Geometric road runoff estimation from laser mobile mapping data
Mountain roads are the lifelines of remote areas but are often situated in complicated settings and prone to landslides, rock fall, avalanches and damages due to surface water runoff. The impact and likelihood of these types of hazards can be partly assessed by a detailed geometric analysis of the r...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-05-01
|
| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5/385/2014/isprsannals-II-5-385-2014.pdf |
| _version_ | 1811276159252430848 |
|---|---|
| author | J. Wang H. Gonzalez-Jorge R. Lindenbergh P. Arias-Sanchez M. Menenti |
| author_facet | J. Wang H. Gonzalez-Jorge R. Lindenbergh P. Arias-Sanchez M. Menenti |
| author_sort | J. Wang |
| collection | DOAJ |
| description | Mountain roads are the lifelines of remote areas but are often situated in complicated settings and prone to landslides, rock fall,
avalanches and damages due to surface water runoff. The impact and likelihood of these types of hazards can be partly assessed by a
detailed geometric analysis of the road environment. Field measurements in remote areas are expensive however. A possible solution
is the use of a Laser Mobile Mapping System (LMMS) which, at high measuring rate, captures dense and accurate point clouds.
This paper presents an automatic approach for the delineation of both the direct environment of a road and the road itself into local
catchments starting from a LMMS point cloud. The results enable a user to assess where on the road most water from the surroundings
will assemble, and how water will flow over the road after e.g. heavy snow melt or rainfall. To arrive at these results the following steps
are performed. First outliers are removed and point cloud data is gridded at a uniform width. Local surface normal and gradient of each
grid point are determined. The relative smoothness of the road is used as a criterion to identify the road’s outlines. The local gradients
are input for running the so-called D8 method, which simply exploits that surface water follows the direction of steepest descent. This
method first enables the identification of sinks on the roadside, i.e. the locations where water flow accumulates and potentially enters
the road. Moreover, the method divides the road’s direct neighbourhood into catchments, each consisting of all grid cells having runoff
to the same sink. In addition the method is used to analyse the surface flow over the road’s surface. The new method is demonstrated
on a piece of 153 meters long Galician mountain road as sampled by LMMS data. |
| first_indexed | 2024-04-12T23:52:38Z |
| format | Article |
| id | doaj.art-80556dff34d14910b5752761de4434ad |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-04-12T23:52:38Z |
| publishDate | 2014-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-80556dff34d14910b5752761de4434ad2022-12-22T03:11:39ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502014-05-01II-538539110.5194/isprsannals-II-5-385-2014Geometric road runoff estimation from laser mobile mapping dataJ. Wang0H. Gonzalez-Jorge1R. Lindenbergh2P. Arias-Sanchez3M. Menenti4Dept. of Geoscience and Remote Sensing, Delft University of Technology Building 23, Stevinweg 1, P.O. Box 5048, 2628CN Delft, The NetherlandsKey Laboratory of Quantitative Remote Sensing Information Technology Academy of Opto-Electronics, Chinese Academy of Sciences No. 9 Deng Zhuang South Road, HaiDian District, 100094 Beijing, ChinaDept. of Geoscience and Remote Sensing, Delft University of Technology Building 23, Stevinweg 1, P.O. Box 5048, 2628CN Delft, The NetherlandsKey Laboratory of Quantitative Remote Sensing Information Technology Academy of Opto-Electronics, Chinese Academy of Sciences No. 9 Deng Zhuang South Road, HaiDian District, 100094 Beijing, ChinaDept. of Geoscience and Remote Sensing, Delft University of Technology Building 23, Stevinweg 1, P.O. Box 5048, 2628CN Delft, The NetherlandsMountain roads are the lifelines of remote areas but are often situated in complicated settings and prone to landslides, rock fall, avalanches and damages due to surface water runoff. The impact and likelihood of these types of hazards can be partly assessed by a detailed geometric analysis of the road environment. Field measurements in remote areas are expensive however. A possible solution is the use of a Laser Mobile Mapping System (LMMS) which, at high measuring rate, captures dense and accurate point clouds. This paper presents an automatic approach for the delineation of both the direct environment of a road and the road itself into local catchments starting from a LMMS point cloud. The results enable a user to assess where on the road most water from the surroundings will assemble, and how water will flow over the road after e.g. heavy snow melt or rainfall. To arrive at these results the following steps are performed. First outliers are removed and point cloud data is gridded at a uniform width. Local surface normal and gradient of each grid point are determined. The relative smoothness of the road is used as a criterion to identify the road’s outlines. The local gradients are input for running the so-called D8 method, which simply exploits that surface water follows the direction of steepest descent. This method first enables the identification of sinks on the roadside, i.e. the locations where water flow accumulates and potentially enters the road. Moreover, the method divides the road’s direct neighbourhood into catchments, each consisting of all grid cells having runoff to the same sink. In addition the method is used to analyse the surface flow over the road’s surface. The new method is demonstrated on a piece of 153 meters long Galician mountain road as sampled by LMMS data.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5/385/2014/isprsannals-II-5-385-2014.pdf |
| spellingShingle | J. Wang H. Gonzalez-Jorge R. Lindenbergh P. Arias-Sanchez M. Menenti Geometric road runoff estimation from laser mobile mapping data ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Geometric road runoff estimation from laser mobile mapping data |
| title_full | Geometric road runoff estimation from laser mobile mapping data |
| title_fullStr | Geometric road runoff estimation from laser mobile mapping data |
| title_full_unstemmed | Geometric road runoff estimation from laser mobile mapping data |
| title_short | Geometric road runoff estimation from laser mobile mapping data |
| title_sort | geometric road runoff estimation from laser mobile mapping data |
| url | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5/385/2014/isprsannals-II-5-385-2014.pdf |
| work_keys_str_mv | AT jwang geometricroadrunoffestimationfromlasermobilemappingdata AT hgonzalezjorge geometricroadrunoffestimationfromlasermobilemappingdata AT rlindenbergh geometricroadrunoffestimationfromlasermobilemappingdata AT pariassanchez geometricroadrunoffestimationfromlasermobilemappingdata AT mmenenti geometricroadrunoffestimationfromlasermobilemappingdata |