Relevant bibliographies by topics / Python and libraries of python (SciPy

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Python and libraries of python (SciPy'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Python and libraries of python (SciPy"

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Boulle, A., and J. Kieffer. "High-performance Python for crystallographic computing." Journal of Applied Crystallography 52, no. 4 (July 24, 2019): 882–97. http://dx.doi.org/10.1107/s1600576719008471.

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The Python programming language, combined with the numerical computing library NumPy and the scientific computing library SciPy, has become the de facto standard for scientific computing in a variety of fields. This popularity is mainly due to the ease with which a Python program can be written and executed (easy syntax, dynamical typing, no compilation etc.), coupled with the existence of a large number of specialized third-party libraries that aim to lift all the limitations of the raw Python language. NumPy introduces vector programming, improving execution speeds, whereas SciPy brings a wealth of highly optimized and reliable scientific functions. There are cases, however, where vector programming alone is not sufficient to reach optimal performance. This issue is addressed with dedicated compilers that aim to translate Python code into native and statically typed code with support for the multi-core architectures of modern processors. In the present article it is shown how these approaches can be efficiently used to tackle different problems, with increasing complexity, that are relevant to crystallography: the 2D Laue function, scattering from a strained 2D crystal, scattering from 3D nanocrystals and, finally, diffraction from films and multilayers. For each case, detailed implementations and explanations of the functioning of the algorithms are provided. Different Python compilers (namely NumExpr, Numba, Pythran and Cython) are used to improve performance and are benchmarked against state-of-the-art NumPy implementations. All examples are also provided as commented and didactic Python (Jupyter) notebooks that can be used as starting points for crystallographers curious to enter the Python ecosystem or wishing to accelerate their existing codes.
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Kumar, Rakesh. "FUTURE FOR SCIENTIFIC COMPUTING USING PYTHON." International Journal of Engineering Technologies and Management Research 2, no. 1 (January 29, 2020): 30–41. http://dx.doi.org/10.29121/ijetmr.v2.i1.2015.28.

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Computational science (scientific computing or scientific computation) is concerned with constructing mathematical models as well as quantitative analysis techniques and using computers to analyze as well as solve scientific problems. In practical use, it is basically the application of computer simulation as well as other forms of computation from numerical analysis and theoretical computer science to problems in different scientific disciplines. The scientific computing approach is to gain understanding, basically through the analysis of mathematical models implemented on computers. Python is frequently used for highperformance scientific applications and widely used in academia as well as scientific projects because it is easy to write and performs well. Due to its high performance nature, scientific computing in Python often utilizes external libraries like NumPy, SciPy and Matplotlib etc.
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Nunez-Iglesias, Juan, Adam J. Blanch, Oliver Looker, Matthew W. Dixon, and Leann Tilley. "A new Python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton." PeerJ 6 (February 15, 2018): e4312. http://dx.doi.org/10.7717/peerj.4312.

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We present Skan (Skeleton analysis), a Python library for the analysis of the skeleton structures of objects. It was inspired by the “analyse skeletons” plugin for the Fiji image analysis software, but its extensive Application Programming Interface (API) allows users to examine and manipulate any intermediate data structures produced during the analysis. Further, its use of common Python data structures such as SciPy sparse matrices and pandas data frames opens the results to analysis within the extensive ecosystem of scientific libraries available in Python. We demonstrate the validity of Skan’s measurements by comparing its output to the established Analyze Skeletons Fiji plugin, and, with a new scanning electron microscopy (SEM)-based method, we confirm that the malaria parasite Plasmodium falciparum remodels the host red blood cell cytoskeleton, increasing the average distance between spectrin-actin junctions.
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Lemenkova, Polina. "R Libraries {dendextend} and {magrittr} and Clustering Package scipy.cluster of Python For Modelling Diagrams of Dendrogram Trees." Carpathian Journal of Electronic and Computer Engineering 13, no. 3 (September 1, 2020): 5–12. http://dx.doi.org/10.2478/cjece-2020-0002.

