866 research outputs found

    GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing

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    Volunteer cotton plants germinate and grow at unwanted locations like transport routes and can serve as hosts for a harmful cotton pests called cotton boll weevils. The main objective of this study was to develop a geographic information system (GIS) framework to efficiently locate volunteer cotton plants in the cotton production regions in southern Texas, thus reducing time and economic cost for their removal. A GIS network analysis tool was applied to estimate the most likely routes for cotton transportation, and a GIS model was created to identify and visualize potential areas of volunteer cotton growth. The GIS model indicated that, of the 31 counties in southern Texas that may have habitat for volunteer cotton, Hidalgo, Cameron, Nueces, and San Patricio are the counties at the greatest risk. Moreover, a method based on unmanned aerial vehicle (UAV) remote sensing was proposed to detect the precise locations of volunteer cotton plants in potential areas for their subsequent removal. In this study, a UAV was used to scan limited samples of potential volunteer cotton growth areas identified with the GIS model. The results indicated that UAV remote sensing coupled with the proposed image analysis methods could accurately identify the precise locations of volunteer cotton and could potentially assist in the elimination of volunteer cotton along transport routes

    AI-Enabled Contextual Representations for Image-based Integration in Health and Safety

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    Recent advancements in the area of Artificial Intelligence (AI) have made it the field of choice for automatically processing and summarizing information in big-data domains such as high-resolution images. This approach, however, is not a one-size-fits-all solution, and must be tailored to each application. Furthermore, each application comes with its own unique set of challenges including technical variations, validation of AI solutions, and contextual information. These challenges are addressed in three human-health and safety related applications: (i) an early warning system of slope failures in open-pit mining operations; (ii) the modeling and characterization of 3D cell culture models imaged with confocal microscopy; and (iii) precision medicine of biomarker discovery from patients with glioblastoma multiforme through digital pathology. The methodologies and results in each of these domains show how tailor-made AI solutions can be used for automatically extracting and summarizing pertinent information from big-data applications for enhanced decision making

