A methodology to produce geographical information for land planning using very-high resolution images

Actualmente, os municípios são obrigados a produzir, no âmbito da elaboração dos instrumentos de gestão territorial, cartografia homologada pela autoridade nacional. O Plano Director Municipal (PDM) tem um período de vigência de 10 anos. Porém, no que diz respeito à cartografia para estes planos, principalmente em municípios onde a pressão urbanística é elevada, esta periodicidade não é compatível com a dinâmica de alteração de uso do solo. Emerge assim, a necessidade de um processo de produção mais eficaz, que permita a obtenção de uma nova cartografia de base e temática mais frequentemente. Em Portugal recorre-se à fotografia aérea como informação de base para a produção de cartografia de grande escala. Por um lado, embora este suporte de informação resulte em mapas bastante rigorosos e detalhados, a sua produção têm custos muito elevados e consomem muito tempo. As imagens de satélite de muito alta-resolução espacial podem constituir uma alternativa, mas sem substituir as fotografias aéreas na produção de cartografia temática, a grande escala. O tema da tese trata assim da satisfação das necessidades municipais em informação geográfica actualizada. Para melhor conhecer o valor e utilidade desta informação, realizou-se um inquérito aos municípios Portugueses. Este passo foi essencial para avaliar a pertinência e a utilidade da introdução de imagens de satélite de muito alta-resolução espacial na cadeia de procedimentos de actualização de alguns temas, quer na cartografia de base quer na cartografia temática. A abordagem proposta para solução do problema identificado baseia-se no uso de imagens de satélite e outros dados digitais em ambiente de Sistemas de Informação Geográfica. A experimentação teve como objectivo a extracção automática de elementos de interesse municipal a partir de imagens de muito alta-resolução espacial (fotografias aéreas ortorectificadas, imagem QuickBird, e imagem IKONOS), bem como de dados altimétricos (dados LiDAR). Avaliaram-se as potencialidades da informação geográfica extraídas das imagens para fins cartográficos e analíticos. Desenvolveram-se quatro casos de estudo que reflectem diferentes usos para os dados geográficos a nível municipal, e que traduzem aplicações com exigências diferentes. No primeiro caso de estudo, propõe-se uma metodologia para actualização periódica de cartografia a grande escala, que faz uso de fotografias aéreas vi ortorectificadas na área da Alta de Lisboa. Esta é uma aplicação quantitativa onde as qualidades posicionais e geométricas dos elementos extraídos são mais exigentes. No segundo caso de estudo, criou-se um sistema de alarme para áreas potencialmente alteradas, com recurso a uma imagem QuickBird e dados LiDAR, no Bairro da Madre de Deus, com objectivo de auxiliar a actualização de cartografia de grande escala. No terceiro caso de estudo avaliou-se o potencial solar de topos de edifícios nas Avenidas Novas, com recurso a dados LiDAR. No quarto caso de estudo, propõe-se uma série de indicadores municipais de monitorização territorial, obtidos pelo processamento de uma imagem IKONOS que cobre toda a área do concelho de Lisboa. Esta é uma aplicação com fins analíticos onde a qualidade temática da extracção é mais relevante.Currently, the Portuguese municipalities are required to produce homologated cartography, under the Territorial Management Instruments framework. The Municipal Master Plan (PDM) has to be revised every 10 years, as well as the topographic and thematic maps that describe the municipal territory. However, this period is inadequate for representing counties where urban pressure is high, and where the changes in the land use are very dynamic. Consequently, emerges the need for a more efficient mapping process, allowing obtaining recent geographic information more often. Several countries, including Portugal, continue to use aerial photography for large-scale mapping. Although this data enables highly accurate maps, its acquisition and visual interpretation are very costly and time consuming. Very-High Resolution (VHR) satellite imagery can be an alternative data source, without replacing the aerial images, for producing large-scale thematic cartography. The focus of the thesis is the demand for updated geographic information in the land planning process. To better understand the value and usefulness of this information, a survey of all Portuguese municipalities was carried out. This step was essential for assessing the relevance and usefulness of the introduction of VHR satellite imagery in the chain of procedures for updating land information. The proposed methodology is based on the use of VHR satellite imagery, and other digital data, in a Geographic Information Systems (GIS) environment. Different algorithms for feature extraction that take into account the variation in texture, color and shape of objects in the image, were tested. The trials aimed for automatic extraction of features of municipal interest, based on aerial and satellite high-resolution (orthophotos, QuickBird and IKONOS imagery) as well as elevation data (altimetric information and LiDAR data). To evaluate the potential of geographic information extracted from VHR images, two areas of application were identified: mapping and analytical purposes. Four case studies that reflect different uses of geographic data at the municipal level, with different accuracy requirements, were considered. The first case study presents a methodology for periodic updating of large-scale maps based on orthophotos, in the area of Alta de Lisboa. This is a situation where the positional and geometric accuracy of the extracted information are more demanding, since technical mapping standards must be complied. In the second case study, an alarm system that indicates the location of potential changes in building areas, using a QuickBird image and LiDAR data, was developed for the area of Bairro da Madre de Deus. The goal of the system is to assist the updating of large scale mapping, providing a layer that can be used by the municipal technicians as the basis for manual editing. In the third case study, the analysis of the most suitable roof-tops for installing solar systems, using LiDAR data, was performed in the area of Avenidas Novas. A set of urban environment indicators obtained from VHR imagery is presented. The concept is demonstrated for the entire city of Lisbon, through IKONOS imagery processing. In this analytical application, the positional quality issue of extraction is less relevant.GEOSAT – Methodologies to extract large scale GEOgraphical information from very high resolution SATellite images (PTDC/GEO/64826/2006), e-GEO – Centro de Estudos de Geografia e Planeamento Regional, da Faculdade de Ciências Sociais e Humanas, no quadro do Grupo de Investigação Modelação Geográfica, Cidades e Ordenamento do Territóri

