Complete classification of raw LIDAR data and 3D reconstruction of buildings

Forlani, G.; Nardinocchi, Carla; Scaioni, Marco; Zingaretti, Primo

doi:10.1007/s10044-005-0018-2

Cited by 124 publications

(80 citation statements)

References 9 publications

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“…In these circumstances, not only the attributes of an object are relevant but most importantly both its spatial location and its spatial interaction with other objects (Barr and Barnsley, 1997;Anders et al, 1999;Kim and Muller, 2002;Bauer and Steinnocher, 2001;Nardinocchi et al, 2003;Forlani et al, 2006).…”

Section: Aim and Objectivesmentioning

confidence: 99%

A graph-based algorithm to define urban topology from unstructured geospatial data

Almeida

Morley

Dowman

2013

International Journal of Geographical Information Science

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Interpretation and analysis of urban topology are particularly challenging tasks given the complex spatial pattern of the urban elements, and hence their automation is especially needed. In terms of the urban scene meaning, the starting point in this study is unstructured geospatial data, i.e. no prior knowledge of the geospatial entities is assumed. The aim of translating these data into more meaningful homogeneous regions can be achieved by detecting geographic features within the initial random collection of geospatial objects, and then by grouping them according to their spatial arrangement. The techniques applied to achieve this are those of graph theory applied to urban topology analysis within GIS environment. The role of graph theory in such a task and pre-processing of raw geospatial data were object of another previously published paper; this manuscript focuses primarily on the description of the procedure: "containmentfirst search" (CFS) -based on the well known breadth-first search algorithm for graph traversal, CFS analyzes and interprets the geospatial object arrangements in terms of the extension of the standard notion of the topological relation of adjacency to that of containment, what is defined within this research as "polygon-ring containment". LiDAR data were used as an example scenario for development purposes; in order to test the system proposed as a whole, further tests will entail the use of real world LiDAR datasets with different technical characteristics as well as photogrammetric datasets.

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Section: Aim and Objectivesmentioning

confidence: 99%

A graph-based algorithm to define urban topology from unstructured geospatial data

Almeida

Morley

Dowman

2013

International Journal of Geographical Information Science

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“…For the task of automatic building detection from ALS, or ALS and image data, a large number of different methods have been presented ( [22,[27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43] and many others). Among these methods, step-wise classification approaches exploiting many different input features are typical.…”

Section: Previous Studiesmentioning

confidence: 99%

Automatic Detection of Buildings and Changes in Buildings for Updating of Maps

et al. 2010

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Abstract:There is currently high interest in developing automated methods to assist the updating of map databases. This study presents methods for automatic detection of buildings and changes in buildings from airborne laser scanner and digital aerial image data and shows the potential usefulness of the methods with thorough experiments in a 5 km 2 suburban study area. 96% of buildings larger than 60 m 2 were correctly detected in the building detection. The completeness and correctness of the change detection for buildings larger than 60 m 2 were about 85% (including five classes). Most of the errors occurred in small or otherwise problematic buildings.

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“…A three-stage framework has been implemented for a complete, robust and automatic classification of LiDAR data and is composed by: a region-growing technique to obtain regions with a step edge along their border, a grouping of connected sets of pixels on the basis of an 8-classes partition of the height gradient orientation and a rule based scheme applied to the classification of the regions [8].…”

Section: Related Workmentioning

confidence: 99%

Semi-Automatic Objects Recognition in Urban Areas Based on Fuzzy Logic

Prandi¹,

Brumana²,

Fassi

2010

JGIS

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Three dimensional object extraction and recognition (OER) from geographic data has been definitely one of more important topic in photogrammetry for quite a long time. Today, the capability of rapid generating high-density DSM increases the supply of geographic information but the discrete nature of the measuring makes more difficult to recognize correctly and to extract 3D objects from these surface. The proposed methodology wants to semi-automate some geographic objects clustering operations, in order to perform the recognition process. The clustering is a subjective process; the same set of data items often needs to be partitioned differently based on the application. Fuzzy logic gives the possibility to use in a mathematical process the uncertain information typical of human reasoning. The concept at the base of our proposal is to use the information contained in Image Matching or LiDAR DSM, and typically understood by the human operator, in a fuzzy recognition process able to combine the different input in order to perform the classification. So the object recognition approach proposed in our workflow integrates 3D structural descriptive components of objects, extracted from DSM, into a fuzzy reasoning process in order to exploit more fully all available information, which can contribute to the extraction and recognition process and, to handling the object's vagueness. The recognition algorithm has been tested with to different data set and different objectives. An important issue is to apply the typical human process which allows to recognize objects in a range image in a fuzzy reasoning process. The investigations presented here have given a first demonstration of the capability of this approach.

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Complete classification of raw LIDAR data and 3D reconstruction of buildings

Cited by 124 publications

References 9 publications

A graph-based algorithm to define urban topology from unstructured geospatial data

A graph-based algorithm to define urban topology from unstructured geospatial data

Automatic Detection of Buildings and Changes in Buildings for Updating of Maps

Semi-Automatic Objects Recognition in Urban Areas Based on Fuzzy Logic

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