Relevant bibliographies by topics / Multi-temporal Analysi

Academic literature on the topic 'Multi-temporal Analysi'

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Published: 11 March 2023

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Journal articles on the topic "Multi-temporal Analysi"

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Ronchi, Bruno, Andrea Amici, Carlo Maía Rossi, Riccardo Primi, Rita Biasi, and María Nicolina Ripa. "Multi-temporal analysis of urban and periurban land use changes in medieval towns of central Italy." Hábitat y Sociedad, no. 7 (2014): 77–92. http://dx.doi.org/10.12795/habitatysociedad.2013.i6.05.

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García Culqui, Cristina Lourdes, and Michael Xavier Hachi Pazmiño. "Análisis multitemporal de la dinámica de uso de suelo y cobertura vegetal en la microcuenca del Río Illangama." Revista de Investigación Talentos 9, no. 2 (July 1, 2022): 101–16. http://dx.doi.org/10.33789/talentos.9.2.173.

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Esta investigación tuvo como objetivo realizar un análisis multitemporal de la dinámica de uso de suelo y cobertura vegetal de la microcuenca del Río Illangama durante el período 1996 - 2021, su incidencia social y ambiental, para el desarrollo sostenible de la misma. La metodología utilizada fue no experimental, tuvo un enfoque de estudio cualitativo con alcance descriptivo. Se realizó la clasificación supervisada de imágenes satelitales, con seis clases. Se empleó el Índice Diferencial Normalizado de Vegetación que evidenciando que en el año 1996 el valor bajo fue de -0,41 y el valor alto a 0,48, mientras que, en el año 2021 el valor bajo fue de -0,7 y el valor alto de 0,54. El área poblada en el 2021 en la microcuenca del Río Illangama disminuyó. Se determinó que, del año 1996 al 2021 ha crecido la zona de páramo sobre la cobertura denominada área sin cobertura vegetal en la zona alta de la microcuenca. Durante el lapso de 25 años se ha perdido en la microcuenca el tipo de vegetación densa y poco densa, sin embargo, han aumentado los reservorios de agua. La población principalmente se dedicaba a la agricultura, expandiendo sus cultivos y viviendas hacia la cuenca alta, no obstante, debido a mayores estrategias de conservación tanto de páramos como de bosques, se han ido controlando estas actividades.
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Zhao, Ling, Hanhan Deng, Linyao Qiu, Sumin Li, Zhixiang Hou, Hai Sun, and Yun Chen. "Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding." Symmetry 12, no. 2 (February 1, 2020): 199. http://dx.doi.org/10.3390/sym12020199.

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Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.
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Meixner, P., and M. Eckstein. "MULTI-TEMPORAL ANALYSIS OF WWII RECONNAISSANCE PHOTOS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 973–78. http://dx.doi.org/10.5194/isprs-archives-xli-b8-973-2016.

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There are millions of aerial photographs from the period of the Second Wold War available in the Allied archives, obtained by aerial photo reconnaissance, covering most of today’s European countries. They are spanning the time from 1938 until the end of the war and even beyond. Photo reconnaissance provided intelligence information for the Allied headquarters and accompanied the bombing offensive against the German homeland and the occupied territories. <br><br> One of the initial principal targets in Bohemia were the synthetized fuel works STW AG (Sudetenländische Treibstoffwerke AG) in Zaluzi (formerly Maltheuren) near Most (formerly Brück), Czech Republic. The STW AG synthetized fuel plant was not only subject to bombing raids, but a subject to quite intensive photo reconnaissance, too - long before the start of the bombing campaign. With a multi-temporal analysis of the available imagery from international archives we will demonstrate the factory build-up during 1942 and 1943, the effects of the bombing raids in 1944 and the struggle to keep the plant working in the last year of the war. Furthermore we would like to show the impact the bombings have today, in form of potential unexploded ordnance in the adjacent area of the open cast mines.
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Meixner, P., and M. Eckstein. "MULTI-TEMPORAL ANALYSIS OF WWII RECONNAISSANCE PHOTOS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 973–78. http://dx.doi.org/10.5194/isprsarchives-xli-b8-973-2016.

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There are millions of aerial photographs from the period of the Second Wold War available in the Allied archives, obtained by aerial photo reconnaissance, covering most of today’s European countries. They are spanning the time from 1938 until the end of the war and even beyond. Photo reconnaissance provided intelligence information for the Allied headquarters and accompanied the bombing offensive against the German homeland and the occupied territories. &lt;br&gt;&lt;br&gt; One of the initial principal targets in Bohemia were the synthetized fuel works STW AG (Sudetenländische Treibstoffwerke AG) in Zaluzi (formerly Maltheuren) near Most (formerly Brück), Czech Republic. The STW AG synthetized fuel plant was not only subject to bombing raids, but a subject to quite intensive photo reconnaissance, too - long before the start of the bombing campaign. With a multi-temporal analysis of the available imagery from international archives we will demonstrate the factory build-up during 1942 and 1943, the effects of the bombing raids in 1944 and the struggle to keep the plant working in the last year of the war. Furthermore we would like to show the impact the bombings have today, in form of potential unexploded ordnance in the adjacent area of the open cast mines.
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Rybakov, V. V. "Multi-Agent Temporal Nontransitive Linear Logics and the Admissibility Problem." Algebra and Logic 59, no. 1 (March 2020): 87–100. http://dx.doi.org/10.1007/s10469-020-09581-0.

