New sensors benchmark report on Kompsat-3

The following document has been drawn up as a follow up to the Quality Control Record L [i] on the commissioning phase of the Kompsat-3 imagery, planned benchmarking tests as well as the methodology used in the tests. Benchmarking is necessary to be performed in order to estimate the usability of the imagery collected by particular sensor in The Common Agricultural Policy (CAP) image acquisition Campaign. The main requirement that should be fulfilled concerns the planimetric accuracy of the orthoimagery which should not exceed particular thresholds given in VHR Specifications [iii]. The methodologies used in the benchmarking tests were performed based on Guidelines for Best Practice and Quality Checking of Ortho Imagery [ii]. However, in addition the tests were performed according to alternative methodology, described in [i], which differs from the standard one, the GCPs selection/measurement phase i.e. image to image correlation techniques are used.JRC.H.6-Digital Earth and Reference Dat

An investigation into semi-automated 3D city modelling

Creating three dimensional digital representations of urban areas, also known as 3D city modelling, is essential in many applications, such as urban planning, radio frequency signal propagation, flight simulation and vehicle navigation, which are of increasing importance in modern society urban centres. The main aim of the thesis is the development of a semi-automated, innovative workflow for creating 3D city models using aerial photographs and LiDAR data collected from various airborne sensors. The complexity of this aim necessitates the development of an efficient and reliable way to progress from manually intensive operations to an increased level of automation. The proposed methodology exploits the combination of different datasets, also known as data fusion, to achieve reliable results in different study areas. Data fusion techniques are used to combine linear features, extracted from aerial photographs, with either LiDAR data or any other source available including Very Dense Digital Surface Models (VDDSMs). The research proposes a method which employs a semi automated technique for 3D city modelling by fusing LiDAR if available or VDDSMs with 3D linear features extracted from stereo pairs of photographs. The building detection and the generation of the building footprint is performed with the use of a plane fitting algorithm on the LiDAR or VDDSMs using conditions based on the slope of the roofs and the minimum size of the buildings. The initial building footprint is subsequently generalized using a simplification algorithm that enhances the orthogonality between the individual linear segments within a defined tolerance. The final refinement of the building outline is performed for each linear segment using the filtered stereo matched points with a least squares estimation. The digital reconstruction of the roof shapes is performed by implementing a least squares-plane fitting algorithm on the classified VDDSMs, which is restricted by the building outlines, the minimum size of the planes and the maximum height tolerance between adjacent 3D points. Subsequently neighbouring planes are merged using Boolean operations for generation of solid features. The results indicate very detailed building models. Various roof details such as dormers and chimneys are successfully reconstructed in most cases

Surveying of Tall Buildings over Theory Height within Obstacle Free Airspace

According to the National Airport standards, this article has looked over the height of 180000 buildings in the airfield control height region of Cencun Airport (about 123.6 sq. km) for the frst time. In the paper, based on the planning & approving data and ADS40 aerial survey data, GIS spatial analysis, ADS40 stereo-altimetry technology, field digital measurement and 2D/3D visualization are used synthetically to achieve the results above. Furthermore, the quantity and spatial distribution of over theoretical height buildings are clarifed, which is signifcant to review the airfeld height management and to guide the regulatory plan of Tianhe Intellectualized Business District

An investigation into semi-automated 3D city modelling

Creating three dimensional digital representations of urban areas, also known as 3D city modelling, is essential in many applications, such as urban planning, radio frequency signal propagation, flight simulation and vehicle navigation, which are of increasing importance in modern society urban centres. The main aim of the thesis is the development of a semi-automated, innovative workflow for creating 3D city models using aerial photographs and LiDAR data collected from various airborne sensors. The complexity of this aim necessitates the development of an efficient and reliable way to progress from manually intensive operations to an increased level of automation. The proposed methodology exploits the combination of different datasets, also known as data fusion, to achieve reliable results in different study areas. Data fusion techniques are used to combine linear features, extracted from aerial photographs, with either LiDAR data or any other source available including Very Dense Digital Surface Models (VDDSMs). The research proposes a method which employs a semi automated technique for 3D city modelling by fusing LiDAR if available or VDDSMs with 3D linear features extracted from stereo pairs of photographs. The building detection and the generation of the building footprint is performed with the use of a plane fitting algorithm on the LiDAR or VDDSMs using conditions based on the slope of the roofs and the minimum size of the buildings. The initial building footprint is subsequently generalized using a simplification algorithm that enhances the orthogonality between the individual linear segments within a defined tolerance. The final refinement of the building outline is performed for each linear segment using the filtered stereo matched points with a least squares estimation. The digital reconstruction of the roof shapes is performed by implementing a least squares-plane fitting algorithm on the classified VDDSMs, which is restricted by the building outlines, the minimum size of the planes and the maximum height tolerance between adjacent 3D points. Subsequently neighbouring planes are merged using Boolean operations for generation of solid features. The results indicate very detailed building models. Various roof details such as dormers and chimneys are successfully reconstructed in most cases

Automated processing of high resolution airborne images for earthquake damage assessment

Emergency response ought to be rapid, reliable and efficient in terms of bringing the necessary help to sites where it is actually needed. Although the remote sensing techniques require minimum fieldwork and allow for continuous coverage, the established approaches rely on a vast manual work and visual assessment thus are time-consuming and imprecise. Automated processes with little possible interaction are in demand. This paper attempts to address the aforementioned issues by employing an unsupervised classification approach to identify building areas affected by an earthquake event. The classification task is formulated in the Markov Random Fields (MRF) framework and only post-event airborne high-resolution images serve as the input. The generated photogrammetric Digital Surface Model (DSM) and a true orthophoto provide height and spectral information to characterize the urban scene through a set of features. The classification proceeds in two phases, one for distinguishing the buildings out of an urban context (urban classification), and the other for identifying the damaged structures (building classification). The algorithms are evaluated on a dataset consisting of aerial images (7 cm GSD) taken after the Emilia-Romagna (Italy) earthquake in 2012