    COUPLING BIOPHYSICAL COMPLEXITY AND FOREST METABOLISM IN A FLOODPLAIN LANDSCAPE

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    Floodplains are biophysically complex systems that are considered among the most productive and biodiverse ecosystems on Earth. Until recently, quantitative assessment of the relationship between complexity and terrestrial production has been constrained by technological limitation. To address how floodplain biophysical complexity and ecosystem function are related, I employed remote sensing, GIS, and spatial analyses to quantify and couple metrics of complexity and terrestrial production, as well as explore the relationship among complexity, vegetation structural diversity, and terrestrial primary productivity. The study site is a 6.75-km by 1.75-km portion of the Bitterroot River floodplain near Carlton, MT upon which 551 sample plots were delimited via segmentation classification. Biophysical complexity, characterized by topographic heterogeneity, structural heterogeneity, and hydrologic connectivity was represented in each sample plot by mean standard deviation ground height, vegetative structural diversity index, mean flow length, mean flow accumulation, mean percent inundation, and gamma index metrics computed from Light Detection and Ranging (LiDAR) data, HEC-RAS inundation modeling, and ArcGIS Arc Hydro derived metrics. Potential primary production was represented by Normalized Difference Vegetation Index (NDVI) values generated from aerial 4-band multispectral imagery. Two questions were addressed in the analyses: 1) What is the causal relationship among floodplain physical complexity, vegetation structural diversity, and terrestrial productivity, and 2) How does floodplain biophysical complexity influence terrestrial primary production. Through these efforts, my goal was to explain how the dynamic nature of riverscapes translates to fundamental measures of ecological form and function. NDVI values ranged from -0.27 to 0.43, and were robustly related to biophysical complexity in which the explanatory variables together accounted for 58% of variation in NDVI (p \u3c 0.001). In investigating the relationship between biophysical complexity, vegetation structural diversity, and NDVI, biophysical complexity was positively correlated to NDVI (r2= 0.25, p \u3c 0.001), and structural diversity was positively related to NDVI (r2= 0.51, p \u3c 0.001). These results suggest a causal relationship and support the complexity diversity hypothesis, and the diversity- productivity hypothesis. Structural diversity and connectivity variables accounted for the most explanatory power in all analyses, and overall results indicate that areas of the floodplain with greater biophysical complexity exhibited greater productivity. Davis, Peter, M.S., Summer 2017 Systems Ecology Coupling Biophysical Complexity and Forest Metabolism in Floodplain Landscapes Chairperson: H. Maurice Valett Floodplains are biophysically complex systems that are considered among the most productive and biodiverse ecosystems on Earth. Until recently, quantitative assessment of the relationship between complexity and terrestrial production has been constrained by technological limitation. To address how floodplain biophysical complexity and ecosystem function are related, I employed remote sensing, GIS, and spatial analyses to quantify and couple metrics of complexity and terrestrial production, as well as explore the relationship among complexity, vegetation structural diversity, and terrestrial primary productivity. The study site is a 6.75-km by 1.75-km portion of the Bitterroot River floodplain near Carlton, MT upon which 551 sample plots were delimited via segmentation classification. Biophysical complexity, characterized by topographic heterogeneity, structural heterogeneity, and hydrologic connectivity was represented in each sample plot by mean standard deviation ground height, vegetative structural diversity index, mean flow length, mean flow accumulation, mean percent inundation, and gamma index metrics computed from Light Detection and Ranging (LiDAR) data, HEC-RAS inundation modeling, and ArcGIS Arc Hydro derived metrics. Potential primary production was represented by Normalized Difference Vegetation Index (NDVI) values generated from aerial 4-band multispectral imagery. Two questions were addressed in the analyses: 1) What is the causal relationship among floodplain physical complexity, vegetation structural diversity, and terrestrial productivity, and 2) How does floodplain biophysical complexity influence terrestrial primary production. Through these efforts, my goal was to explain how the dynamic nature of riverscapes translates to fundamental measures of ecological form and function. NDVI values ranged from -0.27 to 0.43, and were robustly related to biophysical complexity in which the explanatory variables together accounted for 58% of variation in NDVI (p \u3c 0.001). In investigating the relationship between biophysical complexity, vegetation structural diversity, and NDVI, biophysical complexity was positively correlated to NDVI (r2= 0.25, p \u3c 0.001), and structural diversity was positively related to NDVI (r2= 0.51, p \u3c 0.001). These results suggest a causal relationship and support the complexity diversity hypothesis, and the diversity- productivity hypothesis. Structural diversity and connectivity variables accounted for the most explanatory power in all analyses, and overall results indicate that areas of the floodplain with greater biophysical complexity exhibited greater productivity

    A Comprehensive Review of YOLO: From YOLOv1 and Beyond

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    YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO's evolution, examining the innovations and contributions in each iteration from the original YOLO to YOLOv8 and YOLO-NAS. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO's development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.Comment: 31 pages, 15 figures, 4 tables, submitted to ACM Computing Surveys This version includes YOLO-NAS and a more detailed description of YOLOv5 and YOLOv8. It also adds three new diagrams for the architectures of YOLOv5, YOLOv8, and YOLO-NA

    Methodical basis for landscape structure analysis and monitoring: inclusion of ecotones and small landscape elements