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KIRIMOTO, Kensuke, and Shigeru NISHIO. "Multi-Dimensional Analysis of Spatio-Temporal Correlation Information." Transaction of the Visualization Society of Japan 29, no. 11 (2009): 67–75. http://dx.doi.org/10.3154/tvsj.29.67.

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Nocerino, E., F. Menna, and F. Menna. "MULTI-TEMPORAL ANALYSIS OF LANDSCAPES AND URBAN AREAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B4 (July 27, 2012): 85–90. http://dx.doi.org/10.5194/isprsarchives-xxxix-b4-85-2012.

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Alessandretti, Laura, Piotr Sapiezynski, Sune Lehmann, and Andrea Baronchelli. "Multi-scale spatio-temporal analysis of human mobility." PLOS ONE 12, no. 2 (February 15, 2017): e0171686. http://dx.doi.org/10.1371/journal.pone.0171686.

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Lin, Yi-Chun, Jinyuan Shao, Sang-Yeop Shin, Zainab Saka, Mina Joseph, Raja Manish, Songlin Fei, and Ayman Habib. "Comparative Analysis of Multi-Platform, Multi-Resolution, Multi-Temporal LiDAR Data for Forest Inventory." Remote Sensing 14, no. 3 (January 29, 2022): 649. http://dx.doi.org/10.3390/rs14030649.

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LiDAR technology is rapidly evolving as various new systems emerge, providing unprecedented data to characterize forest vertical structure. Data from different LiDAR systems present distinct characteristics owing to a combined effect of sensor specifications, data acquisition strategies, as well as forest conditions such as tree density and canopy cover. Comparative analysis of multi-platform, multi-resolution, and multi-temporal LiDAR data provides guidelines for selecting appropriate LiDAR systems and data processing tools for different research questions, and thus is of crucial importance. This study presents a comprehensive comparison of point clouds from four systems, linear and Geiger-mode LiDAR from manned aircraft and multi-beam LiDAR on unmanned aerial vehicle (UAV), and in-house developed Backpack, with the consideration of different forest canopy cover scenarios. The results suggest that the proximal Backpack LiDAR can provide the finest level of information, followed by UAV LiDAR, Geiger-mode LiDAR, and linear LiDAR. The emerging Geiger-mode LiDAR can capture a significantly higher level of detail while operating at a higher altitude as compared to the traditional linear LiDAR. The results also show: (1) canopy cover percentage has a critical impact on the ability of aerial and terrestrial systems to acquire information corresponding to the lower and upper portions of the tree canopy, respectively; (2) all the systems can obtain adequate ground points for digital terrain model generation irrespective of canopy cover conditions; and (3) point clouds from different systems are in agreement within a ±3 cm and ±7 cm range along the vertical and planimetric directions, respectively.
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Dissertations / Theses on the topic "Multi-temporal Analysi"

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Saha, Sudipan. "Advanced deep learning based multi-temporal remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/263814.

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Multi-temporal image analysis has been widely used in many applications such as urban monitoring, disaster management, and agriculture. With the development of the remote sensing technology, the new generation remote sensing satellite images with High/ Very High spatial resolution (HR/VHR) are now available. Compared to the traditional low/medium spatial resolution images, the detailed information of ground objects can be clearly analyzed in the HR/VHR images. Classical methods of multi-temporal image analysis deal with the images at pixel level and have worked well on low/medium resolution images. However, they provide sub-optimal results on new generation images due to their limited capability of modeling complex spatial and spectral information in the new generation products. Although significant number of object-based methods have been proposed in the last decade, they depend on suitable segmentation scale for diverse kinds of objects present in each temporal image. Thus their capability to express contextual information is limited. Typical spatial properties of last generation images emphasize the need of having more flexible models for object representation. Another drawback of the traditional methods is the difficulty in transferring knowledge learned from one specific problem to another. In the last few years, an interesting development is observed in the machine learning/computer vision field. Deep learning, especially Convolution Neural Networks (CNNs) have shown excellent capability to capture object level information and in transfer learning. By 2015, deep learning achieved state-of-the-art performance in most computer vision tasks. Inspite of its success in computer vision fields, the application of deep learning in multi-temporal image analysis saw slow progress due to the requirement of large labeled datasets to train deep learning models. However, by the start of this PhD activity, few works in the computer vision literature showed that deep learning possesses capability of transfer learning and training without labeled data. Thus, inspired by the success of deep learning, this thesis focuses on developing deep learning based methods for unsupervised/semi-supervised multi-temporal image analysis. This thesis is aimed towards developing methods that combine the benefits of deep learning with the traditional methods of multi-temporal image analysis. Towards this direction, the thesis first explores the research challenges that incorporates deep learning into the popular unsupervised change detection (CD) method - Change Vector Analysis (CVA) and further investigates the possibility of using deep learning for multi-temporal information extraction. The thesis specifically: i) extends the paradigm of unsupervised CVA to novel Deep CVA (DCVA) by using a pre-trained network as deep feature extractor; ii) extends DCVA by exploiting Generative Adversarial Network (GAN) to remove necessity of having a pre-trained deep network; iii) revisits the problem of semi-supervised CD by exploiting Graph Convolutional Network (GCN) for label propagation from the labeled pixels to the unlabeled ones; and iv) extends the problem statement of semantic segmentation to multi-temporal domain via unsupervised deep clustering. The effectiveness of the proposed novel approaches and related techniques is demonstrated on several experiments involving passive VHR (including Pleiades), passive HR (Sentinel-2), and active VHR (COSMO-SkyMed) datasets. A substantial improvement is observed over the state-of-the-art shallow methods.
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Martorell, Domínguez Agustín. "Modelling tonal context dynamics by temporal multi-scale analysis." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123810.