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AbstractThe paper presents a comparison of the two languages Python and R related to the classification tools and demonstrates the differences in their syntax and graphical output. It indicates the functionality of R and Python packages {dendextend} and scipy.cluster as effective tools for the dendrogram modelling by the algorithms of sorting and ranking datasets. R and Python programming languages have been tested on a sample dataset including marine geological measurements. The work aims to detect how bathymetric data change along the 25 bathymetric profiles digitized across the Mariana Trench. The methodology includes performed hierarchical cluster analysis with dendrograms and plotted clustermap with marginal dendrograms. The statistical libraries include Matplotlib, SciPy, NumPy, Pandas by Python and {dendextend}, {pvclust}, {magrittr} by R. The dendrograms were compared by the model-simulated clusters of the bathymetric ranges. The results show three distinct groups of the profiles sorted by the elevation ranges with maximal depths detected in a group of profiles 19-21. The dendrogram visualization in a cluster analysis demonstrates the effective representation of the data sorting, grouping and classifying by the machine learning algorithms. The programming codes presented in this study enable to sort a dataset in a similar research aimed to group data based on the similarity of attributes. Effective visualization by dendrograms is a useful modelling tool for the geospatial management where data ranking is required. Plotting dendrograms by R, comparing to Python, presented functional and sophisticated algorithms, refined design control and fine graphical data output. The interdisciplinary nature of this work consists in application of the coding algorithms for spatial data analysis.
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Grose, Lachlan, Laurent Ailleres, Gautier Laurent, and Mark Jessell. "LoopStructural 1.0: time-aware geological modelling." Geoscientific Model Development 14, no. 6 (June 29, 2021): 3915–37. http://dx.doi.org/10.5194/gmd-14-3915-2021.

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Abstract. In this contribution we introduce LoopStructural, a new open-source 3D geological modelling Python package (http://www.github.com/Loop3d/LoopStructural, last access: 15 June 2021). LoopStructural provides a generic API for 3D geological modelling applications harnessing the core Python scientific libraries pandas, numpy and scipy. Six different interpolation algorithms, including three discrete interpolators and 3 polynomial trend interpolators, can be used from the same model design. This means that different interpolation algorithms can be mixed and matched within a geological model allowing for different geological objects, e.g. different conformable foliations, fault surfaces and unconformities to be modelled using different algorithms. Geological features are incorporated into the model using a time-aware approach, where the most recent features are modelled first and used to constrain the geometries of the older features. For example, we use a fault frame for characterising the geometry of the fault surface and apply each fault sequentially to the faulted surfaces. In this contribution we use LoopStructural to produce synthetic proof of concepts models and a 86 km × 52 km model of the Flinders Ranges in South Australia using map2loop.
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Sánchez-Jiménez, David, Fernando Buchón-Moragues, Begoña Escutia-Muñoz, and Rafael Botella-Estrada. "Development of Computer Vision Applications to Automate the Measurement of the Dimensions of Skin Wounds." Proceedings 19, no. 1 (July 16, 2019): 18. http://dx.doi.org/10.3390/proceedings2019019018.

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This paper shows the progress in the development of two computer vision applications for measuring skin wounds. Both applications have been written in Python programming language and make use of OpenCV and Scipy open source libraries. Their objective is to be part of a software that calculates the dimensions of skin wounds in an objective and reliable way. This could be useful in the clinical follow-up, assessing the evolution of skin wounds, as well as in research, comparing the efficacy of different treatments. Merging these two applications into a single one would allow to generate two-dimensional results in real time, and three-dimensional results after a few hours of processing.
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Rubint, Jakub. "Effects of meshing density of 1D structural members with non-uniform cross-section along the length on the calculation of eigenfrequencies." MATEC Web of Conferences 313 (2020): 00004. http://dx.doi.org/10.1051/matecconf/202031300004.

