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DEVELOPING METHODOLOGY TO MAP TREE CANOPY IN URBAN AREAS FROM LOW COST COMMERCIAL UAVS

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Urban Green Spaces are essential for the well-being of the urban society. Mapping and monitoring of tree canopy in urban landscape is an integral part of the Urban Planning. Recent popularity and improvement of UAV technology enables planners to assess available urban green spaces in high resolution with greater accuracy. One of most famous UAV series is Phantom drone series. In this study, we have used Phantom 3 Drone (Professional Edition) which has 4K camera, approximately 20minutes flight time and range of maximum 5 km. But, most of the available commercial Drones lacks Near Infrared (NIR) observation which is crucial to map and monitor vegetation. In this paper, we are exploring an efficient methodology which is based on textural information from RGB image and elevation information derived from a Drone, to extract tree canopies in Urban Area. A university environment which consists of a desirable amount of tree canopy and buildings have been mapped to demonstrate this methodology in this paper. Finally, results were compared to digitized tree canopy layer to validate the results.
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DEVELOPING METHODOLOGY TO MAP TREE CANOPY IN URBAN AREAS
FROM LOW COST COMMERCIAL UAVS
Niluka Munasinghe1, U.I. Manatunga1, H.M.R. Premasiri1, N. Lakmal Deshapriya2, S.L. Madawalagama2 and Lal
Samarakoon2
1Department of Earth Resources Engineering, University of Moratuwa, Sri Lanka
Email: mniluka@gmail.com,hmranjith@yahoo.com
2Geoinformatics Center, Asian Institute of Technology, Thailand
Email: lakmalnd@yahoo.com,madawalagama@gmail.com,lalsamarakoon@gmail.com
KEYWORDS: UAV, Urban Planning, Tree Canopy, DSM, Orthoimage
ABSTRACT:
Urban Green Spaces are essential for the well-being of the urban society. Mapping and monitoring of tree canopy in
urban landscape is an integral part of the Urban Planning. Recent popularity and improvement of UAV technology
enables planners to assess available urban green spaces in high resolution with greater accuracy. One of most famous
UAV series is Phantom drone series. In this study, we have used Phantom 3 Drone (Professional Edition) which has
4K camera, approximately 20minutes flight time and range of maximum 5 km. But, most of the available commercial
Drones lacks Near Infrared (NIR) observation which is crucial to map and monitor vegetation. In this paper, we are
exploring an efficient methodology which is based on textural information from RGB image and elevation
information derived from a Drone, to extract tree canopies in Urban Area. A university environment which consists
of a desirable amount of tree canopy and buildings have been mapped to demonstrate this methodology in this paper.
Finally, results were compared to digitized tree canopy layer to validate the results.
1. INTRODUCTION
Urban planning means dealing with the constant change as communities, cities, and towns which continue to
evolve. In response, professional planning, aside from requiring expertise, understanding, and political savvy,
requires the correct technology to collect topographical information/data to facilitate effective translation of
strategic visions into strategic action plans. However, collecting data is a challenging aspect of urban planning,
because, effective urban planning requires large amounts of accurate data, which is difficult to collect since most
often, locations, prove difficult to access. Further, most of the traditional technology for aerial surveys is highly
expensive. An ideal methodology that is cost effective, highly accurate, and able to navigate difficult locations is
required. One such methodology is UAV photogrammetry.
UAV technology has revolutionized the aerial photogrammetric mapping, and its applications are speedily
increasing. When compared with present day conventional methods of photogrammetry, UAV photogrammetry
has improved and facilitated the creation of an advanced tool for photographic measurement. The major
components of a UAV include an unmanned aircraft, a transmitter, a communication link, an image sensor such
as a digital or infrared camera and mission planning. UAV photogrammetry can also be integrated with a LiDAR
system. UAV photogrammetry facilitates both autonomous and semi-autonomous remote controlling and ensures
the accuracy of an approximate 1cm to 2cm. Further, it creates new opportunities for application in close range
domains by combining terrestrial and aerial photogrammetry, but its major advantage is that it is highly cost
effective, as it enhances problem-solving applications and offers an alternative to the traditionally manned and
high-cost aerial photogrammetry. UAV photogrammetry enables application in both small scale and large scale,
with varying or similar system costs, depending to an extent on the intricateness of the system. Nevertheless, this
study utilizes a UAV system approach and its cost effective applications to develop a methodology that facilitates
mapping tree canopies in urban areas.