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This work explores the multidimensional, ambiguous and temporal characteristics of tonality. The approach relies on interfacing pitch-spaces with time vs. time-scale descriptions. In this combined representation, the spatial and temporal hierarchies of tonality are evidenced simultaneously and in relation to each other. A visual exploration method is proposed for the analysis of tonal context in music works, using a simple model of tonal induction. The method is extended for the analysis of music based on tonal systems beyond the major-minor paradigm. Two perceptual studies are approached from this descriptive framework. The first study evidences the impact of time-scale in a simple mode of tonal induction, and analyses the mathematical artefacts introduced by evaluations in scaled spaces. In the second study, a model of contextual instability is proposed and discussed in relation to the modelling of tonal tension. The analysis and representation methods are then generalised, through a set-class theoretical domain.
Esta tesis analiza la naturaleza ambigua, multidimensional y temporal de la tonalidad. El método propuesto parte de la conexión entre espacios tonales y descripciones en tiempo y escala temporal. Esta representación conjunta pone de manifiesto la relación entre las jerarquías espaciales y temporales de la tonalidad. Utilizando un modelo simple de inducción tonal, se propone un método de exploración visual del contexto tonal en obras musicales. Dos estudios perceptuales son abordados desde el entorno descriptivo propuesto. En el primer estudio, se pone en evidencia el impacto de la escala temporal como parámetro de un modelo simple de inducción tonal, y se analizan los artificios matemáticos introducidos por evaluaciones en espacios escalados dimensionalmente. En el segundo estudio se propone un modelo de inestabilidad contextual, y se analiza en relación al modelado de la tensión tonal. El método de análisis se generaliza, a través de una categorización contextual en set-classes.
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D'AMATO, VINCENZO STEFANO. "Deep Multi Temporal Scale Networks for Human Motion Analysis." Doctoral thesis, Università degli studi di Genova, 2023. https://hdl.handle.net/11567/1104759.

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The movement of human beings appears to respond to a complex motor system that contains signals at different hierarchical levels. For example, an action such as ``grasping a glass on a table'' represents a high-level action, but to perform this task, the body needs several motor inputs that include the activation of different joints of the body (shoulder, arm, hand, fingers, etc.). Each of these different joints/muscles have a different size, responsiveness, and precision with a complex non-linearly stratified temporal dimension where every muscle has its temporal scale. Parts such as the fingers responds much faster to brain input than more voluminous body parts such as the shoulder. The cooperation we have when we perform an action produces smooth, effective, and expressive movement in a complex multiple temporal scale cognitive task. Following this layered structure, the human body can be described as a kinematic tree, consisting of joints connected. Although it is nowadays well known that human movement and its perception are characterised by multiple temporal scales, very few works in the literature are focused on studying this particular property. In this thesis, we will focus on the analysis of human movement using data-driven techniques. In particular, we will focus on the non-verbal aspects of human movement, with an emphasis on full-body movements. The data-driven methods can interpret the information in the data by searching for rules, associations or patterns that can represent the relationships between input (e.g. the human action acquired with sensors) and output (e.g. the type of action performed). Furthermore, these models may represent a new research frontier as they can analyse large masses of data and focus on aspects that even an expert user might miss. The literature on data-driven models proposes two families of methods that can process time series and human movement. The first family, called shallow models, extract features from the time series that can help the learning algorithm find associations in the data. These features are identified and designed by domain experts who can identify the best ones for the problem faced. On the other hand, the second family avoids this phase of extraction by the human expert since the models themselves can identify the best set of features to optimise the learning of the model. In this thesis, we will provide a method that can apply the multi-temporal scales property of the human motion domain to deep learning models, the only data-driven models that can be extended to handle this property. We will ask ourselves two questions: what happens if we apply knowledge about how human movements are performed to deep learning models? Can this knowledge improve current automatic recognition standards? In order to prove the validity of our study, we collected data and tested our hypothesis in specially designed experiments. Results support both the proposal and the need for the use of deep multi-scale models as a tool to better understand human movement and its multiple time-scale nature.
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Saha, Sudipan. "Advanced deep learning based multi-temporal remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/263814.