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    Habitat variation is considered as an expression of biodiversity at landscape level in addition to genetic variation and species variation. Thus, effective methods for measuring habitat pattern at landscape level can be used to evaluate the status of biological conservation. However, the commonly used model (i.e. patch-corridor-matrix) for spatial pattern analysis has deficiencies. This model assumes discrete structures within the landscape without explicit consideration of “transitional zones” or “gradients” between patches. The transitional zones, often called “ecotones”, are dynamic and have a profound influence on adjacent ecosystems. Besides, this model takes landscape as a flat surface without consideration of the third spatial dimension (elevation). This will underestimate the patches’ size and perimeter as well as distances between patches especially in mountainous regions. Thus, the mosaic model needs to be adapted for more realistic and more precise representation of habitat pattern regarding to biodiversity assessment. Another part of information that has often been ignored is “small biotopes” inside patches (e.g. hedgerows, tree rows, copse, and scattered trees), which leads to within-patch heterogeneity being underestimated. The present work originates from the integration of the third spatial dimension in land-cover classification and landscape structure analysis. From the aspect of data processing, an integrated approach of Object-Based Image Analysis (OBIA) and Pixel-Based Image Analysis (PBIA) is developed and applied on multi-source data set (RapidEye images and Lidar data). At first, a general OBIA procedure is developed according to spectral object features based on RapidEye images for producing land-cover maps. Then, based on the classified maps, pixel-based algorithms are designed for detection of the small biotopes and ecotones using a Normalized Digital Surface Model (NDSM) which is derived from Lidar data. For describing habitat pattern under three-dimensional condition, several 3D-metrics (measuring e.g. landscape diversity, fragmentation/connectivity, and contrast) are proposed with spatial consideration of the ecological functions of small biotopes and ecotones. The proposed methodology is applied in two real-world examples in Germany and China. The results are twofold. First, it shows that the integrated approach of object-based and pixel-based image processing is effective for land-cover classification on different spatial scales. The overall classification accuracies of the main land-cover maps are 92 % in the German test site and 87 % in the Chinese test site. The developed Red Edge Vegetation Index (REVI) which is calculated from RapidEye images has been proved more efficient than the traditionally used Normalized Differenced Vegetation Index (NDVI) for vegetation classification, especially for the extraction of the forest mask. Using NDSM data, the third dimension is helpful for the identification of small biotopes and height gradient on forest boundary. The pixel-based algorithm so-called “buffering and shrinking” is developed for the detection of tree rows and ecotones on forest/field boundary. As a result the accuracy of detecting small biotopes is 80 % and four different types of ecotones are detected in the test site. Second, applications of 3D-metrics in two varied test sites show the frequently-used landscape diversity indices (i.e. Shannon’s diversity (SHDI) and Simpson’s diversity (SIDI)) are not sufficient for describing the habitats diversity, as they quantify only the habitats composition without consideration on habitats spatial distribution. The modified 3D-version of Effective Mesh Size (MESH) that takes ecotones into account leads to a realistic quantification of habitat fragmentation. In addition, two elevation-based contrast indices (i.e. Area-Weighted Edge Contrast (AWEC) and Total Edge Contrast Index (TECI)) are used as supplement to fragmentation metrics. Both ecotones and small biotopes are incorporated into the contrast metrics to take into account their edge effect in habitat pattern. This can be considered as a further step after fragmentation analysis with additional consideration of the edge permeability in the landscape structure analysis. Furthermore, a vector-based