Digital images can be directly captured by use of a digital camera, or they can be scanned from aerial photographs.
Being in digital form facilitates the easy storage and management of the photographic information as digital maps,
digital orthoimage and DEM in a computer. This method of using a computer to process digital images and aerial
photography is called the digital photogrammetric method.
A consumer grade UAV system was used to acquire the aerial photographs required for the study. Specifically,
the Professional Edition, DJI Phantom 3 with a Sony EXMOR camera was utilized as the system for data
acquisition of aerial photographs of the area of study.
2. STUDY AREA AND DATA USED
2.1 Study Area
The University environment (The Asian Institute of Technology, Thailand) was selected as the study area.
Because, it has land-cover type consists of combination of buildings and trees, which can be, considers as eco-
friendly urban setting.
Figure 1: Study Area (c WorldView-2 image 2012 DigitalGlobe)
2.2 Data from UAV System
Phantom 3 professional drone is selected as the platform to collect aerial images of the study area which are
processed to make the orthoimage and DSM. Phantom series drones by DJI are one of the most common civilian
drones among the community mainly used for photography and as hobby. In recent studies, accurate mapping
capacity of phantom 3 drone is exploited and it is used in this task to acquire aerial images. The drone is chosen
considering the affordability and mobility. According to DJI, the Phantom 3 professional weights 1280g and
falls to the micro UAV category. It is listed with a flight time of 23 minutes with a fully charged lithium polymer
battery. The included remote control unit operates on a 2.4GHz ISM frequency and has the ability to
communicate with the Phantom 3 up to 5 km range from the remote control. The 12 megapixel Sony EXMOR
camera is factory built and mounted in 3 axis stabilization gimbal which provides clear stabilized images.
Flight planning was done with a software named Map Pilot for DJI, which is used to execute full autonomous
flight. The advantage of modern flight planning software is requirement of minimal interference for operators
which allows to focus on field operation rather than calculation. The app automatically computes the exposure
location and flight paths according to the given flying altitude and image overlap. Flying altitude is set to 100m
above ground level which provides 4.2cm per pixel average resolution in unprocessed photographs. Both side
overlap and forward overlap were set to 80% to provide more key points as possible for accurate
photogrammetric processing. Total number of 1670 near vertical photos were collected by 7 successive flights
which were processed together to obtain DSM and orthoimage.
3. METHODOLOGY
3.1 DSM & Orthoimage Generation
The DSM & orthoimage generation was carried out using Agisoft PhotoScan software. The below diagram shows
the basic processing steps of the software.
Figure 2: Processing steps of Agisoft PhotoScan
The first step is the feature matching across photos. Here the PhotoScan software identifies, in the source
photos, the stable points under variations of viewpoint and lighting. After identifying, the software produces
descriptors for the stable points based on their local neighborhood. The produced descriptors are then utilized to
discover any correlation, communication or consistency among the photos. Aspects of this process are similar to
the popular SIFT approach, but unlike the SIFT approach, this software employs different algorithms to acquire
an alignment of slightly higher quality. Subsequently, the PhotoScan software applies a greedy algorithm to
approximately locate the camera positions and then with the use of a bundle adjustment algorithm, proceeds to
refine them. A variety of algorithms are at hand at the following dense surface reconstruction phase. Here, the
methods dependent on the pair-wise depth map computation are the Exact, Height field and Smooth methods
while the one dependent on the multi-view approach is the Fast method. At the next phase, the software
develops a texture atlas by creating parameters of the surface possibly by cutting it (the surface) into smaller
pieces and blending source photos. Resulted DSM and orthoimage shown below.
Figure 2: Left: DSM; Right: Orthoimage
Feature matching
across the photos
Solving for
camera intrinsic
and extrinsic
orientation
parameters
Dense surface
reconstruction Texture Mapping
3.2 Tree Canopy Mapping
A decision tree was developed to map the tree canopy
(See Figure 2). Tree canopy mapping proves to be a
complex task without near infrared band. So we used the
height information and object parameters to map the
canopy. This task was carried out using eCognition
software package.