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Multi-temporal image analysis has been widely used in many applications such as urban monitoring, disaster management, and agriculture. With the development of the remote sensing technology, the new generation remote sensing satellite images with High/ Very High spatial resolution (HR/VHR) are now available. Compared to the traditional low/medium spatial resolution images, the detailed information of ground objects can be clearly analyzed in the HR/VHR images. Classical methods of multi-temporal image analysis deal with the images at pixel level and have worked well on low/medium resolution images. However, they provide sub-optimal results on new generation images due to their limited capability of modeling complex spatial and spectral information in the new generation products. Although significant number of object-based methods have been proposed in the last decade, they depend on suitable segmentation scale for diverse kinds of objects present in each temporal image. Thus their capability to express contextual information is limited. Typical spatial properties of last generation images emphasize the need of having more flexible models for object representation. Another drawback of the traditional methods is the difficulty in transferring knowledge learned from one specific problem to another. In the last few years, an interesting development is observed in the machine learning/computer vision field. Deep learning, especially Convolution Neural Networks (CNNs) have shown excellent capability to capture object level information and in transfer learning. By 2015, deep learning achieved state-of-the-art performance in most computer vision tasks. Inspite of its success in computer vision fields, the application of deep learning in multi-temporal image analysis saw slow progress due to the requirement of large labeled datasets to train deep learning models. However, by the start of this PhD activity, few works in the computer vision literature showed that deep learning possesses capability of transfer learning and training without labeled data. Thus, inspired by the success of deep learning, this thesis focuses on developing deep learning based methods for unsupervised/semi-supervised multi-temporal image analysis. This thesis is aimed towards developing methods that combine the benefits of deep learning with the traditional methods of multi-temporal image analysis. Towards this direction, the thesis first explores the research challenges that incorporates deep learning into the popular unsupervised change detection (CD) method - Change Vector Analysis (CVA) and further investigates the possibility of using deep learning for multi-temporal information extraction. The thesis specifically: i) extends the paradigm of unsupervised CVA to novel Deep CVA (DCVA) by using a pre-trained network as deep feature extractor; ii) extends DCVA by exploiting Generative Adversarial Network (GAN) to remove necessity of having a pre-trained deep network; iii) revisits the problem of semi-supervised CD by exploiting Graph Convolutional Network (GCN) for label propagation from the labeled pixels to the unlabeled ones; and iv) extends the problem statement of semantic segmentation to multi-temporal domain via unsupervised deep clustering. The effectiveness of the proposed novel approaches and related techniques is demonstrated on several experiments involving passive VHR (including Pleiades), passive HR (Sentinel-2), and active VHR (COSMO-SkyMed) datasets. A substantial improvement is observed over the state-of-the-art shallow methods.
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Boynton, Ansel John. "EARLY WILDFIRE DETECTION USING TEMPORAL FILTERING AND MULTI-BAND INFRARED ANALYSIS." DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/1048.

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Every year wildfires threaten or destroy ecological habitats, man-made infrastructure and people’s lives. Additionally millions of dollars are spent each year trying to prevent and control these fires. Ideally if a wildfire can be detected before it rages out of control it can be extinguished and avoid large scale devastation. Traditional manned fire lookout towers are neither cost effective nor particularly efficient at detecting wildfire. It is proposed that temporal filtering can be used to isolate the signals created at the beginnings of potential wildfires. Temporal filtering can remove any background image and any periodic signals created by the camera movement. Once typical signals are analyzed, digital filters can be designed to pass fire signals while blocking the unwanted signals. The temporal filter passes only fire signals and signals generated by moving objects. These objects can be distinguished from each other by analyzing the objects mid and long wave energy profile. This algorithm is tested on 17 data sources and its results analyzed.
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Pantaleoni, Eva. "Assessing Coastal Plain Wetland Composition using Advanced Spaceborne Thermal Emission and Reflection Radiometer Imagery." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28419.

