ORCID Profile
0000-0003-4973-9212
Current Organisation
Esri Australia
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Publisher: Copernicus GmbH
Date: 30-05-2018
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-35-2018
Abstract: Abstract. Accessible cities with accessible services are an old claim of people with reduced mobility. But this demand is still far away of becoming a reality as lot of work is required to be done yet. First step towards accessible cities is to know about real situation of the cities and its pavement infrastructure. Detailed maps or databases on street slopes, access to sidewalks, mobility in public parks and gardens, etc. are required. In this paper, we propose to use smartphone based photogrammetric point clouds, as a starting point to create accessible maps or databases. This paper analyses the performance of these point clouds and the complexity of the image acquisition procedure required to obtain them. The paper proves, through two test cases, that smartphone technology is an economical and feasible solution to get the required information, which is quite often seek by city planners to generate accessible maps. The proposed approach paves the way to generate, in a near term, accessibility maps through the use of point clouds derived from crowdsourced smartphone imagery.
Publisher: Wiley
Date: 22-02-2015
DOI: 10.1111/PHOR.12090
Publisher: Informa UK Limited
Date: 26-11-2019
Publisher: Copernicus GmbH
Date: 26-10-2016
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-W1-81-2016
Abstract: Abstract. Timely and accurate acquisition of information on the condition and structural changes of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting tree features include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraint, such as labour intensive field work, a lot of financial requirement, influences by weather condition and topographical covers which can be overcome by means of integrated airborne based LiDAR and very high resolution digital image datasets. This study presented a semi-automated approach for extracting urban trees from integrated airborne based LIDAR and multispectral digital image datasets over Istanbul city of Turkey. The above scheme includes detection and extraction of shadow free vegetation features based on spectral properties of digital images using shadow index and NDVI techniques and automated extraction of 3D information about vegetation features from the integrated processing of shadow free vegetation image and LiDAR point cloud datasets. The ability of the developed algorithms shows a promising result as an automated and cost effective approach to estimating and delineated 3D information of urban trees. The research also proved that integrated datasets is a suitable technology and a viable source of information for city managers to be used in urban trees management.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Publisher: Springer Science and Business Media LLC
Date: 25-09-2015
Publisher: Elsevier BV
Date: 11-2013
Publisher: Copernicus GmbH
Date: 10-2019
DOI: 10.5194/ISPRS-ARCHIVES-XLII-4-W16-303-2019
Abstract: Abstract. To acquire 3D geospatial information, LiDAR technology provides the rapid, continuous and cost-effective capability. In this paper, two automated approaches for extracting building features from the integrated aerial LiDAR point cloud and digital imaging datasets are proposed. The assumption of the two approaches is that the LiDAR data can be used to distinguish between high- and low-rise objects while the multispectral dataset can be used to filter out vegetation from the data. Object-based image analysis techniques are applied to the extracted building objects. The two automated buildings extraction approaches are tested on a fusion of aerial LiDAR point cloud and digital imaging datasets of Istanbul city. The object-based automated technique presents better results compared to the threshold-based technique for extraction of building objects in term of visual interpretation.
Publisher: MDPI AG
Date: 19-03-2019
DOI: 10.3390/SMARTCITIES2010008
Abstract: Spatial Data Infrastructures (SDIs) are frequently used to exchange 2D & 3D data, in areas such as city planning, disaster management, urban navigation and many more. City Geography Mark-up Language (CityGML), an Open Geospatial Consortium (OGC) standard has been developed for the storage and exchange of 3D city models. Due to its encoding in XML based format, the data transfer efficiency is reduced which leads to data storage issues. The use of CityGML for analysis purposes is limited due to its inefficiency in terms of file size and bandwidth consumption. This paper introduces XML based compression technique and elaborates how data efficiency can be achieved with the use of schema-aware encoder. We particularly present CityGML Schema Aware Compressor (CitySAC), which is a compression approach for CityGML data transaction within SDI framework. Our test results show that the encoding system produces smaller file size in comparison with existing state-of-the-art compression methods. The encoding process significantly reduces the file size up to 7–10% of the original data.
Publisher: Copernicus GmbH
Date: 29-09-2016
DOI: 10.5194/ISPRS-ARCHIVES-XLII-4-W1-127-2016
Abstract: Abstract. Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.
Publisher: Copernicus GmbH
Date: 31-05-2017
DOI: 10.5194/ISPRS-ARCHIVES-XLII-1-W1-91-2017
Abstract: Abstract. Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.
Publisher: Elsevier BV
Date: 10-2014
Publisher: Copernicus GmbH
Date: 19-10-2015
DOI: 10.5194/ISPRSANNALS-II-2-W2-215-2015
Abstract: Abstract. The applicability of Mobile Laser Scanning (MLS) systems continue to prove their worth in route corridor mapping due to the rapid, continuous and cost effective 3D data acquisition capability. LiDAR data provides a number of attributes which can be useful for extracting various road features. Road edge is a fundamental feature and its accurate knowledge increases the reliability and precision of extracting other road features. We developed an automated algorithm for extracting left and right edges from MLS data. The algorithm involved several input parameters which are required to be analysed in order to find their optimal values. In this paper, we present a detailed analysis of the dimension parameters of input data and raster cell in our algorithm. These parameters were analysed based on temporal, completeness and accuracy performance of our algorithm for their different sets of values. This analysis provided the estimation of an optimal values of parameters which were used to automate the process of extracting road edges from MLS data.
Publisher: MDPI AG
Date: 22-07-2017
Publisher: Springer International Publishing
Date: 2017
Location: Netherlands
No related grants have been discovered for Pankaj Kumar.