contours found in Figs. 1.1-1.3 for example, which we consider would be both too ambitious in the context of collaborative annotation and inadequate for neural-network-based semantic segmentation. This relative simplicity is undoubtedly necessary in order to allow a strong coherence between the annotations produced, given the number of people who could potentially be involved in this process.

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