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Density of division in finite element method does not affect only the accuracy of calculation, but also the necessary calculation time. This is directly influenced by the power of the used hardware, the efficiency of the algorithm used to assemble global stiffness and mass matrices and finally, by the method used to find eigenvalues of matrices for determination of eigenfrequencies. In engineering practice, when commercially available software is used, it is necessary to look for the optimum between the accuracy of the calculation and the length of the calculation. This paper deals with solution of eigenfrequencies of 1D elements with nonuniform cross section using Python 3.7.4 with libraries "numpy 1.18.1" for finding eigenvalues of matrices and "scipy 1.4.1" for finding solution for system of nonlinear equations.
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Siebert, Julien, Janek Groß, and Christof Schroth. "A Systematic Review of Packages for Time Series Analysis." Engineering Proceedings 5, no. 1 (June 28, 2021): 22. http://dx.doi.org/10.3390/engproc2021005022.

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This paper presents a systematic review of Python packages with a focus on time series analysis. The objective is to provide (1) an overview of the different time series analysis tasks and preprocessing methods implemented, and (2) an overview of the development characteristics of the packages (e.g., documentation, dependencies, and community size). This review is based on a search of literature databases as well as GitHub repositories. Following the filtering process, 40 packages were analyzed. We classified the packages according to the analysis tasks implemented, the methods related to data preparation, and the means for evaluating the results produced (methods and access to evaluation data). We also reviewed documentation aspects, the licenses, the size of the packages’ community, and the dependencies used. Among other things, our results show that forecasting is by far the most frequently implemented task, that half of the packages provide access to real datasets or allow generating synthetic data, and that many packages depend on a few libraries (the most used ones being numpy, scipy and pandas). We hope that this review can help practitioners and researchers navigate the space of Python packages dedicated to time series analysis. We also provide an updated list of the reviewed packages online.
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Stančić, Adam, Ivan Grgurević, and Zvonko Kavran. "Integration of Transport-relevant Data within Image Record of the Surveillance System." PROMET - Traffic&Transportation 28, no. 5 (October 27, 2016): 517–27. http://dx.doi.org/10.7307/ptt.v28i5.2114.

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Integration of the collected information on the road within the image recorded by the surveillance system forms a unified source of transport-relevant data about the supervised situation. The basic assumption is that the procedure of integration changes the image to the extent that is invisible to the human eye, and the integrated data keep identical content. This assumption has been proven by studying the statistical properties of the image and integrated data using mathematical model modelled in the programming language Python using the combinations of the functions of additional libraries (OpenCV, NumPy, SciPy and Matplotlib). The model has been used to compare the input methods of meta-data and methods of steganographic integration by correcting the coefficients of Discrete Cosine Transform JPEG compressed image. For the procedures of steganographic data processing the steganographic algorithm F5 was used. The review paper analyses the advantages and drawbacks of the integration methods and present the examples of situations in traffic in which the formed unified sources of transport-relevant information could be used.
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Sanyal, Parikshit, and Sanghita Barui. "The watershed transform in pathological image analysis: application in rectiulocyte count from supravital stained smears." International Journal of Research in Medical Sciences 7, no. 3 (February 27, 2019): 871. http://dx.doi.org/10.18203/2320-6012.ijrms20190939.

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Background: Morphometric studies based on image analysis are a useful adjunct for quantitative analysis of microscopic images. However, effective separation of overlapping objects if often the bottleneck in image analysis techniques. We employ the watershed transform for counting reticulocytes from images of supravitally stained smears.Methods: The algorithm was developed with the Python programming platform, using the Numpy, Scipy and OpenCV libraries. The initial development and testing of the software were carried out with images from the American Society of Hematology Image Library. Then a pilot study with 30 samples was then taken up. The samples were incubated with supravital stain immediately after collection, and smears prepared. The smears were microphotographed at 100X objective, with no more than 150 RBCs per field. Reticulocyte count was carried out manually as well as by image analysis.Results: 600 out of 663 reticulocytes (90.49%) were correctly identified, with a specificity of 98%. The major difficulty faced was the slight bluish tinge seen in polychromatic RBCs, which were inconsistently detected by the software.Conclusions: The watershed transform can be used successfully to separate overlapping objects usually encountered in pathological smears. The algorithm has the potential to develop into a generalized cell classifier for cytopathology and hematology.
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Dissertations / Theses on the topic "Python and libraries of python (SciPy"

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Jindra, Jakub. "Detekce stresu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400971.