The most elementary step while using eCognition for
image analysis is the segmentation or separation of a
scene depicting an image turn, into image objects.
Therefore, segmentation at the initial stage involves the
separation of an image into isolated regions that are
represented by simple unclassified image objects, known
as ‘image object primitives’’. To get the most realistic
image objects, the necessary elevation information must
be represented, and it should not be too big or too small
for the analysis task.
The first step involved is creating image objects. To do
this, the multiresolution segmentation algorithm was
utilized. This is because it organizes the image into
objects, regions with similar pixel values. Therefore, the
areas that are homogenous produce objects that are
larger, and areas that are heterogeneous produce smaller
objects. How homogeneous/heterogeneous the objects
are allowed to get is operated by the ‘scale parameter’.
An assumption, that the buildings and trees were always
elevated was made. Further, the study area had not
experienced much change in terrain elevation, so simply
the applying elevation threshold is good enough to
classify elevated objects. In the second processing, step
trees had to be separated from buildings, as both were
elevated objects. Here again the DSM information was
used. It is apparent that Elevation (DSM) texture of the
buildings is far smoother than trees, which can be
represented by object’s standard deviation (Trees - high
Elevation Standard Deviation and Building - law
Elevation Standard Deviation). So, the thresholding on
Standard Deviation was used to separate Trees and Buildings, which are both elevated objects.
Therefore, the standard deviation of the DSM was applied to separate the trees and the buildings. Nonetheless,
some trees were still classified as ‘Buildings’ even after refining the classification using the standard deviation
of the DSM layer. It was obvious that the green layer contained significant information about vegetation. So
well know Green Ratio “green/(red+green+blue)”, which measures green component of the color was used for
further refinements. Some areas of the buildings had not thus far been classified or were de-classified again
because they accomplished one of the conditions before. Some of the not classified objects were highly
surrounded by ‘Buildings’ objects. If an unclassified object had a high common border to ‘Buildings’ objects it
ought to belong to the class ‘Buildings’. The neighborhood relationships of objects were then used to refine the
classification. There were still some misclassified objects, but they were very small. The compactness of the
‘object of interest’ was the separating condition here, based on the assumption, that the buildings have a regular
shape. Refining the classification depended on that characteristic.
4. RESULTS
Results showing the separation of Tree Canopy from the building is shown in Figure 4. Additionally, these results
were compared with respect to manual digitized results of the study area, which is shown in Table 1. 64% of Total
Accuracy was obtained through this methodology using consumer-grade UAV.
Figure 3: Decision Tree
Figure 4: Resulted tree canopy map
Table 1: Accuracy Assessment of the Classification
5. CONCLUSION AND DISCUSSION
Total Accuracy with respect to manual digitizing and visual inspection of the results; validate the approach that
we have used in this paper. Even without Near Infrared band, which is not available in consumer-grade UAV’s,
this approach successfully maps the Vegetation Canopy. It is prominent that this accuracy was good enough for
urban planners to assess accessibility to green spaced of urban population for sustainable planning of the urban
environment.
Unavailability of freely available Software for Object Oriented Classification, high cost of UAV’s and
legal barriers associated with UAV are the main obstacles to this approach. With the current advancement of
technology and the popularity of UAVs will help to overcome these obstacles in the future.
6. REFERENCES
Uzar, Melis and Naci Yastikli. "Automatic Building Extraction Using Lidar and Aerial Photographs". Bol. Ciênc.
Geod. 19.2 (2013): 153-171. Web.
Attarzadeh, R. and Momeni, M. (2012) ‘Object-based building extraction from high resolution satellite imagery’,
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,
XXXIX-B4, pp. 5760. doi: 10.5194/isprsarchives-xxxix-b4-57-2012.
Baatz, M., Benz, U., Dehghani, S. and Heynen, M. (2004) eCognition User Guide 4 , Definiens Imagine GmbH,
Munchen, Germany,pp.
