{"id":1958,"date":"2024-07-24T11:02:31","date_gmt":"2024-07-24T09:02:31","guid":{"rendered":"https:\/\/ocad.com\/blog\/?p=1958"},"modified":"2024-07-24T11:19:54","modified_gmt":"2024-07-24T09:19:54","slug":"feature-map-get-more-out-of-your-lidar-data","status":"publish","type":"post","link":"https:\/\/ocad.com\/blog\/2024\/07\/feature-map-get-more-out-of-your-lidar-data\/","title":{"rendered":"Feature Map – Get more out of your LiDAR Data"},"content":{"rendered":"\n

In the latest OCAD Update, the option Create Feature Map<\/a><\/strong> was added to the DEM Wizard to gain even more information from LiDAR data.<\/p>\n\n\n\n

The Feature Map<\/em> can be used to recognize objects close to the ground such as stones, walls, tree trunks, fences, or cars which were previously not or only poorly recognizable on background maps that can be created with the DEM Wizard<\/a> in OCAD.<\/p>\n\n\n\n

How the creation of Feature Maps works<\/strong><\/p>\n\n\n\n

In a LiDAR file, each point is typically assigned to a class based on the reflection of the laser pulse, such as ground, vegetation, buildings or water. Only ground points are used to generate the Hill Shading and Slope Gradient map. This means that a lot of information is lost that is available in points classified differently.<\/p>\n\n\n\n

To generate the Feature Map<\/em>, OCAD reclassifies the LiDAR points so that not only ground points but all points are used for the calculation. You can further define from and up to which height points are taken into account. <\/p>\n\n\n\n

Example stone detection<\/strong>
Pontresina, Switzerland
LiDAR data from 2022, 29 points per square meter, 0.5m cell size, 0.0-2.0m threshold.<\/p>\n\n\n\n

\"\"<\/a>
Orienteering Map of stony terrain.<\/figcaption><\/figure>\n\n\n\n
\"\"<\/a>
Slope Gradient Map. Most stones are not visible. The stones that are visible are probably overgrown and therefore classified as ground points.<\/figcaption><\/figure>\n\n\n\n
\"\"<\/a>
Feature Map. Some stones becomes clearly visible.<\/figcaption><\/figure>\n\n\n\n

Example stone wall detection<\/strong>
Jura, Switzerland
LiDAR data from 2020, 25 points per square meter, 0.5m cell size, 0.0-1.5m threshold.<\/p>\n\n\n\n

\"\"<\/a>
Slope Gradient Map. The stone walls are only partially visible.<\/figcaption><\/figure>\n\n\n\n
\"\"<\/a>
Feature Map. The stone walls become clearly visible.<\/figcaption><\/figure>\n\n\n\n

Example tree trunks detection <\/strong>
S-chanf, Switzerland
LiDAR data from 2022, 25 points per square meter, 0.5m cell size, 0.0-0.5m threshold. <\/p>\n\n\n\n

\"\"<\/a>
Slope Gradient Map.<\/figcaption><\/figure>\n\n\n\n
\"\"<\/a>
Feature Map. Tree trunks are visible and can help to determine the exact position.<\/figcaption><\/figure>\n\n\n\n

Example urban area<\/strong>
Zeiningen, Switzerland
LiDAR data from 2020, 13 points per square meter, 0.5m cell size, 0.0-2.0m threshold. <\/p>\n\n\n\n

\"\"<\/a>
Slope Gradient Map.<\/figcaption><\/figure>\n\n\n\n
\"\"<\/a>
Feature Map. Hedges, walls, cars and bridges becomes visible.<\/figcaption><\/figure>\n\n\n\n
\"\"<\/a>
Vegetation Height Map. Most of the objects on the Feature Map are also visible on this map.<\/figcaption><\/figure>\n\n\n\n

Example without satisfactory result<\/strong>
Lillehammer, Norway
LiDAR data from 2017, 10 points per square meter, 0.75m cell size, 0.0-2.0m threshold. <\/p>\n\n\n\n

\"\"<\/a>
Slope Gradient Map.<\/figcaption><\/figure>\n\n\n\n
\"\"<\/a>
Feature Map. In this area there are many small trees and bushes with branches down to the ground. It is difficult to distinguish, for example, large stones from trees or knolls from this data.<\/figcaption><\/figure>\n\n\n\n
\"\"<\/a>
Vegetation Height Map of the same area.<\/figcaption><\/figure>\n\n\n\n

Conclusion:<\/strong><\/p>\n\n\n\n

The information content of the feature map depends on the terrain type, the settings you choose in the dialog and the quality of the LiDAR data, in particular the point density. With good data quality, the feature map<\/em> can be a useful addition to the existing background maps, to detect objects and help the cartographer to determine the exact position in the terrain.<\/p>\n\n\n\n

Credit goes to Jeff Teutsch<\/a> and his Lidar Case Study – Using simple ground reclassification to see features in data<\/em>.<\/p>\n","protected":false},"excerpt":{"rendered":"

In the latest OCAD Update, the option Create Feature Map was added to the DEM Wizard to gain even more information from LiDAR data. The Feature Map can be used to recognize objects close to the ground such as stones, walls, tree trunks, fences, or cars which were previously not or only poorly recognizable on […]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11,35,46,1],"tags":[49,55,9],"class_list":["post-1958","post","type-post","status-publish","format-standard","hentry","category-ocad-11","category-ocad-12","category-ocad-2018","category-uncategorized","tag-dem-wizard","tag-lidar","tag-orienteering-map-making"],"_links":{"self":[{"href":"https:\/\/ocad.com\/blog\/wp-json\/wp\/v2\/posts\/1958","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ocad.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ocad.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ocad.com\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/ocad.com\/blog\/wp-json\/wp\/v2\/comments?post=1958"}],"version-history":[{"count":8,"href":"https:\/\/ocad.com\/blog\/wp-json\/wp\/v2\/posts\/1958\/revisions"}],"predecessor-version":[{"id":1983,"href":"https:\/\/ocad.com\/blog\/wp-json\/wp\/v2\/posts\/1958\/revisions\/1983"}],"wp:attachment":[{"href":"https:\/\/ocad.com\/blog\/wp-json\/wp\/v2\/media?parent=1958"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ocad.com\/blog\/wp-json\/wp\/v2\/categories?post=1958"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ocad.com\/blog\/wp-json\/wp\/v2\/tags?post=1958"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}