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Stress detection based on non-EEG physiological data can be useful for monitoring drivers, pilots, and also for monitoring of people in ordinary situation, where standard EEG monitoring is unsuitable. This work uses Non-EEG database freely available from Physionet. The database contains records of heart rate, saturation of blood oxygen, motion, a conductance of skin and temperature recorded for 3 type of stress alternated with relax state. Two final models were created in this thesis. First model for Binary classification stress/relax, second for classification of 4 different type of psychical state. Best results were reached using model created by decision tree algorithm with 8 features for binary classification and with 8 features for classification of 4 psychical state. Accuracy of final models is aproximately 95 % for binary model and 99 % for classification of 4 psychical state. All algorithms were implemented in Python.
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Svensson, Patrik, and Fredrik Galfi. "Performance evaluation of NumPy, SciPy, PyMEL and OpenMaya compared to the C++ API in Autodesk Maya." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21664.

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Background. Autodesk Maya allows scripting through both MEL and Python, and it is also possible to use different Python modules and a C++ API to perform the desired tasks. In theory, the C++ API is the fastest option in Maya, but there are no studies that support this claim. Other studies show that PyMEL is the slowest module in Maya to work with, but it is still the one used most frequently. This thesis has therefore made a speed measurement to determine which of the four selected Python modules and the C++ API is the fastest to use, regarding animation transfer between skeletal hierarchies with different numbers of data. Objectives. The aim of this thesis is to measure the performance in terms of speed of the Python modules NumPy, SciPy, OpenMaya and PyMEL, as well as the C++ API, in order to determine which is the fastest. Our objectives are to determine the speed performance of each module by conducting experiments. Methods. To achieve the objectives, an experiment was conducted to compare the speed of each Python module and the C++ API. To perform the experiments, the implementations for each module and the API have been written in the same way, with their own data types and classes. After performing the experiments for each module, the mean time consumption of each program has been compared. Results. The results from the experiments show that there was a noticeable difference in the speed between the C++ API and the Python modules, as the C++ API delivered the highest speed for all the skeletons that took place in the experiments. The OpenMaya module was the fastest Python module that was tested, while PyMEL was the slowest. The C++ API’s measurements show that it took 0,388–1,909 seconds depending on which skeleton was used to perform the experiment, while OpenMaya’s measurements were 0,538–3,119 seconds which show that OpenMaya is 39–68% slower than the C++ API. NumPy, SciPy and PyMEL’s measurements ranged from 689% to 3165% slower than the C++ API. Conclusions. The conclusion of the experiments show that the C++ API is the fastest to use, while PyMEL is the slowest module, as it is 2632–3165 % slower, when used for these animation transfers. This shows that the C++ API can be a better choice for complex calculations, such as animation transfers.
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Šmejkal, Oldřich. "Využití metod UI v algoritmickém obchodování." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264397.

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Diploma thesis is focused on research and description of current state of machine learning field, focusing on methods that can be used for prediction and classification of time series, which could be then applied in the algorithmic trading field. Reading of theoretical section should explain basic principles of financial markets, algorithmic trading and machine learning also to reader, which was previously familiar with the subject only very thoroughly. Main objective of application part is to choose appropriate methods and procedures, which match current state of art techniques in machine learning field. Next step is to apply it to historical price data. Result of application of selected methods is determination of their success at out of sample data that was not used during model calibration. Success of prediction was evaluated by accuracy metric along with Sharpe ratio of basic trading strategy that is based on model predictions. Secondary outcome of this work is to explore possibilities and test usability of technologies used in application part. Specifically is tested and used SciPy environment, that combines Python with packages and tools designed for data analysis, statistics and machine learning.
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Sládeček, Martin. "3D rekonstrukce z více pohledů kamer." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400663.