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Establishing wetland gains and losses, delineating wetland boundaries, and determining their vegetative composition are major challenges that can be improved through remote sensing studies. In this study, we used the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to separate wetlands from uplands in a study of 870 locations on the Virginia Coastal Plain. We used the first five bands from each of two ASTER scenes (6 March 2005 and 16 October 2005), covering the visible to the short-wave infrared region (0.52-2.185υm). We included GIS data layers for soil survey, topography, and presence or absence of water in a logistic regression model that predicted the location of over 78% of the wetlands. While this was slightly less accurate (78% vs. 86%) than current National Wetland Inventory (NWI) aerial photo interpretation procedures of locating wetlands, satellite imagery analysis holds great promise for speeding wetland mapping, lowering costs, and improving update frequency. To estimate wetland vegetation composition classs of the study locations, we generated a Classification and Regression Tree (CART) model and a Multinomial Logistic Regression (logit) model, and compared their accuracy in separating woody wetlands, emergent wetlands and open water. The overall accuracy of the CART model was 73.3%, while the overall accuracy of the logit model was 76.7%. Although the CART producer's accuracy (correct category classification) of the emergent wetlands was higher than the accuracy from the multinomial logit (57.1% vs. 40.7%), we obtained the opposite result for the woody wetland category (68.7% vs. 52.6%). A McNemar test between the two models and NWI maps showed that their accuracies were not statistically different. We conducted a sub-pixel analysis of the ASTER images to establish canopy cover of forested wetlands. The canopy cover ranged from 0 to 225 m2. We used visble-near-infrared ASTER bands, Delta Normalized Difference Vegetation Index, and a Tasselled Cap transformation in an ordinary linear regression (OLS) model. The model achieved an adjusted-R2 of 0.69 and an RMSE of 2.73% when the canopy cover is less than 16%. For higher canopy cover values, the adjusted-R2 was 0.4 and the RMSE was19.79%. Taken together, these findings suggest that satellite remote sensing, in concert with other spatial data, has strong potential for mapping both wetland presence and type.
Ph. D.
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Yamaguchi, Yasushi, and Megumi Maruyama. "ANALYSIS OF DEFORESTATION IN MATO GROSSO USING MULTI-TEMPORAL LANDSAT TM IMAGERIES." IEEE, 2010. http://hdl.handle.net/2237/14448.

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Orstavik, Odd-Halvdan Sakse. "Analysis of chaotic multi-variate time-series from spatio-temporal dynamical systems." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314071.

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MARZI, DAVID. "Analysis of multi-temporal spaceborne Earth observation data to map selected land cover classes." Doctoral thesis, Università degli studi di Pavia, 2023. https://hdl.handle.net/11571/1470898.