Baatz, M. and Schape, A. (2000) Multiresolution Segmentation: an optimization approach for high quality
multi-scale image segmentation, Proceedings of Angewandte Geogr. Informationsverarbeitung XII , Strobl, J.
and Blaschke, T. eds., Wichmann, Heidelberg, pp. 12-23.
Algorithms used in Photoscan (2015) Retrieved 17 September 2015, Available at
http://www.agisoft.com/forum/index.php?topic=89.0
Hofmann, A.D., Mass, H., Streilein, A., 2002. Knowledge-based building detection based on laser scanner data
and topographic map information, IAPRS, Vol. 34, Part 3A+B, pp.163-169.
Trimble Navigation Limited, 2010. Getting started Example: Simple building extraction. In eCognition 8.0:
Guided Tour Level 1. United States, pp. 32-55.
Manual Digitizing
Tree
Non-Tree
Results
Tree
18 %
4 %
Non-Tree
32 %
46 %
Total Accuracy: 64%
Lee, D.S., Shan J., Bethel, J.S., 2003. Class-guided building extraction from IKONOS imagery.
Photogrammetric Engineering and Remote Sensing 69 (2), 143-150.
Jiang, N., Zhang, J.X., 2008. Object-oriented building extraction by DSM and very high-resolution orthoimages.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol
XXXVII. Part B3b.
Song, W. and Haithcoat, T., 2005. Development of comprehensive accuracy assessment indexes for building
footprint extraction. IEEE Transactions on Geoscience and Remote Sensing 43(2), pp. 402404.
Wei, Y.; Zhao, Z.; Song, J. Urban Building Extraction from High-Resolution Satellite
Panchromatic Image Using Clustering and Edge Detection. In Proceedings of IEEE International Geoscience
and Remote Sensing Symposium, Honolulu, HI, USA, 2024 September 2004;Volume 7, pp.2008-2010.
Wei, Y.; Zhao, Z.; Song, J. Urban Building Extraction from High-Resolution Satellite Panchromatic Image Using
Clustering and Edge Detection. In Proceedings of IEEE International Geoscience and Remote Sensing
Symposium, Honolulu, HI, USA, 2024 September 2004; Volume 7, pp. 2008-2010.
Agisoft, L. (2013). Agisoft PhotoScan user manual. Professional edition, version 0.9. 0. AgiSoft LLC (Pub),
Calgary, CA.
... In order to plan, maintain and manage these dynamic greens and take informed decision with regard to their provisions, it is important to have efficient technologies and methods to gather topographical information or data. The data would allow planner understand these dynamic landscapes in a comprehensive manner and allow effective translation of strategic visions into strategic action plans (Niluka et al. 2016). The mapped data set is well established in developed countries, while developing countries lack such data set (Sreetheran and Adnan 2007). ...
... This new technology has revolutionized the aerial photogrammetric mapping, and its use in various research fields is increasing. As compared to conventional methods, UAV photogrammetry acts as an advanced tool for photographic measurement, where the major components are unmanned aircraft, a transmitter, a communication link, an image sensor like a digital or infrared camera and mission plan (Niluka et al. 2016). UAVs integration with a Light Detection and Ranging (LiDAR) also has wide range of applications (Sofonia et al. 2019). ...
... In urban areas, the study by Quanlong et al. used UAV to produce land cover map to help planners and decision makers understand the UGS of cities, however, such studies are still countable and mainly focused on forest and natural vegetation (2015). Another study highlights that low cost commercial UAVs are effective and accurate to capture tree canopy in urban areas and support planning decisions with regard to access to UGS (Niluka et al. 2016). In the field of infrastructure management, a recent study by Saad et al. demonstrates UAVs wide application in mapping ruts and potholes on road surface, thereby helping in monitoring road condition (Saad and Tahar 2019). ...