Automatic 3D Building Detection and Modeling from Airborne LiDAR Point Clouds

Urban reconstruction, with an emphasis on man-made structure modeling, is an active research area with broad impact on several potential applications. Urban reconstruction combines photogrammetry, remote sensing, computer vision, and computer graphics. Even though there is a huge volume of work that has been done, many problems still remain unsolved. Automation is one of the key focus areas in this research. In this work, a fast, completely automated method to create 3D watertight building models from airborne LiDAR (Light Detection and Ranging) point clouds is presented. The developed method analyzes the scene content and produces multi-layer rooftops, with complex rigorous boundaries and vertical walls, that connect rooftops to the ground. The graph cuts algorithm is used to separate vegetative elements from the rest of the scene content, which is based on the local analysis about the properties of the local implicit surface patch. The ground terrain and building rooftop footprints are then extracted, utilizing the developed strategy, a two-step hierarchical Euclidean clustering. The method presented here adopts a divide-and-conquer scheme. Once the building footprints are segmented from the terrain and vegetative areas, the whole scene is divided into individual pendent processing units which represent potential points on the rooftop. For each individual building region, significant features on the rooftop are further detected using a specifically designed region-growing algorithm with surface smoothness constraints. The principal orientation of each building rooftop feature is calculated using a minimum bounding box fitting technique, and is used to guide the refinement of shapes and boundaries of the rooftop parts. Boundaries for all of these features are refined for the purpose of producing strict description. Once the description of the rooftops is achieved, polygonal mesh models are generated by creating surface patches with outlines defined by detected vertices to produce triangulated mesh models. These triangulated mesh models are suitable for many applications, such as 3D mapping, urban planning and augmented reality

Building extraction from airborne laser scanning data : an analysis of the state of the art

