Coastal monitoring and feature estimation with small format cameras: application to the shoreline of Monte Hermoso, Argentina

Image and video processing of natural phenomena is one of the preferred non-invasive monitoring techniques for environmental studies that is, however, limited through the high cost of the required equipment and the limited access and precision of the processing algorithms. In this work we propose a low cost methodology for environmental studies using unexpensive off-the-shelf hardware and simple yet powerful processing algorithms. The images are taken using small format RGB cameras and processed in standard laptop equipments using open source libraries and processing algorithms specifically developed in general purpose programming languages. We applied this methodology to the coastal monitoring the shoreline of Monte Hermoso, Argentina, aimed at establishing accurate measurements of specific coastal features, for instance the coastal length. The experimental results show that our proposed unsupervised processing algorithm obtains results with a very high level of accuracy.VII Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Investigating The Potential Of Satellite Images In Topographic Map Production

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2013Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2013Bu tezin amacı topografik harita üretim süreçlerinde farklı spektral ve mekansal çözünürlükteki uydu görüntülerinin kullanım olanaklarını incelemektir. Topografik haritalar yükseklik bilgisi (eş yükseklik eğrileri) olmaksızın, sadece iki boyutlu olarak ele alınmıştır. Bu kapsamda, gereksinim matrislerini oluşturmak üzere, ölçeğe bağlı olarak topografik harita özellikleri ve mekansal çözünürlüğe bağlı olarak uydu görüntüsü karakteristikleri incelenmiştir. Ulusal ve uluslararası standartlardan yararlanarak ölçek ve mekansal çözünürlük arasındaki ilişki kurulmuş ve topografik harita ve uydu görüntüleri için gereksinim matrisleri oluşturulmuştur. Uygulama, aynı bölgeye ait Worldview-2, SPOT-5 ve Landsat-5 TM uydu görüntülerinde ulaşım, yerleşim, bitki örtüsü ve su bilgisini içerecek çizgisel ve alansal detayların sayısallaştırılması ve sonuçların karşılaştırılmasını kapsamaktadır. Sayısallaştırma sonuçları gereksinim matrisleri de göz önünde bulundurularak geometrik ve tematik anlamda irdelenmiştir. Geometrik doğruluğu yüksek (en az 0.5 m) yer kontrol noktalarına sahip Worldview-2 görüntüsü kullanılarak amaca ve kullanıcıya bağlı olarak 1 : 5 000 ölçekli harita üretilebilir. SPOT-5 ise bazı kısıtlamalarla birlikte 1 : 25 000 ölçekli harita üretime elverişlidir.This thesis aims to examine the usage possibilities of remote sensing imagery at different spectral and spatial resolutions in topographic map production procedure. While considering the topographic maps, third dimension (i.e height information) was kept out of the thesis. In this context, characteristics of topographic maps (by means of scale) and satellite image data (by means of spatial resolution) were examined in order to create requirement matrices for maps and remote sensing data. Benefiting from several national and international standards, the relationship between map scale and spatial resolution was introduced and requirements of maps and satellite data were determined. A case study was conducted with Worldview-2, SPOT-5 and Landsat-5 TM images belonging to the same study area. On-screen digitization (linear and areal) was applied to the images for identifying administrative, transportation, vegetation and hydrographic details. Digitization results obtained from three different images were compared and evaluated considering requirement matrices by means geometric and thematic aspects. With a good geometric accuracy of ground control points (at least 0.5 m), Worldview-2 images can be used to generate 1 : 5 000 scale maps depending on the purpose and user whereas SPOT-5 is suitable for 1 : 25 000-scale mapping.Yüksek LisansM.Sc

Satellite Image Based Classification Mapping For Spatially Analyzing West Virginia Corridor H Urban Development

The study area for this project is Corridor H, a designated Appalachian Development Highway located in Lewis, Upshur, Barbour Counties which are part of the Appalachian Plateau Province, and Randolph, Tucker, Grant and Hardy Counties which are Part of the Ridge and Valley Province in West Virginia. The region has a long history of occupation and a traditional economic structure consisting of mainly agriculture, timbering and coal mining. The final objective of the study was to perform change detection, using two Landsat datasets obtained from the USGS of the study area from 1987 and 2005 to determine if economic development, via change to cropland/ pasture and Urban Built Up Areas, could be measured and detected along Corridor H by using remote sensing techniques. Geometric Registration, Principal Component Analysis, Radiometric Normalization, Accuracy Analysis, Unsupervised Classification, and Spatial Analysis logical operators were utilized in IDRISI, ERMapper, and ESRI to complete the study. The total land change for the study area for Urban was 1.4% of the total 2,573,351 acres and 4.9% for change in Cropland/Pasture. More significantly there is a 2.7 %increase in Urban development within a 1-mile buffer around the length of Corridor H in the study area. When a buffer was placed 1-mile around Corridor H from Weston to Elkins the percentage of change increased to 4.5% for Urban areas and 7.5% for Cropland/Pasture. These results indicate economic change is occurring already along Corridor H before its completion. The development of this data will provide a baseline on which to base future studies of the area for tracking the expected economic growth of the region, and for Appalachian corridor highway systems in general. This data should be used with more traditional methods of economic impact and growth reporting and measurement, to focus these studies, and supply spatial relevance to changes in rural Appalachia