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Oggigiorno, per poter affrontare efficacemente i problemi ambientali su larga scala, la necessità di disporre di mappe di copertura del suolo affidabili e ad alta risoluzione spaziale e temporale è più che mai urgente. Infatti, numerosi contesti potrebbero trarre beneficio da tali prodotti come, ad esempio, il cambiamento climatico, la desertificazione, l'inverdimento dell'artico, la deforestazione, l'urbanizzazione, l'erosione del suolo, il monitoraggio delle foreste, la conservazione della biodiversità, la gestione delle aree urbane, la gestione delle risorse idriche, l'agricoltura, la sicurezza alimentare e molti altri. Siccome le variabili di interesse tendono a cambiare molto rapidamente nel tempo e nello spazio, la disponibilità di mappe di copertura del suolo frequenti e di buona qualità suscita un grande interesse. Negli ultimi anni sono state prodotte diverse mappe tematiche e di copertura del suolo su scala regionale/globale le quali, tuttavia, spesso non soddisfano i requisiti imposti dalle applicazioni; ciò è dovuto principalmente al fatto che i prodotti esistenti sono stati generati da diversi sensori satellitari (ottici, radar o entrambi), diverse strategie di campionamento, diverse legende, diversi protocolli di validazione, ecc. Inoltre, la risoluzione spaziale e/o temporale di tali prodotti è spesso insufficiente per diverse applicazioni. In questo lavoro di tesi è stato studiato come sfruttare dati multitemporali di tipo ottico e SAR (Synthetic Aperture Radar) per caratterizzare un insieme molto ristretto di classi, piuttosto che un'ampia gamma di tipi di copertura del suolo. Il lavoro presentato si concentra sulla vegetazione (tra cui specie arboree, praterie, arbusti ed altri), corpi idrici (tra cui laghi, mari, fiumi ed altri) e colture biologiche (in particolare, pratiche di agricoltura biologica). Per quanto riguarda la vegetazione, la letteratura scientifica offre numerose metodologie consolidate, finalizzate alla mappatura delle coperture vegetative. Al contrario, gli approcci che sfruttano i sensori SAR come principale fonte di dati sono decisamente più rari. Per questo motivo, parte di questa tesi è dedicata all'analisi della potenzialità dei dati SAR multitemporali nel caratterizzare diversi tipi di vegetazione naturale. Per quanto riguarda la mappatura dei corpi idrici, la letteratura tecnica fornisce diverse soluzioni basate sia su dati ottici che SAR. Tuttavia, la maggioranza delle metodologie analizzate presentano alcune limitazioni legate principalmente alla mancanza di automatismo degli algoritmi, l'impossibilità di utilizzare il modello in altre regioni di interesse, alla risoluzione spaziale relativamente bassa ed altri. Dal momento che la comunità sul cambiamento climatico necessita di informazioni tempestive relative allo stato dei corpi idrici a livello non solo locale/regionale ma anche globale, e che sia indipendente dalle condizioni meteorologiche delle diverse aree del mondo, in questa tesi si propone una metodologia volta a mappare i corpi idrici sfruttando sequenze temporali di dati SAR, che sia in grado di superare le limitazioni più gravi presenti negli approcci esistenti. Infine, per quanto concerne la caratterizzazione dei terreni agricoli biologici, occorre rilevare e monitorare diversi aspetti, tra cui le operazioni di diserbo, le attività di fertilizzazione e le tecniche di lavorazione del terreno. A tal fine, sia i dati ottici multitemporali che i dati SAR vengono sfruttati per costruire piccoli blocchi che faranno parte di un sistema di monitoraggio dell'agricoltura biologica più complesso, volto a migliorare la trasparenza e la tracciabilità all'interno della catena di approvvigionamento alimentare biologica. In generale, i risultati hanno dimostrato che le sequenze temporali di dati SAR e multispettrali possono essere impiegate con successo nella classificazione dei diversi tipi di copertura del suolo di cui sopra.
Nowadays, the need for reliable, timely, high-resolution land cover maps is more than urgent if large-scale environmental problems are to be tackled effectively. Many different contexts would in fact benefit from such products, such as climate change, desertification, arctic greening, deforestation, urbanization, soil erosion, forest monitoring, conservation of biodiversity, urban area management, water resources management, agriculture, food security and many others. Due to the fact that the involved variables tend to change very rapidly in time and space, the availability of frequent and good quality global land cover products raises great interest. Several regional/global thematic and land cover maps have been delivered and other are expected, but they often do not meet the specific requirements of various applications; this is mainly due to the fact that all the existing products have been generated from different satellite sensors (optical, radar or both), different sampling strategies, different types of mapped land cover types, different validation protocols, etc. Moreover, the spatial and/or temporal resolution of these products is often insufficient for some applications. In this thesis work, we investigated how to leverage multitemporal optical and SAR data to characterize a very small set of classes rather than a full range of land cover types. Our work focuses on vegetation (including tree species, grasslands, shrublands and others), water bodies (including lakes, seas, rivers and others) and organic croplands (specifically, organic farming practices). Regarding vegetation, the technical literature offers numerous well-established methodologies aimed at mapping vegetated land covers. On the contrary, approaches that use SAR sensors as the main source of data are definitely more scarce. For this reason, part of this thesis work will be devoted to analyze the potential of multitemporal SAR data to characterize several types of natural vegetation. Regarding mapping of water bodies, the scientific literature provides several solutions based on optical and SAR data. However, almost all the analyzed methodologies have some limitations, mainly related to lack of automatism, impossibility to use the proposed method in other regions of interest, relatively low spatial resolution and others. Given the climate change community's need for timely information on the status of water bodies at the global level regardless of weather conditions, in this thesis a methodology aimed at mapping water bodies using sequences of SAR data, that is able to overcome the most severe limitations of the existing methodologies, is proposed. Finally, to characterize organic farmland, several aspects must be detected and monitored, including weed-killer operations, fertilization activities and tillage techniques. To do so, both multitemporal optical and SAR data are exploited to build small detection blocks, that will be part of a more complex organic farming monitoring system aimed at improving transparency and traceability within the organic food supply chain. In general, results showed that SAR and multispectral time series can be successfully employed to classify these land cover types.
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10

Day, Ryan David. "A Framework for Simulating and Analyzing Multi-UAV Persistent Search and Retrieval with Stochastic Target Appearance." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/9222.

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In recent years, advances in small unmanned aerial vehicle (UAV) technology have transformed the use cases of these aircraft from hobby flying to industrial and business applications. These maneuverable, easily deployed tools can be retrofitted with a myriad of sensors and equipment, which make them suitable to perform a variety of specialized tasks. With increasing UAV capabilities, the function of small UAVs can be extended from pure monitoring or surveillance to the dual objective of monitoring an environment for events and addressing the events in some way. This thesis seeks to explore a subdomain of the dual objective problem described, referred to in this thesis as the multi-UAV persistent search and retrieval task with stochastic target appearance (PSR-STA), in which UAVs continuously search an area over a long period of time for targets of interest, which appear according to a probabilistic model, to retrieve and deliver them to a collector location. The advent of high-speed computers and agent-based modeling theory enable the simulation of multi-UAV PSR-STA. However, it can be complicated to combine parts of multi-UAV PSR-STA such as motion models and multi-UAV coordination into one integrated system, and even after they are combined successfully, it is difficult to analyze the system except with simple comparison tools. This thesis 1) proposes a framework that builds a foundation for understanding how to simulate and analyze multi-UAV PSR-STA through prescribing important design decisions and methods for simulation and 2) identifies metrics, analysis tools, and trends related to overall system effectiveness for multi-UAV PSR-STA. A case study of multi-UAV park cleanup is implemented where many simulations with input parameters chosen by a latin hypercube design of experiments are examined, algorithms for choosing the locations of collectors and charging stations based on probabilistic information are proposed, and the differences in effectiveness between four coverage search patterns are analyzed. Measures are highlighted that provide insight into performance variability over time and space. Line charts and the discrete Fourier transform are used to understand temporal patterns inherent in the data. Principal component analysis is used to analyze relevant spatial patterns in effectiveness, and a random forest surrogate model with a profiler is used to explore the non-linear influence of input parameters on the spatial patterns. The trellis chart or figure of figures method is presented for visualizing spatial and temporal data across many simulations. A second set of experiments based on the park cleanup case study are performed and examined to verify the benefits of these methods.
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Books on the topic "Multi-temporal Analysi"