This article provides an overview of building extraction approaches applied to Airborne Laser Scanning (ALS) data by examining elements used in original publications, such as data set area, accuracy measures, reference data for accuracy assessment, and the use of auxiliary data. We succinctly analyzed the most cited publication for each year between 1998 and 2014, resulting in 54 ISI-indexed articles and 14 non-ISI indexed publications. Based on this, we position some built-in features of ALS to create a comprehensive picture of the state of the art and the progress through the years. Our analyses revealed trends and remaining challenges that impact the community. The results show remaining deficiencies, such as inconsistent accuracy assessment measures, limitations of independent reference data sources for accuracy assessment, relatively few documented applications of the methods to wide area data sets, and the lack of transferability studies and measures. Finally, we predict some future trends and identify some gaps which existing approaches may not exhaustively cover. Despite these deficiencies, this comprehensive literature analysis demonstrates that ALS data is certainly a valuable source of spatial information for building extraction. When taking into account the short civilian history of ALS one can conclude that ALS has become well established in the scientific community and seems to become indispensable in many application fields.(VLID)174964

Terrain Referenced Navigation Using SIFT Features in LiDAR Range-Based Data

The use of GNSS in aiding navigation has become widespread in aircraft. The long term accuracy of INS are enhanced by frequent updates of the highly precise position estimations GNSS provide. Unfortunately, operational environments exist where constant signal or the requisite number of satellites are unavailable, significantly degraded, or intentionally denied. This thesis describes a novel algorithm that uses scanning LiDAR range data, computer vision features, and a reference database to generate aircraft position estimations to update drifting INS estimates. The algorithm uses a single calibrated scanning LiDAR to sample the range and angle to the ground as an aircraft flies, forming a point cloud. The point cloud is orthorectified into a coordinate system common to a previously recorded reference of the flyover region. The point cloud is then interpolated into a Digital Elevation Model (DEM) of the ground. Range-based SIFT features are then extracted from both the airborne and reference DEMs. Features common to both the collected and reference range images are selected using a SIFT descriptor search. Geometrically inconsistent features are filtered out using RANSAC outlier removal, and surviving features are projected back to their source coordinates in the original point cloud. The point cloud features are used to calculate a least squares correspondence transform that aligns the collected features to the reference features. Applying the correspondence that best aligns the ground features is then applied to the nominal aircraft position, creating a new position estimate. The algorithm was tested on legacy flight data and typically produces position estimates within 10 meters of truth using threshold conditions

Automatic reconstruction of three-dimensional building models from dense image matching datasets

PhD ThesisThe generation of three-dimensional (3D) building models without roof geometry is currently easily automated using a building footprint and single height value. The automatic reconstruction of roof structures, however, remains challenging because of the complexity and variability in building geometry. Attempts from imagery have utilised high spatial resolution but have only reconstructed simple geometry. This research addresses the complexity of roof geometry reconstruction by developing an approach, which focuses on the extraction of corners to reconstruct 3D buildings as boundary representation models, to try overcome the limitations of planar fitting procedures, which are currently favoured. Roof geometry information was extracted from surface models, true orthophotos and photogrammetric point clouds; reconstructed at the same spatial resolution of the captured aerial imagery, with developments in pixel-to-pixel matching. Edges of roof planes were extracted by the Canny edge detector, and then refined with a workflow based on the principles of scan-line segmentation to remove false positive detection. Line tracing procedures defined the corner positions of the extracted edges. A connectivity ruleset was developed, which searches around the endpoints of unconnected lines, testing for potential connecting corners. All unconnected lines were then removed reconstruct 3D models as a closed network of connecting roof corners. Building models have been reconstructed both as block models and also with roof structures. The methodology was tested on data of Newcastle upon Tyne, United Kingdom, with results showing corner extraction success at 75% and to within a planimetric accuracy of ±0.5 m. The methodology was then tested on data of Vaihingen, Germany, which forms part of the ISPRS 3D reconstruction benchmark. This allowed direct comparisons to be made with other methods. The results from both study areas showed similar planimetric accuracy of extracted corners. However, both sites were not as successful in the reconstruction of roof planes.Ordnance Surve