Chapter
Farming in developing countries is majorly dependent on the traditional knowledge of farmers, with unscientific agricultural practices commonly implemented, leading to low productivity and degradation of resources. Moreover, mechanization has not been integral to farming, and thus managing a farm is a timeconsuming and labor-intensive process. Consequently, precision agriculture (PA) offers great opportunities for improvement. Using geographic information and communication technology (Geo-ICTs) principles, PA offers the opportunity for a farmer to apply the right amount of treatment at the right time and at the right location in the farm. However, in order to collect timely high-resolution data, dronebased sensing and image interpretation is required. These high-resolution images can give detailed information about the soil and crop condition, which can be used for farm management purposes. Leaf area index, normalized difference vegetation index, photochemical reflectance index, crop water stress index, and other such vegetation indices can provide important information on crop health. Temporal changes in these indices can give vital information about changes in health and canopy structure of the crop over time, which can be related to its biophysical and biochemical stress. These stresses may have occurred due to insufficient soil nutrient, inappropriate soil moisture, or pest attack. Through UAV-based PA, stressed areas can be identified in real time, and some corrective measures can also be carried out (e.g., fertilizer and pesticide spraying). Moreover, the advantages and different approaches to integrate the UAV data in the crop models are also described.
... In order to plan, maintain and manage these dynamic greens and take informed decision with regard to their provisions, it is important to have efficient technologies and methods to gather topographical information or data. The data would allow planner understand these dynamic landscapes in a comprehensive manner and allow effective translation of strategic visions into strategic action plans (Niluka et al. 2016). The mapped data set is well established in developed countries, while developing countries lack such data set (Sreetheran and Adnan 2007). ...
... This new technology has revolutionized the aerial photogrammetric mapping, and its use in various research fields is increasing. As compared to conventional methods, UAV photogrammetry acts as an advanced tool for photographic measurement, where the major components are unmanned aircraft, a transmitter, a communication link, an image sensor like a digital or infrared camera and mission plan (Niluka et al. 2016). UAVs integration with a Light Detection and Ranging (LiDAR) also has wide range of applications (Sofonia et al. 2019). ...
... In urban areas, the study by Quanlong et al. used UAV to produce land cover map to help planners and decision makers understand the UGS of cities, however, such studies are still countable and mainly focused on forest and natural vegetation (2015). Another study highlights that low cost commercial UAVs are effective and accurate to capture tree canopy in urban areas and support planning decisions with regard to access to UGS (Niluka et al. 2016). In the field of infrastructure management, a recent study by Saad et al. demonstrates UAVs wide application in mapping ruts and potholes on road surface, thereby helping in monitoring road condition (Saad and Tahar 2019). ...
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This book showcases how new and emerging technologies like Unmanned Aerial Vehicles (UAVs) are trying to provide solutions to unresolved socio-economic and environmental problems. Unmanned vehicles can be classified into five different types according to their operation. These five types are unmanned ground vehicles, unmanned aerial vehicles, unmanned surface vehicles (operating on the surface of the water), unmanned underwater vehicles, and unmanned spacecraft. Unmanned vehicles can be guided remotely or function as autonomous vehicles. The technology has a wide range of uses including agriculture, industry, transport, communication, surveillance and environment applications. UAVs are widely used in precision agriculture; from monitoring the crops to crop damage assessment. This book explains the different methods in which they are used, providing step-by-step image processing and sample data. It also discusses how smart UAVs will provide unique opportunities for manufacturers to utilise new technological trends to overcome the current challenges of UAV applications. The book will be of great interest to researchers engaged in forest carbon measurement, road patrolling, plantation monitoring, crop yield estimation, crop damage assessment, terrain modelling, fertilizer control, and pest control.
... In order to plan, maintain and manage these dynamic greens and take informed decision with regard to their provisions, it is important to have efficient technologies and methods to gather topographical information or data. The data would allow planner understand these dynamic landscapes in a comprehensive manner and allow effective translation of strategic visions into strategic action plans (Niluka et al. 2016). The mapped data set is well established in developed countries, while developing countries lack such data set (Sreetheran and Adnan 2007). ...
... This new technology has revolutionized the aerial photogrammetric mapping, and its use in various research fields is increasing. As compared to conventional methods, UAV photogrammetry acts as an advanced tool for photographic measurement, where the major components are unmanned aircraft, a transmitter, a communication link, an image sensor like a digital or infrared camera and mission plan (Niluka et al. 2016). UAVs integration with a Light Detection and Ranging (LiDAR) also has wide range of applications (Sofonia et al. 2019). ...