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This thesis deals with the task of three-dimensional scene reconstruction using image data obtained from multiple views. It is assumed that intrinsic parameters of the utilized cameras are known. The theoretical chapters describe the basic priciples of individual reconstruction steps. Variuous possible implementaions of data model suitable for this task are also described. The practical part also includes a comparison of false keypoint correspondence filtering, implementation of polar stereo rectification and comparison of disparity map calculation methods that are bundled with the OpenCV library. In the final portion of the thesis, examples of reconstructed 3D models are presented and discussed.
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Rohovets, Taras. "Machine learning algorithms to predict stocks movements with Python language and dedicated libraries." Master's thesis, 2019. http://hdl.handle.net/10400.26/30163.

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This research work focuses on machine learning algorithms in order to make predictions in financial markets. The foremost objective is to test whether the two machine learning algorithms: SVM and LSTM are capable of predicting the price movement in different time-frames and then develop a comparison analysis. In this research work, it is applied supervised machine learning with different input features. The practical and software component of this thesis applies Python programming language to test the hypothesis and act as proof of concept. The financial data quotes were obtained through online financial databases. The results demonstrate that SVM is capable of predicting the direction of the price while the LSTM did not present reliable results.
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Books on the topic "Python and libraries of python (SciPy"

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Bressert, Eli. SciPy and NumPy: Optimizing & boosting your Python programming. Beijing: O'Reilly, 2012.

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Inc, ebrary, ed. Spring Python 1.1: Create powerful and versatile Spring Python applications using pragmatic libraries and useful abstractions. Birmingham, U.K: Packt Open Source, 2010.

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Hightower, Richard. Python programming with the Java class libraries: A tutorial for building Web and Enterprise applications. Boston, MA: Addison-Wesley, 2002.

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Python programming with the Java class libraries: A tutorial for building Web and Enterprise applications with Jython. Boston, MA: Addison-Wesley, 2003.

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Inc, ebrary, ed. Python text processing with NLTK 2.0 cookbook: Over 80 practical recipes for using Python's NLTK suite of libraries to maximize your natural language processing capabilities. Birmingham, U.K: Packt Pub., 2010.

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Barber, Nan, ed. Elegant SciPy: The Art of Scientific Python. O'Reilly Media, 2017.

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Nanjekye, Joannah. Python 2 and 3 Compatibility: With Six and Python-Future Libraries. Apress, 2017.

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Rajagopalan, Gayathri. A Python Data Analyst’s Toolkit: Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics. Apress, 2020.

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Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib. Apress, 2018.

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Pajankar. RASPBERRY PI IMAGE PROCESSING PROGRAMMING: DEVELOP REAL-LIFE EXAMPLES WITH PYTHON, PILLOW, AND SCIPY. Apress, 2019.

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Book chapters on the topic "Python and libraries of python (SciPy"

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Schäfer, Christoph. "Erweiterungen für Naturwissenschaftler: NumPy, SciPy, Matplotlib, pandas." In Schnellstart Python, 51–59. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-26133-7_9.

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Woyand, Hans-Bernhard. "Numerische Analysen mit Scipy." In Python für Ingenieure und Naturwissenschaftler, 259–86. München: Carl Hanser Verlag GmbH & Co. KG, 2018. http://dx.doi.org/10.3139/9783446457966.010.

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Woyand, Hans-Bernhard. "Numerische Analysen mit Scipy." In Python für Ingenieure und Naturwissenschaftler, 265–98. München: Carl Hanser Verlag GmbH & Co. KG, 2019. http://dx.doi.org/10.3139/9783446461093.010.

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Woyand, Hans-Bernhard. "Numerische Analysen mit Scipy." In Python für Ingenieure und Naturwissenschaftler, 281–316. 4th ed. München: Carl Hanser Verlag GmbH & Co. KG, 2021. http://dx.doi.org/10.3139/9783446465015.010.