Deep neural network for city mapping using Google Street View data

S rozvojem výpočetní síly a rozsáhlýmidatovými soubory vede masivní zlepšeníhluboké neuronové sítě k mnoha rozšíře-ným aplikacím. Jednou z aplikací hlubokéneuronové sítě je řešení problémů počíta-čového vidění, jako je klasifikace a segmen-tace. Soutěž jako ImageNet Výzva provizuální rozpoznávání ve velkém měřítku posunula schopnost na další úroveň;v některých případech je klasifikace lepšínež lidská.Tato práce je příkladem aplikace vyu-žívající schopnost neuronových sítí. Do-kument popisuje implementaci, metodiku,experimenty prováděné pro vývoj softwa-rových řešení pomocí hluboké neuronovésítě na obrázkových prostředcích z ob-rázků Google Street View .Uživatel poskytuje soubor geojson se-stávající z oblasti zájmu ve tvaru čtvercenebo mnohoúhelníku jako vstup. GoogleStreetView API stáhne dostupné ob-rázky. Snímky jsou nejprve zpracoványpomocí nejmodernějších CNN (Mask R-CNN), aby detekovaly objekty, kla-sifikovaly je pomocí skóre spolehlivosti,vytvořily ohraničující rámeček a kolemdetekovaného objektu malovaly pixely. .Textový soubor ukládá informace, jakojsou souřadnice ohraničovacího rámečku,název třídy a hodnoty masky.Obyčejný RGB (panoramatický) sní-mek z GSV neobsahuje žádné hloubkovéúdaje. Obrázky jsou zpracovávány s jinýmnejmodernějším CNN (monodepth2),aby se odhadla hloubka objektů v obra-zech po pixelech.Průměrná hodnota hloubky v masce sepoužívá jako vzdálenost objektu. Souřad-nice ohraničovacího rámečku se používajípro umístění objektu v jiných osách.Výsledné výstupy jsou markery deteko-vaných objektů, které jsou základem mapy.Sloupcový graf pro vizualizaci počtu de-tekcí ve třídě. Textový soubor obsahujícípočet detekcí pro každou třídu. Výstupz každého kroku zpracování výše, jakojsou detekce, hloubkové obrázky, hodnotymasky pro porovnání a vyhodnocení.With the advancement of computation power and large datasets, a massive improvement of the deep neural network leads to many widespread applications. One of the applications of the deep neural network is solving computer vision problems like classification and segmentation.Competition like ImageNet Large Scale Visual Recognition Challenge, took the capability to the next level; in some cases, classification is better than human. This thesis is an example of an application that utilizes the ability of neural networks. The document describes the implementation, methodology, experiments done for developing software solutions by using the deep neural network on image resources form Google Street View images. The user provides a geojson file consists of an area of interest in the form of square or polygon as the input. Google StreetView API downloads the available images. The images are first processed with the state of the art CNN (Mask R-CNN) to detect the objects, classify them with the confidence score, generate a bounding box, and a pixel-wise mask around the detected object. The text file stores information like coordinates of the bounding box, name of the class, and the mask values. An ordinary RGB ( panoramic ) image from GSV does not consist of any depth data. The images are processed with another state of art CNN (monodepth2), to estimate the pixel-wise depth of the objects in the images. The averaged value of the depth within the mask is used as the distance of the object. The coordinates of the bounding box are used for positioning of the object in other axes. The resulting outputs are markers of detected objects underlying in the map. A bar graph to visualize the number of detection per class. A text file containing the number of detection per each class. The output from each processing step above, like detections, depth images, mask values to compare and evaluate

Planar projection of mobile laser scanning data in tunnels

Laser scanning is now a common technology in the surveying and monitoring of large engineering infrastructures, such as tunnels, both in motorways and railways. Extended possibilities exist now with the mobile terrestrial laser scanning systems, which produce very large data sets that need efficient processing techniques in order to facilitate their exploitation and usability. This paper deals with the implementation of a methodology for processing and presenting 3D point clouds acquired by laser scanning in tunnels, making use of the approximately cylindrical shape of tunnels. There is a need for a 2D presentation of the 3D point clouds, in order to facilitate the inspection of important features as well as to easily obtain their spatial location. An algorithm was developed to treat automatically point clouds obtained in tunnels in order to produce rectified images that can be analysed. Tests were carried with data acquired with static and mobile Riegl laser scanning systems, by Artescan company, in highway tunnels in Portugal and Spain, with very satisfactory results. The final planar image is an alternative way of data presentation where image analysis tools can be used to analyze the laser intensity in order to detect problems in the tunnel structure

Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model.