... In urban areas, the study by Quanlong et al. used UAV to produce land cover map to help planners and decision makers understand the UGS of cities, however, such studies are still countable and mainly focused on forest and natural vegetation (2015). Another study highlights that low cost commercial UAVs are effective and accurate to capture tree canopy in urban areas and support planning decisions with regard to access to UGS (Niluka et al. 2016). In the field of infrastructure management, a recent study by Saad et al. demonstrates UAVs wide application in mapping ruts and potholes on road surface, thereby helping in monitoring road condition (Saad and Tahar 2019). ...
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... In order to plan, maintain and manage these dynamic greens and take informed decision with regard to their provisions, it is important to have efficient technologies and methods to gather topographical information or data. The data would allow planner understand these dynamic landscapes in a comprehensive manner and allow effective translation of strategic visions into strategic action plans (Niluka et al. 2016). The mapped data set is well established in developed countries, while developing countries lack such data set (Sreetheran and Adnan 2007). ...
... This new technology has revolutionized the aerial photogrammetric mapping, and its use in various research fields is increasing. As compared to conventional methods, UAV photogrammetry acts as an advanced tool for photographic measurement, where the major components are unmanned aircraft, a transmitter, a communication link, an image sensor like a digital or infrared camera and mission plan (Niluka et al. 2016). UAVs integration with a Light Detection and Ranging (LiDAR) also has wide range of applications (Sofonia et al. 2019). ...
... In urban areas, the study by Quanlong et al. used UAV to produce land cover map to help planners and decision makers understand the UGS of cities, however, such studies are still countable and mainly focused on forest and natural vegetation (2015). Another study highlights that low cost commercial UAVs are effective and accurate to capture tree canopy in urban areas and support planning decisions with regard to access to UGS (Niluka et al. 2016). In the field of infrastructure management, a recent study by Saad et al. demonstrates UAVs wide application in mapping ruts and potholes on road surface, thereby helping in monitoring road condition (Saad and Tahar 2019). ...
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Farming in developing countries is majorly dependent on the traditional knowledge of farmers, with unscientific agricultural practices commonly implemented, leading to low productivity and degradation of resources. Moreover, mechanization has not been integral to farming, and thus managing a farm is a time-consuming and labor-intensive process. Consequently, precision agriculture (PA) offers great opportunities for improvement. Using geographic information and communication technology (Geo-ICTs) principles, PA offers the opportunity for a farmer to apply the right amount of treatment at the right time and at the right location in the farm. However, in order to collect timely high-resolution data, drone-based sensing and image interpretation is required. These high-resolution images can give detailed information about the soil and crop condition, which can be used for farm management purposes. Leaf area index, normalized difference vegetation index, photochemical reflectance index, crop water stress index, and other such vegetation indices can provide important information on crop health. Temporal changes in these indices can give vital information about changes in health and canopy structure of the crop over time, which can be related to its biophysical and biochemical stress. These stresses may have occurred due to insufficient soil nutrient, inappropriate soil moisture, or pest attack. Through UAV-based PA, stressed areas can be identified in real time, and some corrective measures can also be carried out (e.g., fertilizer and pesticide spraying). Moreover, the advantages and different approaches to integrate the UAV data in the crop models are also described.
... The area of interest, flying altitude, overlap, maximum speed, and etc. were given manually to the application. The exposure location and flight paths were automatically calculated according to the given flying altitude and image overlap [5]. ...
