Building Reconstruction

Se presenta un modelo de documento expedido por el contratante, haciendo constar que -despues de un lapso de 6 meses- no se han manifestado desperfectos ("vicios ocultos) en las obras o productos terminados y entregados por el contratado. 

En el caso a partir del cual se generó este modelo de constancia de no vicios ocultos, fue el de la rehabilitación de la estación que la Comisión de Areas Naturales Protegidas (CONANP) opera en la localidad de Xcalak, Municipio de Othón P. Blanco, estado de Quintana Roo, Estados Unidos Mexicanos.  El contratado fue la microempresa Vision EnerGtiK S. de R.L. MI.  Las obras fueron de mantenimiento, conservación y adecuación de la infraestructura a cargo de la Comisión Nacional de Áreas Naturales Protegidas (SEMARNAT) en la localidad de Xcalak, municipio de Othón P. Blanco, Quintana Roo, México.  Ello, conforme a precios unitarios y tiempo determinado especificados en el contrato CONANP/DRPY/AD-OP/16151003-073/2009.  El proyecto inició el 1 de marzo del 2009 y terminó el 14 de abril del 2009.  La constancia de no vicios ocultos se extendió luego de un año de concluído el proyecto. 

El modelo de constancia de no vicios ocultos, forma parte de la carpeta-antología que Visión EnerGtiK S. de R.L. MI  utiliza como material de apoyo al entrenamiento, sobre el trabajo, para la administración de proyectos en el campo de ahorro y uso eficiente de energía.

Laser scanning or LiDAR data are increasingly used in forestry applications but also e.g. in urban environments or for building reconstructions. Huge point clouds are usually converted to a grid or are pre-processed in specific software packages. In this paper we present a methodology to extract and delineate single trees from small footprint, high intensity laser scanning point data in

An automatic building detection technique using LIDAR data and multispectral imagery has been proposed. Two masks are obtained from the LIDAR data: a 'primary building mask' and a 'secondary building mask'. The primary building mask indicates the void areas where the laser does not reach below a certain height threshold. The secondary building mask indicates the filled areas, from where the laser reflects, above the same threshold. Line segments are extracted from around the void areas in the primary building mask. Line segments around trees are removed using the normalized difference vegetation index derived from the orthorectified multispectral images. The initial building positions are obtained based on the remaining line segments. The complete buildings are detected from their initial positions using the two masks and multispectral images in the YIQ colour system. It is experimentally shown that the proposed technique can successfully detect buildings, when assessed in terms of 15 indices including completeness, cor-rectness and quality.

Building detection in complex scenes is a non-trivial exercise due to building shape variability, irregular terrain, shadows, and occlusion by highly dense vegetation. In this research, we present a graph based algorithm, which combines multispectral imagery and airborne LiDAR information to completely delineate the building boundaries in urban and densely vegetated area. In the first phase, LiDAR data is divided into two groups: ground and non-ground data, using ground height from a bare-earth DEM. A mask, known as the primary building mask, is generated from the non-ground LiDAR points where the black region represents the elevated area (buildings and trees), while the white region describes the ground (earth). The second phase begins with the process of Connected Component Analysis (CCA) where the number of objects present in the test scene are identified followed by initial boundary detection and labelling. Additionally, a graph from the connected components is generated, where each black pixel corresponds to a node. An edge of a unit distance is defined between a black pixel and a neighbouring black pixel, if any. An edge does not exist from a black pixel to a neighbouring white pixel, if any. This phenomenon produces a disconnected components graph, where each component represents a prospective building or a dense vegetation (a contiguous block of black pixels from the primary mask). In the third phase, a clustering process clusters the segmented lines, extracted from multispectral imagery, around the graph components, if possible. In the fourth step, NDVI, image entropy, and LiDAR data are utilised to discriminate between vegetation, buildings, and isolated building's occluded parts. Finally, the initially extracted building boundary is extended pixel-wise using NDVI, entropy, and LiDAR data to completely delineate the building and to maximise the boundary reach towards building edges. The proposed technique is evaluated using two Australian data sets: Aitkenvale and Hervey Bay, for object-based and pixel-based completeness, correctness, and quality. The proposed technique detects buildings larger than 50 m 2 and 10 m 2 in the Aitkenvale site with 100% and 91% accuracy, respectively, while in the Hervey Bay site it performs better with 100% accuracy for buildings larger than 10 m 2 in area.

Automated methods for reliable 3-D reconstruction of man-made objects are essential to many users and providers of 3-D city data. Manual 3-D processing of aerial images is time consuming and requires the expertise of qualified personnel. Therefore, the necessity to interpret, classify and quantitatively process aerial images in a semi- or fully automatic mode and to integra te the results into CAD- or spatial information systems is more urgent than ever. This paper describes current research activities at ETH Zurich in automated 3-D reconstruction of buildings. Two conceptually different approaches to building reconstruction are presented: TOBAGO and ARUBA. The first approach is semi-automatic and aims at the automatic structuring of manually measured 3-D point clouds to generate CAD models of complete buildings. It is geared towards a reliable and flexible procedure which has a large potential to be useful for professional practice. The second approach aims at fully automatic detec...

Automated 3D building model generation continues to attract research interests in photogrammetry and computer vision. Airborne Light Detection and Ranging (LIDAR) data with increasing point density and accuracy has been recognized as a valuable source for automated 3D building reconstruction. While considerable achievements have been made in roof extraction, limited research has been carried out in modelling and reconstruction of walls, which constitute important components of a full building model. Low point density and irregular point distribution of LIDAR observations on vertical walls render this task complex. This paper develops a novel approach for wall reconstruction from airborne LIDAR data. The developed method commences with point cloud segmentation using a region growing approach. Seed points for planar segments are selected through principle component analysis,
and points in the neighbourhood are collected and examined to form planar segments. Afterwards, segment-based classification is performed to identify roofs, walls and planar ground surfaces. For walls with sparse LIDAR observations, a search is conducted in the neighbourhood of each individual roof segment to collect wall points, and the walls are then reconstructed using geometrical and topological constraints. Finally, walls which were not illuminated by the LIDAR sensor are determined via both reconstructed roof data and neighbouring walls. This leads to the generation of topologically consistent and geometrically accurate and complete 3D building models. Experiments have been conducted in two test sites in the Netherlands and Australia to evaluate the performance of the proposed method. Results show that planar segments can be reliably extracted in the two reported test sites, which have different point density, and the building walls can be correctly reconstructed if the walls are illuminated by the LIDAR sensor.

El recibo se refiere al monto monetario erogado por el Gobierno del Estado de Quintana Roo, por mejora en la Clínica No. 2 de primer nivel de atención, operada en la ciudad de Chetumal por su Secretaría de Salud.  El documento forma parte de la carpeta-antología de materiales de apoyo al entrenamiento, sobre el trabajo ("on the job training"),  para la administración de proyectos de mejora tecnológica en el campo de ahorro y uso eficiente de energías.  Se presenta desglose de conceptos de mejoras infraestructurales realizadas, con respectivos costos (Pesos mexicanos de 14 de marzo del 2007)