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International Workshop on the Analysis of Multi-temporal Remote Sensing Images (2007 Leuven, Belgium). 2007 International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Leuven, Belgium, 18 - 20 July 2007. Piscataway, NJ: IEEE, 2007.

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International Workshop on the Analysis of Multi-temporal Remote Sensing Images (3rd 2005 Biloxi, Miss.). Proceedings of the Third International Workshop on the Analysis of Multi-temporal Remote Sensing Images: Multi Temp 2005, 16-18 May 2005, Beau Rivage Resort and Casino, Biloxi, Mississippi USA. Edited by King Roger L, Younan Nicolas H, and Institute of Electrical and Electronics Engineers. Piscataway, N.J: IEEE, 2005.

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Lorenzo, Bruzzone, and Smits Paul, eds. Proceedings of the First International Workshop on the Analysis of Multi-temporal Remote Sensing Images: University of Trento, Italy, 13-14 September 2001. River Edge, N.J: World Scientific, 2002.

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International, Workshop on the Analysis of Multi-Temporal Remote Sensing Images (2nd 2003 Ispra Italy). Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003. [River Edge] N.J: World Scientific, 2004.

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Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003. Singapore: World Scientific, 2005.

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US GOVERNMENT. Proceedings of the Third International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multi Temp 2005, 16-18 May 2005, Beau Rivage. Institute of Electrical & Electronics Enginee, 2005.

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(Editor), Lorenzo Bruzzone, and Paul C. Smits (Editor), eds. Analysis of Multi-Temporal Remote Sensing Images: Proceedings of Multitemp 2001 University of Trento, Italy 13-14 September 2001 (Remote Sensing). World Scientific Publishing Company, 2002.

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(Editor), Paul C. Smits, and Lorenzo Bruzzone (Editor), eds. Analysis Of Multi-Temporal Remote Sensing Images: Proceedings Of The Second International Workshop on the Joint Research Centre Ispra, Italy 16-18 July 2003. World Scientific Publishing Company, 2004.

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Barham, Jeremy, ed. Rethinking Mahler. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199316090.001.0001.

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Gustav Mahler’s anniversary years (2010–11) have provided an opportunity to rethink the composer’s position within the musical, cultural and multi-disciplinary landscapes of the twenty-first century, as well as to reassess his relationship with the historical traditions of his own time. Comprising a collection of essays by leading and emerging scholars in the field, Rethinking Mahler in part counterbalances common scholarly assumptions and preferences which predominantly configure Mahler as proto-modernist, with hitherto somewhat neglected consideration of his debt to, and his re-imagining of, the legacies of his own historical past. It reassesses his engagement both with the immediate creative and cultural present of the late nineteenth century, and with the weight of a creative and cultural past that was the inheritance of artists living and working at that time. From a variety of disciplinary perspectives the contributors pursue ideas of nostalgia, historicism and ‘pastness’ in relation to an emergent pluralist modernity and subsequent musical-cultural developments. Mahler’s relationship with music, media and ideas past, present, and future is explored in three themed sections, addressing among them issues in structural analysis; cultural contexts; aesthetics; reception; performance, genres of stage, screen and literature; history/historiography; and temporal experience.
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Book chapters on the topic "Multi-temporal Analysi"

1

Conrad, Christian, and Rudolf Mester. "Learning Multi-view Correspondences via Subspace-Based Temporal Coincidences." In Image Analysis, 456–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38886-6_43.

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Raszyk, Martin, David Basin, Srđan Krstić, and Dmitriy Traytel. "Multi-head Monitoring of Metric Temporal Logic." In Automated Technology for Verification and Analysis, 151–70. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31784-3_9.

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Ruiz, Gerardo, and Rosio Ruiz. "Multi-Temporal Analysis for Mexico City Aquifer." In GeoPlanet: Earth and Planetary Sciences, 365–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30209-1_26.