Orientation and integration of images and image blocks with laser scanning data

Laser scanning and photogrammetry are methods for effective and accurate measurement and classification of urban and forest areas. Because these methods complement each other, then integration or integrated use brings additional benefits to real-life applications. However, finding tie features between data sets is a challenging task since laser scanning and imagery are far from each other in nature. The aim of this thesis was to create methods for solving relative orientations between laser scanning data and imagery that would assist in near-future applications integrating laser scanning and photogrammetry. Moreover, a further goal was to create methods enabling the use of data acquired from very different perspectives, such as terrestrial and airborne data. To meet these aims, an interactive orientation method enabling the use of single images, stereo images or larger image blocks was developed and tested. The multi-view approach usually has a significant advantage over the use of a single image. After accurate orientation of laser scanning data and imagery, versatile applications become available. Such applications include, e.g., automatic object recognition, accurate classification of individual trees, point cloud densification, automatic classification of land use, system calibration, and generation of photorealistic 3D models. Besides the orientation part, another aim of the research was to investigate how to fuse or use these two data types together in applications. As a result, examples that evaluated the behavior of laser point clouds in both urban and forestry areas, detection and visualization of temporal changes, enhanced data understanding, stereo visualization, multi-source and multi-angle data fusion, point cloud colorizing, and detailed examination of full waveform laser scanning data were given

Fotogrametría de rango cercano aplicada a la Ingeniería Agroforestal

Tesis por compendio de publicaciones[EN]Since the late twentieth century, Geotechnologies are being applied in different research lines in Agroforestry Engineering aimed at advancing in the modeling of biophysical parameters in order to improve the productivity. In this study, low-cost and close range photogrammetry has been used in different agroforestry scenarios to solve identified gaps in the results and improve procedures and technology hitherto practiced in this field. Photogrammetry offers the advantage of being a non-destructive and non-invasive technique, never changing physical properties of the studied element, providing rigor and completeness to the captured information. In this PhD dissertation, the following contributions are presented divided into three research papers: • A methodological proposal to acquire georeferenced multispectral data of high spatial resolution using a low-cost manned aerial platform, to monitor and sustainably manage extensive áreas of crops. The vicarious calibration is exposed as radiometric calibration method of the multispectral sensor embarked on a paraglider. Low-cost surfaces are performed as control coverages. • The development of a method able to determine crop productivity under field conditions, from the combination of close range photogrammetry and computer vision, providing a constant operational improvement and a proactive management in the crop monitoring. An innovate methodology in the sector is proposed, ensuring flexibility and simplicity in the data collection by non-invasive technologies, automation in processing and quality results with low associated cost. • A low cost, efficient and accurate methodology to obtain Digital Height Models of vegatal cover intended for forestry inventories by integrating public data from LiDAR into photogrammetric point clouds coming from low cost flights. This methodology includes the potentiality of LiDAR to register ground points in areas with high density of vegetation and the better spatial, radiometric and temporal resolution from photogrammetry for the top of vegetal covers.[ES]Desde finales del siglo XX se están aplicando Geotecnologías en diferentes líneas de investigación en Ingeniería Agroforestal orientadas a avanzar en la modelización de parámetros biofísicos con el propósito de mejorar la productividad. En este estudio se ha empleado fotogrametría de bajo coste y rango cercano en distintos escenarios agroforestales para solventar carencias detectadas en los resultados obtenidos y mejorar los procedimientos y la tecnología hasta ahora usados en este campo. La fotogrametría ofrece como ventaja el ser una técnica no invasiva y no destructiva, por lo que no altera en ningún momento las propiedades físicas del elemento estudiado, dotando de rigor y exhaustividad a la información capturada. En esta Tesis Doctoral se presentan las siguientes contribuciones, divididas en tres artículos de investigación: • Una propuesta metodológica de adquisición de datos multiespectrales georreferenciados de alta resolución espacial mediante una plataforma aérea tripulada de bajo coste, para monitorizar y gestionar sosteniblemente amplias extensiones de cultivos. Se expone la calibración vicaria como método de calibración radiométrico del sensor multiespectral embarcado en un paramotor empleando como coberturas de control superficies de bajo coste. • El desarrollo de un método capaz de determinar la productividad del cultivo en condiciones de campo, a partir de la combinación de fotogrametría de rango cercano y visión computacional, facilitando una mejora operativa constante así como una gestión proactiva en la monitorización del cultivo. Se propone una metodología totalmente novedosa en el sector, garantizando flexibilidad y sencillez en la toma de datos mediante tecnologías no invasivas, automatismo en el procesado, calidad en los resultados y un bajo coste asociado. • Una metodología de bajo coste, eficiente y precisa para la obtención de Modelos Digitales de Altura de Cubierta Vegetal destinados al inventario forestal mediante la integración de datos públicos procedentes del LiDAR en las nubes de puntos fotogramétricas obtenidas con un vuelo de bajo coste. Esta metodología engloba la potencialidad del LiDAR para registrar el terreno en zonas con alta densidad de vegetación y una mejor resolución espacial, radiométrica y temporal procedente de la fotogrametría para la parte superior de las cubiertas vegetales

Applied Deep Learning: Case Studies in Computer Vision and Natural Language Processing

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover classification. In addition, a super-resolution deep model was implemented to further enhance remote sensing images. In the third study, a deep learning-based framework was proposed for fact-checking on social media to spot fake scientific news. The framework leveraged deep learning, information retrieval, and natural language processing techniques to retrieve pertinent scholarly papers for given scientific news and evaluate the credibility of the news