algorithm called “multi-buffer” approach is suggested for analyzing ecological networks based on land-cover maps. It considers small biotopes as stepping stones to establish connections between patches. Then, corresponding metrics (e.g. Effective Connected Mesh Size (ECMS)) are proposed based on the ecological networks. The network analysis shows the response of habitat connectivity to different dispersal distances in a simple way. Those connections through stepping stones act as ecological indicators of the “health” of the system, indicating the interpatch communications among habitats. In summary, it can be stated that habitat diversity is an essential level of biodiversity and methods for quantifying habitat pattern need to be improved and adapted to meet the demands for landscape monitoring and biodiversity conservation. The approaches presented in this work serve as possible methodical solution for fine-scale landscape structure analysis and function as “stepping stones” for further methodical developments to gain more insights into the habitat pattern.Die Lebensraumvielfalt ist neben der genetischen Vielfalt und der Artenvielfalt eine wesentliche Ebene der Biodiversität. Da diese Ebenen miteinander verknüpft sind, können Methoden zur Messung der Muster von Lebensräumen auf Landschaftsebene erfolgreich angewandt werden, um den Zustand der Biodiversität zu bewerten. Das zur räumlichen Musteranalyse auf Landschaftsebene häufig verwendete Patch-Korridor-Matrix-Modell weist allerdings einige Defizite auf. Dieses Modell geht von diskreten Strukturen in der Landschaft aus, ohne explizite Berücksichtigung von „Übergangszonen“ oder „Gradienten“ zwischen den einzelnen Landschaftselementen („Patches“). Diese Übergangszonen, welche auch als „Ökotone“ bezeichnet werden, sind dynamisch und haben einen starken Einfluss auf benachbarte Ökosysteme. Außerdem wird die Landschaft in diesem Modell als ebene Fläche ohne Berücksichtigung der dritten räumlichen Dimension (Höhe) betrachtet. Das führt dazu, dass die Flächengrößen und Umfänge der Patches sowie Distanzen zwischen den Patches besonders in reliefreichen Regionen unterschätzt werden. Daher muss das Patch-Korridor-Matrix-Modell für eine realistische und präzise Darstellung der Lebensraummuster für die Bewertung der biologischen Vielfalt angepasst werden. Ein weiterer Teil der Informationen, die häufig in Untersuchungen ignoriert werden, sind „Kleinbiotope“ innerhalb größerer Patches (z. B. Feldhecken, Baumreihen, Feldgehölze oder Einzelbäume). Dadurch wird die Heterogenität innerhalb von Patches unterschätzt. Die vorliegende Arbeit basiert auf der Integration der dritten räumlichen Dimension in die Landbedeckungsklassifikation und die Landschaftsstrukturanalyse. Mit Methoden der räumlichen Datenverarbeitung wurde ein integrierter Ansatz von objektbasierter Bildanalyse (OBIA) und pixelbasierter Bildanalyse (PBIA) entwickelt und auf einen Datensatz aus verschiedenen Quellen (RapidEye-Satellitenbilder und Lidar-Daten) angewendet. Dazu wird zunächst ein OBIA-Verfahren für die Ableitung von Hauptlandbedeckungsklassen entsprechend spektraler Objekteigenschaften basierend auf RapidEye-Bilddaten angewandt. Anschließend wurde basierend auf den klassifizierten Karten, ein pixelbasierter Algorithmus für die Erkennung von kleinen Biotopen und Ökotonen mit Hilfe eines normalisierten digitalen Oberflächenmodells (NDSM), welches das aus LIDAR-Daten abgeleitet wurde, entwickelt. Zur Beschreibung der dreidimensionalen Charakteristika der Lebensraummuster unter der räumlichen Betrachtung der ökologischen Funktionen von kleinen Biotopen und Ökotonen, werden mehrere 3D-Maße (z. B. Maße zur landschaftlichen Vielfalt, zur Fragmentierung bzw. Konnektivität und zum Kontrast) vorgeschlagen. Die vorgeschlagene Methodik wird an zwei realen Beispielen in Deutschland und China angewandt. Die Ergebnisse zeigen zweierlei. Erstens zeigt es sich, dass der integrierte Ansatz der objektbasierten und pixelbasierten Bildverarbeitung effektiv für die Landbedeckungsklassifikation auf unterschiedlichen räumlichen Skalen ist. Die Klassifikationsgüte insgesamt für die Hauptlandbedeckungstypen beträgt 92 % im deutschen und 87 % im chinesischen Testgebiet. Der eigens entwickelte Red Edge-Vegetationsindex (REVI), der sich aus RapidEye-Bilddaten berechnen lässt, erwies sich für die