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Liutkus, Antoine, Umut Şimşekli, and A. Taylan Cemgil. "Extraction of Temporal Patterns in Multi-rate and Multi-modal Datasets." In Latent Variable Analysis and Signal Separation, 135–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22482-4_16.

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Piqueras-Salazar, Ignacio, and Pedro García-Sevilla. "Fusion of Multi-temporal and Multi-sensor Hyperspectral Data for Land-Use Classification." In Pattern Recognition and Image Analysis, 724–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_86.

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Arvind, C. S., Ashoka Vanjare, S. N. Omkar, J. Senthilnath, V. Mani, and P. G. Diwakar. "Multi-temporal Satellite Image Analysis Using Unsupervised Techniques." In Advances in Computing and Information Technology, 757–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31552-7_77.

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Mercier, Grégoire, and Florence Tupin. "Analysis of Multi-Temporal Series and Change Detection." In Remote Sensing Imagery, 203–21. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118899106.ch8.

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Hebel, Marcus, Michael Arens, and Uwe Stilla. "Change Detection in Urban Areas by Direct Comparison of Multi-view and Multi-temporal ALS Data." In Photogrammetric Image Analysis, 185–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24393-6_16.

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Ferretti, Roberta, and Silvana Dellepiane. "Color Spaces in Data Fusion of Multi-temporal Images." In Image Analysis and Processing — ICIAP 2015, 612–22. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_55.

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Senthilnath, J., S. N. Omkar, V. Mani, Ashoka Vanjare, and P. G. Diwakar. "Multi-Temporal Satellite Image Analysis Using Gene Expression Programming." In Advances in Intelligent Systems and Computing, 1039–45. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1602-5_109.

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Conference papers on the topic "Multi-temporal Analysi"

1

Ragnar Bang Huseby, L. Aurdal, L. Eikvil, R. Solberg, D. Vikhamar, and A. Solberg. "Alignment of growth seasons from satellite data." In International Workshop on the Analysis of Multi-Temporal Remote Sensing Images. IEEE, 2005. http://dx.doi.org/10.1109/amtrsi.2005.1469875.

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"Committees." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005029.

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"Author index." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005043.

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"Title page." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005027.

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"Copyright page." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005028.

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"Welcome message." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005030.

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"Table of contents." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005031.

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Fernandez-Prieto, D., and M. Marconcini. "A novel approach to targeted change detection." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005032.

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Resta, Salvatore, Nicola Acito, Marco Diani, Giovanni Corsini, Thomas Opsahl, and Trym Vegard Haavardsholm. "Detection of small changes in airborne hyperspectral imagery: Experimental results over urban areas." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005033.

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Bovolo, Francesca, Carlo Marin, and Lorenzo Bruzzone. "A multilevel approach to change detection for port surveillance with very high resolution SAR images." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005034.

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Reports on the topic "Multi-temporal Analysi"

1

Fraser, R., R. Fernandes, and R. Latifovic. Multi-temporal Burned area Mapping Using Logistic Regression Analysis and Change Metrics. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219870.

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Nitta, Katsumi. Development of Meta Level Communication Analysis using Temporal Data Crystallization and Its Application to Multi Modal Human Communication. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada587634.

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Marshall, Michael. Slope Failure Detection through Multi-temporal Lidar Data and Geotechnical Soils Analysis of the Deep-Seated Madrone Landslide, Coast Range, Oregon. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2652.

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Merritt, Elizabeth, Forrest Doss, Eric Loomis, Kirk Flippo, and John Kline. Examining the evolution towards turbulence through spatio-temporal analysis of multi-dimensional structures formed by instability growth along a counter propagating shear layer. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1148305.

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Ukkusuri, Satish, Lu Ling, Tho V. Le, and Wenbo Zhang. Performance of Right-Turn Lane Designs at Intersections. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317277.

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Right-turn lane (RTL) crashes are among the most key contributors to intersection crashes in the US. Different right turn lanes based on their design, traffic volume, and location have varying levels of crash risk. Therefore, engineers and researchers have been looking for alternative ways to improve the safety and operations for right-turn traffic. This study investigates the traffic safety performance of the RTL in Indiana state based on multi-sources, including official crash reports, official database, and field study. To understand the RTL crashes' influencing factors, we introduce a random effect negative binomial model and log-linear model to estimate the impact of influencing factors on the crash frequency and severity and adopt the robustness test to verify the reliability of estimations. In addition to the environmental factors, spatial and temporal factors, intersection, and RTL geometric factors, we propose build environment factors such as the RTL geometrics and intersection characteristics to address the endogeneity issues, which is rarely addressed in the accident-related research literature. Last, we develop a case study with the help of the Indiana Department of Transportation (INDOT). The empirical analyses indicate that RTL crash frequency and severity is mainly influenced by turn radius, traffic control, and other intersection related factors such as right-turn type and speed limit, channelized type, and AADT, acceleration lane and AADT. In particular, the effects of these factors are different among counties and right turn lane roadway types.
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