The potential of small unmanned aircraft systems and structure-from-motion for topographic surveys: a test of emerging integrated approaches at Cwm Idwal, North Wales

This paper was accepted for publication in the journal Geomorphology and the definitive published version is available at http://dx.doi.org/10.1016/j.geomorph.2014.07.021Novel topographic survey methods that integrate both structure-from-motion (SfM) photogrammetry and small unmanned aircraft systems (sUAS) are a rapidly evolving investigative technique. Due to the diverse range of survey configurations available and the infancy of these new methods, further research is required. Here, the accuracy, precision and potential applications of this approach are investigated. A total of 543 images of the Cwm Idwal moraine–mound complex were captured from a light (b5 kg) semi-autonomous multi-rotor unmanned aircraft system using a consumer-grade 18 MP compact digital camera. The imageswere used to produce a DSM(digital surfacemodel) of themoraines. The DSMis in good agreement with 7761 total station survey points providing a total verticalRMSE value of 0.517mand verticalRMSE values as lowas 0.200mfor less densely vegetated areas of the DSM. High-precision topographic data can be acquired rapidly using this technique with the resulting DSMs and orthorectified aerial imagery at sub-decimetre resolutions. Positional errors on the total station dataset, vegetation and steep terrain are identified as the causes of vertical disagreement. Whilst this aerial survey approach is advocated for use in a range of geomorphological settings, care must be taken to ensure that adequate ground control is applied to give a high degree of accuracy

Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours

Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing field maps and observation systems rely on historic administrative maps or labor-intensive field campaigns. These are often expensive to maintain and quickly become outdated, especially in regions of frequently changing agricultural patterns. However, exploiting openly available remote sensing imagery (e.g., from the European Union’s Copernicus programme) may allow for frequent and efficient field mapping with minimal human interaction. We present a new approach to extracting agricultural fields at the sub-pixel level. It consists of boundary detection and a field polygon extraction step based on a newly developed, modified version of the growing snakes active contours model we refer to as graph-based growing contours. This technique is capable of extracting complex networks of boundaries present in agricultural landscapes, and is largely automatic with little supervision required. The whole detection and extraction process is designed to work independently of sensor type, resolution, or wavelength. As a test case, we applied the method to two regions of interest in a study area in the northern Germany using multi-temporal Sentinel-2 imagery. Extracted fields were compared visually and quantitatively to ground reference data. The technique proved reliable in producing polygons closely matching reference data, both in terms of boundary location and statistical proxies such as median field size and total acreage

Article Operational Ship Monitoring System Based on Synthetic Aperture Radar Processing

Abstract: This paper presents a Ship Monitoring System (SIMONS) working with Synthetic Aperture Radar (SAR) images. It is able to infer ship detection and classification information, and merge the results with other input channels, such as polls from the Automatic Identification System (AIS). Two main stages can be identified, namely: SAR processing and data dissemination. The former has three independent modules, which are related to Coastline Detection (CD), Ship Detection (SD) and Ship Classification (SC). The later is solved via an advanced web interface, which is compliant with the OpenSource standards fixed by the Open Geospatial Consortium (OGC). SIMONS has been designed to be a modular, unsupervised and reliable system that meets Near-Real Time (NRT) delivery requirements. From data ingestion to product delivery, the processing chain is fully automatic accepting ERS and ENVISAT formats. SIMONS has been developed by GMV Aerospace, S.A. with three main goals, namely: 1) To limit the dependence on the ancillary information provided by systems such as AIS. 2) To achieve the maximum level of automatism and restrict human manipulation. 3) To limit the error sources and their propagation

An Evaluation of Deep Learning-Based Object Identification

Identification of instances of semantic objects of a particular class, which has been heavily incorporated in people's lives through applications like autonomous driving and security monitoring, is one of the most crucial and challenging areas of computer vision. Recent developments in deep learning networks for detection have improved object detector accuracy. To provide a detailed review of the current state of object detection pipelines, we begin by analyzing the methodologies employed by classical detection models and providing the benchmark datasets used in this study. After that, we'll have a look at the one- and two-stage detectors in detail, before concluding with a summary of several object detection approaches. In addition, we provide a list of both old and new apps. It's not just a single branch of object detection that is examined. Finally, we look at how to utilize various object detection algorithms to create a system that is both efficient and effective. and identify a number of emerging patterns in order to better understand the using the most recent algorithms and doing more study

Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91–99% for healthy subjects and 70–85% for stroke patients

Application of mixed and virtual reality in geoscience and engineering geology

Visual learning and efficient communication in mining and geotechnical practices is crucial, yet often challenging. With the advancement of Virtual Reality (VR) and Mixed Reality (MR) a new era of geovisualization has emerged. This thesis demonstrates the capabilities of a virtual continuum approach using varying scales of geoscience applications. An application that aids analyses of small-scale geological investigation was constructed using a 3D holographic drill core model. A virtual core logger was also developed to assist logging in the field and subsequent communication by visualizing the core in a complementary holographic environment. Enriched logging practices enhance interpretation with potential economic and safety benefits to mining and geotechnical infrastructure projects. A mine-scale model of the LKAB mine in Sweden was developed to improve communication on mining induced subsidence between geologists, engineers and the public. GPS, InSAR and micro-seismicity data were hosted in a single database, which was geovisualized through Virtual and Mixed Reality. The wide array of applications presented in this thesis illustrate the potential of Mixed and Virtual Reality and improvements gained on current conventional geological and geotechnical data collection, interpretation and communication at all scales from the micro- (e.g. thin section) to the macro- scale (e.g. mine)

Filtering of image sequences: on line edge detection and motion reconstruction

L'argomento della Tesi riguarda líelaborazione di sequenze di immagini, relative ad una scena in cui uno o pi˘ oggetti (possibilmente deformabili) si muovono e acquisite da un opportuno strumento di misura. A causa del processo di misura, le immagini sono corrotte da un livello di degradazione. Si riporta la formalizzazione matematica dellíinsieme delle immagini considerate, dellíinsieme dei moti ammissibili e della degradazione introdotta dallo strumento di misura. Ogni immagine della sequenza acquisita ha una relazione con tutte le altre, stabilita dalla legge del moto della scena. Líidea proposta in questa Tesi Ë quella di sfruttare questa relazione tra le diverse immagini della sequenza per ricostruire grandezze di interesse che caratterizzano la scena. Nel caso in cui si conosce il moto, líinteresse Ë quello di ricostruire i contorni dellíimmagine iniziale (che poi possono essere propagati attraverso la stessa legge del moto, in modo da ricostruire i contorni della generica immagine appartenente alla sequenza in esame), stimando líampiezza e del salto del livello di grigio e la relativa localizzazione. Nel caso duale si suppone invece di conoscere la disposizione dei contorni nellíimmagine iniziale e di avere un modello stocastico che descriva il moto; líobiettivo Ë quindi stimare i parametri che caratterizzano tale modello. Infine, si presentano i risultati dellíapplicazione delle due metodologie succitate a dati reali ottenuti in ambito biomedicale da uno strumento denominato pupillometro. Tali risultati sono di elevato interesse nellíottica di utilizzare il suddetto strumento a fini diagnostici