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Porcu, Valentina. "SciPy and NumPy." In Python for Data Mining Quick Syntax Reference, 177–200. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4113-4_9.

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Lindblad, Thomas, and Jason M. Kinser. "NumPy, SciPy and Python Image Library." In Image Processing using Pulse-Coupled Neural Networks, 35–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36877-6_3.

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Pilgrim, Mark. "Packaging Python Libraries." In Dive Into Python 3, 279–93. Berkeley, CA: Apress, 2009. http://dx.doi.org/10.1007/978-1-4302-2416-7_16.

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Pine, David J. "Numerical Routines: SciPy and NumPy." In Introduction to Python for Science and Engineering, 205–38. Boca Raton, Florida : CRC Press, [2019] | Series: Series in computational physics: CRC Press, 2019. http://dx.doi.org/10.1201/9780429506413-9.

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Walters, Gregory. "Libraries." In The Python Quick Syntax Reference, 93–105. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-6479-8_9.

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Frochte, Jörg. "Python, NumPy, SciPy und Matplotlib – in a nutshell." In Maschinelles Lernen, 32–67. München: Carl Hanser Verlag GmbH & Co. KG, 2018. http://dx.doi.org/10.3139/9783446457058.003.

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Conference papers on the topic "Python and libraries of python (SciPy"

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Reikeras, Helge, Ben Herbst, Johan du Preez, and Herman Engelbrecht. "Audio-Visual Speech Recognition using SciPy." In Python in Science Conference. SciPy, 2010. http://dx.doi.org/10.25080/majora-92bf1922-010.

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Baxter, G. "Scientific Computing with SciPy for Undergraduate Physics Majors." In Python in Science Conference. SciPy, 2014. http://dx.doi.org/10.25080/majora-14bd3278-001.

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Johansson, Robert, and Paul Nation. "QuTiP: A framework for the dynamics of open quantum systems using SciPy and Cython." In Python in Science Conference. SciPy, 2012. http://dx.doi.org/10.25080/majora-54c7f2c8-00a.

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Weinstein, Alejandro, and Michael Wakin. "A Tale of Four Libraries." In Python in Science Conference. SciPy, 2012. http://dx.doi.org/10.25080/majora-54c7f2c8-002.

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Malakhov, Anton. "Composable Multi-Threading for Python Libraries." In Python in Science Conference. SciPy, 2016. http://dx.doi.org/10.25080/majora-629e541a-002.

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Malakhov, Anton, David Liu, Anton Gorshkov, and Terry Wilmarth. "Composable Multi-Threading and Multi-Processing for Numeric Libraries." In Python in Science Conference. SciPy, 2018. http://dx.doi.org/10.25080/majora-4af1f417-003.

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Kube, Ralph, R. M. Churchill, JY Choi, R. Wang, S. Klasky, C. S. Chang, MJ Choi, and J. Park. "Leading magnetic fusion energy science into the big-and-fast data lane." In Scientific Computing with Python - SciPy 2020, July 6-12, 2020. US DOE, 2020. http://dx.doi.org/10.2172/1668037.

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Viarbitskaya, Tatsiana, and Andrzej Dobrucki. "Audio processing with using Python language science libraries." In 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE, 2018. http://dx.doi.org/10.23919/spa.2018.8563430.

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Tan, Jialiang, Yu Chen, Zhenming Liu, Bin Ren, Shuaiwen Leon Song, Xipeng Shen, and Xu Liu. "Toward efficient interactions between Python and native libraries." In ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3468264.3468541.

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Tariq, Tayyaba, Javed Frezund, Muhammad Farhan, Rana M. Amir Latif, and Azka Mehmood. "Structure Analysis of Protein Data Bank Using Python Libraries." In 2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2020. http://dx.doi.org/10.1109/ibcast47879.2020.9044525.

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Reports on the topic "Python and libraries of python (SciPy"

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Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.
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