We propose a new algorithm for network segmentation from very high resolution (VHR) remote sensing images. The algorithm performs this task quasi-automatically, that is, with no human intervention except to fix some parameters. The task is made difficult by the amount of prior knowledge about network region geometry needed to perform the task, knowledge that is usually provided by a human being. To include such prior knowledge, we make use of methodological advances in region modelling: a phase field higher-order active contour of directed networks is used as the prior model for region geometry. By adjoining an approximately conserved flow to a phase field model encouraging network shapes (i.e. regions composed of branches meeting at junctions), the model favours network regions in which different branches may have very different widths, but in which width change along a branch is slow; in which branches do not come to an end, hence tending to close gaps in the network; and in which junctions show approximate 'conservation of width'. We also introduce image models for network and background, which are validated using maximum likelihood segmentation against other possibilities. We then test the full model on VHR optical and multispectral satellite images

Coastal monitoring and feature estimation with small format cameras: application to the shoreline of Monte Hermoso, Argentina

Image and video processing of natural phenomena is one of the preferred non-invasive monitoring techniques for environmental studies that is, however, limited through the high cost of the required equipment and the limited access and precision of the processing algorithms. In this work we propose a low cost methodology for environmental studies using unexpensive off-the-shelf hardware and simple yet powerful processing algorithms. The images are taken using small format RGB cameras and processed in standard laptop equipments using open source libraries and processing algorithms specifically developed in general purpose programming languages. We applied this methodology to the coastal monitoring the shoreline of Monte Hermoso, Argentina, aimed at establishing accurate measurements of specific coastal features, for instance the coastal length. The experimental results show that our proposed unsupervised processing algorithm obtains results with a very high level of accuracy.VII Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Algorithms and Data Structures for Automated Change Detection and Classification of Sidescan Sonar Imagery

During Mine Warfare (MIW) operations, MIW analysts perform change detection by visually comparing historical sidescan sonar imagery (SSI) collected by a sidescan sonar with recently collected SSI in an attempt to identify objects (which might be explosive mines) placed at sea since the last time the area was surveyed. This dissertation presents a data structure and three algorithms, developed by the author, that are part of an automated change detection and classification (ACDC) system. MIW analysts at the Naval Oceanographic Office, to reduce the amount of time to perform change detection, are currently using ACDC. The dissertation introductory chapter gives background information on change detection, ACDC, and describes how SSI is produced from raw sonar data. Chapter 2 presents the author\u27s Geospatial Bitmap (GB) data structure, which is capable of storing information geographically and is utilized by the three algorithms. This chapter shows that a GB data structure used in a polygon-smoothing algorithm ran between 1.3 – 48.4x faster than a sparse matrix data structure. Chapter 3 describes the GB clustering algorithm, which is the author\u27s repeatable, order-independent method for clustering. Results from tests performed in this chapter show that the time to cluster a set of points is not affected by the distribution or the order of the points. In Chapter 4, the author presents his real-time computer-aided detection (CAD) algorithm that automatically detects mine-like objects on the seafloor in SSI. The author ran his GB-based CAD algorithm on real SSI data, and results of these tests indicate that his real-time CAD algorithm performs comparably to or better than other non-real-time CAD algorithms. The author presents his computer-aided search (CAS) algorithm in Chapter 5. CAS helps MIW analysts locate mine-like features that are geospatially close to previously detected features. A comparison between the CAS and a great circle distance algorithm shows that the CAS performs geospatial searching 1.75x faster on large data sets. Finally, the concluding chapter of this dissertation gives important details on how the completed ACDC system will function, and discusses the author\u27s future research to develop additional algorithms and data structures for ACDC

Urban Development and Effects to Transportation Systems of Lexington, Fayette County, Kentucky

Urban growth and its relationship to the transportation system of Lexington, Kentucky were investigated using temporal images of Landsat Thematic Mapper and multi-date line data. Land cover of the area was extracted using Remote Sensing and Geographic Information Systems Technologies. Change detection techniques were employed to identify areas of transformation between 1988 and 2000. Several digital image processes such as geometric registration, radiometric normalization, unsupervised classification, and accuracy assessment were applied to analyze the results. Vector analysis was utilized as well along with raster analysis to examine the effect of urban growth to the transportation system. Two datasets of line data were examined. Integration of both vector and raster analyses were implemented to interpret the urban growth. The final outcome shows that accelerated urban development had taken place in the study area and, as a result, the transportation system had developed to support the increased volume of the municipal area

Monitoring Trails and Disturbance in Joshua Tree National Park

Joshua Tree National Park, well known for its recreation activities, is in need of improved vegetation and trail monitoring programs. Specifically, social trails, or trails created by users that deviate from designated paths, are major contributors to vegetation disturbance and loss. Current activity levels are beginning to negatively affect surrounding landscapes. This project was developed to enable staff to monitor large regions of the park without expending significant man-hours or costs. With this in mind, the project was developed using QuickBird satellite imagery as the main component for feature extraction from an ESRI system with the Feature Analyst (FA) and Spatial Analyst extensions. The deliverables for this project were a master geodatabase, vegetation index, and a feature class containing all the social trails within a given region. A customized ArcToolbox and model were developed, in addition to a complete process-flow highlighting the steps required to process and analyze the data. The implemented tools and methods in the project enabled the client to monitor large regions of the park with less effort than field data collection