1,725 research outputs found
Integration of LIDAR and IFSAR for mapping
LiDAR and IfSAR data is now widely used for a number of applications, particularly those needing a digital elevation model. The data is often complementary to other data such as aerial imagery and high resolution satellite data. This paper will review the current data sources and the products and then look at the ways in which the data can be integrated for particular applications. The main
platforms for LiDAR are either helicopter or fixed wing aircraft, often operating at low altitudes, a digital camera is frequently included on the platform, there is an interest in using other sensors such as 3 line cameras of hyperspectral scanners. IfSAR is used from satellite platforms, or from aircraft, the latter are more compatible with LiDAR for integration. The paper will examine the advantages and disadvantages of LiDAR and IfSAR for DEM generation and discuss the issues which still need to be dealt with. Examples of applications will be given and particularly those involving the integration of different types of data. Examples will be given from various sources and future trends examined
Super-resolution-based snake model—an unsupervised method for large-scale building extraction using airborne LiDAR Data and optical image
Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%
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Spectral filtering as a method of visualising and removing striped artefacts in digital elevation data
Spectral filtering was compared with traditional mean spatial filters to assess their ability to identify and remove striped artefacts in digital elevation data. The techniques were applied to two datasets: a 100 m contour derived digital elevation model (DEM) of southern Norway and a 2 m LiDAR DSM of the Lake District, UK. Both datasets contained diagonal data artefacts that were found to propagate into subsequent terrain analysis. Spectral filtering used fast Fourier transformation (FFT) frequency data to identify these data artefacts in both datasets. These were removed from the data by applying a cut filter, prior to the inverse transform. Spectral filtering showed considerable advantages over mean spatial filters, when both the absolute and spatial distribution of elevation changes made were examined. Elevation changes from the spectral filtering were restricted to frequencies removed by the cut filter, were small in magnitude and consequently avoided any global smoothing. Spectral filtering was found to avoid the smoothing of kernel based data editing, and provided a more informative measure of data artefacts present in the FFT frequency domain. Artefacts were found to be heterogeneous through the surfaces, a result of their strong correlations with spatially autocorrelated variables: landcover and landsurface geometry. Spectral filtering performed better on the 100 m DEM, where signal and artefact were clearly distinguishable in the frequency data. Spectrally filtered digital elevation datasets were found to provide a superior and more precise representation of the landsurface and be a more appropriate dataset for any subsequent geomorphological applications
Airborne LiDAR for DEM generation: some critical issues
Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of
LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for
DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage
and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity
characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented
High-resolution DEM generated from LiDAR data for water resource management
Terrain patterns play an important role in determining the nature of water resources and related hydrological modelling. Digital Elevation Models (DEMs), offering an efficient way to represent ground surface, allow automated direct extraction of hydrological features (Garbrecht and Martz, 1999), thus bringing advantages in terms of processing efficiency, cost effectiveness, and accuracy
assessment, compared with traditional methods based on topographic maps, field surveys, or photographic interpretations. However, researchers have found that DEM quality and resolution affect the accuracy of any extracted hydrological features (Kenward et al., 2000). Therefore, DEM quality and resolution must be specified according to the nature and application of the hydrological features.
The most commonly used DEM in Victoria, Australia is Vicmap Elevation delivered by the Land Victoria, Department of Sustainability and Environment. It was produced by using elevation data mainly derived from existing contour map at a
scale of 1:25,000 and digital stereo capture, providing a state-wide terrain surface representation with a horizontal resolution of 20 metres. The claimed standard deviations, vertical and horizontal, are 5 metres and 10 metres respectively (Land- Victoria, 2002). In worst case, horizontal errors could be up to ±30m. Although high resolution stereo aerial photos provide a potential way to
generate high resolution DEMs, under the limitations
of currently used technologies by prevalent commercial photogrammetry software, only DSMs (Digital Surface Models) other than DEMs can be directly generated. Manual removal of the nonground data so that the DSM is transformed into a
DEM is time consuming. Therefore, using stereo aerial photos to produce DEM with currently available techniques is not an accurate and costeffective method.
Light Detection and Ranging (LiDAR) data covering 6900 km² of the Corangamite Catchment area of Victoria were collected over the period 19 July 2003 to 10 August 2003. It will be used to support a series of salinity and water management projects for the Corangamite Catchment Management Authority (CCMA). The DEM derived from the LiDAR data has a vertical accuracy of 0.5m and a horizontal
accuracy of 1.5m. The high quality DEM leads to derive much detailed terrain and hydrological attributes with high accuracy.