Vegetationsklassifizierung als effizienter verglichen mit dem traditionell verwendeten Normalized Differenced Vegetation Index (NDVI), insbesondere für die Gewinnung der Waldmaske. Im Rahmen der Verwendung von NDSM-Daten erwies sich die dritte Dimension als hilfreich für die Identifizierung von kleinen Biotopen und dem Höhengradienten, beispielsweise an der Wald/Feld-Grenze. Für den Nachweis von Baumreihen und Ökotonen an der Wald/Feld-Grenze wurde der sogenannte pixelbasierte Algorithmus „Pufferung und Schrumpfung“ entwickelt. Im Ergebnis konnten kleine Biotope mit einer Genauigkeit von 80 % und vier verschiedene Ökotontypen im Testgebiet detektiert werden. Zweitens zeigen die Ergebnisse der Anwendung der 3D-Maße in den zwei unterschiedlichen Testgebieten, dass die häufig genutzten Landschaftsstrukturmaße Shannon-Diversität (SHDI) und Simpson-Diversität (SIDI) nicht ausreichend für die Beschreibung der Lebensraumvielfalt sind. Sie quantifizieren lediglich die Zusammensetzung der Lebensräume, ohne Berücksichtigung der räumlichen Verteilung und Anordnung. Eine modifizierte 3D-Version der Effektiven Maschenweite (MESH), welche die Ökotone integriert, führt zu einer realistischen Quantifizierung der Fragmentierung von Lebensräumen. Darüber hinaus wurden zwei höhenbasierte Kontrastindizes, der flächengewichtete Kantenkontrast (AWEC) und der Gesamt-Kantenkontrast Index (TECI), als Ergänzung der Fragmentierungsmaße entwickelt. Sowohl Ökotone als auch Kleinbiotope wurden in den Berechnungen der Kontrastmaße integriert, um deren Randeffekte im Lebensraummuster zu berücksichtigen. Damit kann als ein weiterer Schritt nach der Fragmentierungsanalyse die Randdurchlässigkeit zusätzlich in die Landschaftsstrukturanalyse einbezogen werden. Außerdem wird ein vektorbasierter Algorithmus namens „Multi-Puffer“-Ansatz für die Analyse von ökologischen Netzwerken auf Basis von Landbedeckungskarten vorgeschlagen. Er berücksichtigt Kleinbiotope als Trittsteine, um Verbindungen zwischen Patches herzustellen. Weiterhin werden entsprechende Maße, z. B. die Effective Connected Mesh Size (ECMS), für die Analyse der ökologischen Netzwerke vorgeschlagen. Diese zeigen die Auswirkungen unterschiedlicher angenommener Ausbreitungsdistanzen von Organismen bei der Ableitung von Biotopverbundnetzen in einfacher Weise. Diese Verbindungen zwischen Lebensräumen über Trittsteine hinweg dienen als ökologische Indikatoren für den „gesunden Zustand“ des Systems und zeigen die gegenseitigen Verbindungen zwischen den Lebensräumen. Zusammenfassend kann gesagt werden, dass die Vielfalt der Lebensräume eine wesentliche Ebene der Biodiversität ist. Die Methoden zur Quantifizierung der Lebensraummuster müssen verbessert und angepasst werden, um den Anforderungen an ein Landschaftsmonitoring und die Erhaltung der biologischen Vielfalt gerecht zu werden. Die in dieser Arbeit vorgestellten Ansätze dienen als mögliche methodische Lösung für eine feinteilige Landschaftsstrukturanalyse und fungieren als ein „Trittsteine” auf dem Weg zu weiteren methodischen Entwicklungen für einen tieferen Einblick in die Muster von Lebensräumen

    Real-time synthetic primate vision

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    Using vector building maps to aid in generating seams for low-attitude aerial orthoimage mosaicking: Advantages in avoiding the crossing of buildings

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    A novel seam detection approach based on vector building maps is presented for low-attitude aerial orthoimage mosaicking. The approach tracks the centerlines between vector buildings to generate the candidate seams that avoid crossing buildings existing in maps. The candidate seams are then refined by considering their surrounding pixels to minimize the visual transition between the images to be mosaicked. After the refinement of the candidate seams, the final seams further bypass most of the buildings that are not updated into vector maps. Finally, three groups of aerial imagery from different urban densities are employed to test the proposed approach. The experimental results illustrate the advantages of the proposed approach in avoiding the crossing of buildings. The computational efficiency of the proposed approach is also significantly higher than that of Dijkstra’s algorithm
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