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Laser scanner data is being increasingly used to obtain topographical and object height information for mapping and GIS tasks. Valuable information can be derived of the terrain and objects of a region. Different methods have been published to segment laser scanner data in order to extract information. This paper aims to discuss the extraction of buildings to transfer 2D building data into 3D building data of a higher accuracy. For this study, only laser scanner data and scanned topographical maps are to be used. The study can be divided into two parts: segmentation and detection. Firstly, the region-based segmentation method is used to delimit objects. Then a scanned topographic map, filtered and converted into a vector format, is utilised to locate already recorded houses. Buildings not contained in the map could be found by combination of segment attributes. The study shows promising results – more than 95% of the buildings were detected. KURZFASSUNG: Laserscannerdaten werden im zunehmenden Maße zur Informationsgewinnung von topographischen und Objekthöhen für Karten und Geoinformationssysteme genutzt. Sie bieten wertvolle Informationen zu Gelände und Objekten. Jedoch ist es immer noch schwierig und es benötigt weiterer Daten wie digitale Katasterinformationen oder Grundpläne, um passende Informationen aus den Laserscannerdaten zu extrahieren. Dieser Artikel behandelt das Thema Gebäudeextraktion aus Laserscannerdaten mit dem Ziel der Datenaufwertung topographischer Karten. Als Hilfsmittel steht nur eine gescannte topographische Pixelkarte zur Verfügung. Die Analyse besteht hauptsächlich aus zwei Arbeitsschritten: der Segmentierung und der Detektion. Zuerst wird eine nachbarschaftsbasierte Segmentierungssoftware angewendet, um Objekte zu begrenzen. Anschließend wird eine modifizierte topographische Pixelkarte als Vektordatei zum Detektieren bereits aufgenommener Gebäude verwendet. Noch nicht registrierte Gebäude sollen danach mit einer Kombination verschiedenster Segmentattribute identifiziert werden. Dieses Prinzip der Informationsextraktion verspricht gute Erfolge – mehr als 95% der Gebäude der Testgebiete wurden erkannt.
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Recent high-resolution satellite images provide a valuable new data source for geospatial information acquisition. This paper addresses building extraction from Ikonos images in urban areas. The proposed approach uses the classification results of Ikonos multispectral images to provide approximate location and shape for candidate building objects. Their fine extraction is then carried out in the corresponding panchromatic image through segmentation and squaring. The ECHO classifier is used for supervised classification while the ISODATA algorithm is used for unsupervised classification and subsequent image segmentation. The classification performance is evaluated using the classification confusion matrix, while the final building extraction results are assessed based on the manually delineated results. A building squaring approach based on the Hough transformation is developed that detects and forms the rectilinear building boundaries. A number of sample results are presented to illustrate the approach and demonstrate its efficiency. It is shown that about 64.4 percent of the buildings can be detected, extracted, and accurately formed through this process. Remaining difficulties are high percentage false alarm errors caused by the misclassification of road and building classes as well as occlusion and shadows that may mislead the extraction process.
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For decades, large-scale aerial photos have been employed to extract building for mapping application. With the successively launching of high-resolution commercial satellites (e.g. IKONOS and QuickBird), high-resolution satellite imagery has been shown to be a cost-effective alternative to aerial photography in many applications. Drawing on the traditional building extraction approach, this paper proposes an algorithm to extract urban building from high-resolution panchromatic QuickBird image using clustering and edge detection. In the first step, an unsupervised clustering by histogram peak selection is used to split the image into a number of classes. The shadows of building are extracted from the lowest gray class. In the second step, the shadows are used as one of the evidences to verify the presence of buildings. Thus, the candidate building objects are extracted from the clustering classes except for the shadow class. Finally, to refine building boundary and further exclude some false building objects, the Canny operator is applied to detect edge of the candidate building objects in the PAN image. From the Hough transform of the detected edges, the main lines, which compose the polyhedral description of the building, can be found. The building extraction results are compared with manually delineated results. The comparison illustrates the efficiency of the proposed algorithm
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Presents a suite of indexes for comprehensively evaluating the results of automated building extraction. The indexes described include detection rate, correctness, matched overlay, area omission error, area commission error, root mean square error, corner difference, area difference, perimeter difference, and shape similarity. These proposed unbiased quality measures should enable the accuracy assessment of the building extraction process to address extraction issues such as completeness, geometric accuracy, and building shape similarity.
Object-based building extraction from high resolution satellite imagery', ISPRS -International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • R Attarzadeh
  • M Momeni
Attarzadeh, R. and Momeni, M. (2012) 'Object-based building extraction from high resolution satellite imagery', ISPRS -International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B4, pp. 57-60. doi: 10.5194/isprsarchives-xxxix-b4-57-2012.
  • M Baatz
  • U Benz
  • S Dehghani
  • M Heynen
Baatz, M., Benz, U., Dehghani, S. and Heynen, M. (2004) eCognition User Guide 4, Definiens Imagine GmbH, Munchen, Germany,pp.