Available data sources of DEMs in a catchment management area were evaluated in this study, including the Vicmap DEM, a DEM generated from stereo aerial photos, and LiDAR-derived DEM.
LiDAR technology and LiDAR derived DEM were described. In order to assess the capability of LiDAR-derived DEM for improving the quality of extracted hydrological features, sub-catchment boundaries and drainage networks were generated from the Vicmap DEM and the LiDAR-derived
DEM. Results were compared and analysed in terms of accuracy and resolution of DEMs. Elevation differences between Vicmap and LiDAR-derived DEMs are significant, up to 65m in some areas. Subcatchment boundaries derived from these two DEMs are also quite different. In spite of using same resolution for the Vicmap DEM and the LiDARderived
DEM, high accuracy LiDAR-derived DEM gave a detailed delineation of sub-catchment.
Compared with results derived from LiDAR DEM, the drainage networks derived from Vicmap DEM do not give a detailed description, and even lead to discrepancies in some areas. It is demonstrated that a LiDAR-derived DEM with high accuracy and high resolution offers the capability of improving the quality of hydrological features extracted from DEMs
An Automated Process of Creating 3D City Model for Monitoring Urban Infrastructures
This paper describes the process of designing models and tools for an automated way of creating 3D city model based on a raw point cloud.Also, making and forming 3D models of buildings. Models and tools for creating tools made in the model builder application within the ArcGIS Pro software. An unclassified point cloud obtained by the LiDAR system was used for the model input data. The point cloud, collected by the airborne laser scanning system (ALS), is classified into several classes: ground, high and low noise, and buildings. Based on the created DEMs, points classified as buildings and formed prints of buildings, realistic 3D city models were created. Created 3D models of cities can be used as a basis for monitoring the infrastructure of settlements and other analyzes that are important for further development and architecture of cities
Scan4Façade: Automated As-Is Façade Modeling of Historic High-Rise Buildings Using Drones and AI
This paper presents an automated as-is façade modeling method for existing and historic high-rise buildings, named Scan4Façade. To begin with, a camera drone with a spiral path is employed to capture building exterior images, and photogrammetry is used to conduct three-dimensional (3D) reconstruction and create mesh models for the scanned building façades. High-resolution façade orthoimages are then generated from mesh models and pixelwise segmented by an artificial intelligence (AI) model named U-net. A combined data augmentation strategy, including random flipping, rotation, resizing, perspective transformation, and color adjustment, is proposed for model training with a limited number of labels. As a result, the U-net achieves an average pixel accuracy of 0.9696 and a mean intersection over union of 0.9063 in testing. Then, the developed twoStagesClustering algorithm, with a two-round shape clustering and a two-round coordinates clustering, is used to precisely extract façade elements’ dimensions and coordinates from façade orthoimages and pixelwise label. In testing with the Michigan Central Station (office tower), a historic high-rise building, the developed algorithm achieves an accuracy of 99.77% in window extraction. In addition, the extracted façade geometric information and element types are transformed into AutoCAD command and script files to create CAD drawings without manual interaction. Experimental results also show that the proposed Scan4Façade method can provide clear and accurate information to assist BIM feature creation in Revit. Future research recommendations are also stated in this paper
The Potential of Active Contour Models in Extracting Road Edges from Mobile Laser Scanning Data
Active contour models present a robust segmentation approach, which makes efficient use of specific information about objects in the input data rather than processing all of the data. They have been widely-used in many applications, including image segmentation, object boundary localisation, motion tracking, shape modelling, stereo matching and object reconstruction. In this paper, we investigate the potential of active contour models in extracting road edges from Mobile Laser Scanning (MLS) data. The categorisation of active contours based on their mathematical representation and implementation is discussed in detail. We discuss an integrated version in which active contour models are combined to overcome their limitations. We review various active contour-based methodologies, which have been developed to extract road features from LiDAR and digital imaging datasets. We present a case study in which an integrated version of active contour models is applied to extract road edges from MLS dataset. An accurate extraction of left and right edges from the tested road section validates the use of active contour models. The present study provides valuable insight into the potential of active contours for extracting roads from 3D LiDAR point cloud data
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