Automatic extraction of digital terrain model and Building Footprint from airborne LiDAR data using rule-based learning techniques
Topographic information such as feature maps and digital terrain models (DTM) has always been a basic requirement in many engineering sciences. This is even more important in urban environments, because it is very difficult to update city maps, and this is more difficult in large cities due to th...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/114838/1/114838.pdf http://psasir.upm.edu.my/id/eprint/114838/ http://ethesis.upm.edu.my/id/eprint/18188 |
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| Summary: | Topographic information such as feature maps and digital terrain models (DTM) has
always been a basic requirement in many engineering sciences. This is even more
important in urban environments, because it is very difficult to update city maps, and
this is more difficult in large cities due to the high rate of change. On the other hand,
there is an increasing need for up-to-date maps, and this need is greater in larger cities
due to the numerous map-related land uses. However, updating the maps takes a long
time and incurs huge costs, which will prevent it from being done in short periods of
time. Therefore, in this research an algorithm has been created which can achieve the
following goals.
1) To generate DTM only with LiDAR data without the need for layers and other
information from the area
2) To create a building footprint from the LiDAR data by removing the tree cover
effect
3) To create an automatic system that can perform the production process of DTM and
footprint without the intervention of an expert.
To achieve the first goal, the last reflection is separated from the LiDAR point cloud and
the effective distance was calculated. In the next step, noise and roof errors were
removed using KNN filter and a new network was created and re-evaluated based on the
shortest distance in the LiDAR point cloud to create an integrated DTM. Finally, DTM
that has been generated in this research compared by DTM that was created manually.
In the next step, after taking the filtering steps, the Buildings Footprint was created and
was saved as a vector file in the output path by keeping the first reflectance, filtering the
nearest neighbor, filtering based on intensity, creating a new network, applying the height filter, filtering based on a closed range, applying the size filter, creating the initial
boundary, performing noise removal at the boundary, correcting boundary fluctuations,
and finally using the decision tree. Finally, the Buildings Footprint developed based on
the algorithm was compared with the Buildings Footprint developed manually to assess
the accuracy of the results.
In the last section, to achieve third goal, all process was written in Python computer
language and DB-creator program was created and in a fully automatic process, the DTM
and the Buildings Footprint were created and saved.
Based on the results, the RMSE value are ±0.62 meter for the urban and ±0.28 meter for
rural buildings footprints. Also, Kappa coefficient that is 0.95. Considering the technical
properties of LiDAR data used, the results could be considered completely accurate as
compared to the accuracy of the available data. Therefore, it can be concluded that,
although the DTM built in this study differs from the hand-built DTM in areas with
synthetic structures, field studies have shown that this DTM can provide more details on
synthetic environments and is more accurate due to the effects on the study areas of DTM
made in this research. |
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