GEOSPATIAL-BASED ENVIRONMENTAL MODELLING FOR COASTAL DUNE ZONE MANAGEMENT

Tomaintain biodiversity and ecological functionof coastal dune areas, itis important that practical and effective environmentalmanagemental strategies are developed. Advances in geospatial technologies offer a potentially very useful source of data for studies in this environment. This research project aimto developgeospatialdata-basedenvironmentalmodellingforcoastaldunecomplexestocontributetoeffectiveconservationstrategieswithparticularreferencetotheBuckroneydunecomplexinCo.Wicklow,Ireland.Theprojectconducteda general comparison ofdifferent geospatial data collection methodsfor topographic modelling of the Buckroney dune complex. These data collection methodsincludedsmall-scale survey data from aerial photogrammetry, optical satellite imagery, radar and LiDAR data, and ground-based, large-scale survey data from Total Station(TS), Real Time Kinematic (RTK) Global Positioning System(GPS), terrestrial laser scanners (TLS) and Unmanned Aircraft Systems (UAS).The results identifiedthe advantages and disadvantages of the respective technologies and demonstrated thatspatial data from high-end methods based on LiDAR, TLS and UAS technologiesenabled high-resolution and high-accuracy 3D datasetto be gathered quickly and relatively easily for the Buckroney dune complex. Analysis of the 3D topographic modelling based on LiDAR, TLS and UAS technologieshighlighted the efficacy of UAS technology, in particular,for 3D topographicmodellingof the study site.Theproject then exploredthe application of a UAS-mounted multispectral sensor for 3D vegetation mappingof the site. The Sequoia multispectral sensorused in this researchhas green, red, red-edge and near-infrared(NIR)wavebands, and a normal RGB sensor. The outcomesincludedan orthomosiac model, a 3D surface model and multispectral imageryof the study site. Nineclassification strategies were usedto examine the efficacyof UAS-IVmounted multispectral data for vegetation mapping. These strategies involved different band combinations based on the three multispectral bands from the RGB sensor, the four multispectral bands from the multispectral sensor and sixwidely used vegetation indices. There were 235 sample areas (1 m × 1 m) used for anaccuracy assessment of the classification of thevegetation mapping. The results showed vegetation type classification accuracies ranging from 52% to 75%. The resultdemonstrated that the addition of UAS-mounted multispectral data improvedthe classification accuracy of coastal vegetation mapping of the Buckroney dune complex

Semi-Automated DIRSIG scene modeling from 3D lidar and passive imagery

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is an established, first-principles based scene simulation tool that produces synthetic multispectral and hyperspectral images from the visible to long wave infrared (0.4 to 20 microns). Over the last few years, significant enhancements such as spectral polarimetric and active Light Detection and Ranging (lidar) models have also been incorporated into the software, providing an extremely powerful tool for multi-sensor algorithm testing and sensor evaluation. However, the extensive time required to create large-scale scenes has limited DIRSIG’s ability to generate scenes ”on demand.” To date, scene generation has been a laborious, time-intensive process, as the terrain model, CAD objects and background maps have to be created and attributed manually. To shorten the time required for this process, this research developed an approach to reduce the man-in-the-loop requirements for several aspects of synthetic scene construction. Through a fusion of 3D lidar data with passive imagery, we were able to semi-automate several of the required tasks in the DIRSIG scene creation process. Additionally, many of the remaining tasks realized a shortened implementation time through this application of multi-modal imagery. Lidar data is exploited to identify ground and object features as well as to define initial tree location and building parameter estimates. These estimates are then refined by analyzing high-resolution frame array imagery using the concepts of projective geometry in lieu of the more common Euclidean approach found in most traditional photogrammetric references. Spectral imagery is also used to assign material characteristics to the modeled geometric objects. This is achieved through a modified atmospheric compensation applied to raw hyperspectral imagery. These techniques have been successfully applied to imagery collected over the RIT campus and the greater Rochester area. The data used include multiple-return point information provided by an Optech lidar linescanning sensor, multispectral frame array imagery from the Wildfire Airborne Sensor Program (WASP) and WASP-lite sensors, and hyperspectral data from the Modular Imaging Spectrometer Instrument (MISI) and the COMPact Airborne Spectral Sensor (COMPASS). Information from these image sources was fused and processed using the semi-automated approach to provide the DIRSIG input files used to define a synthetic scene. When compared to the standard manual process for creating these files, we achieved approximately a tenfold increase in speed, as well as a significant increase in geometric accuracy