Relevant bibliographies by topics / Small scaled image

Contents

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

Academic literature on the topic 'Small scaled image'

Author: Grafiati
Published: 3 June 2025
Last updated: 20 July 2025

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Journal articles on the topic "Small scaled image"

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Kishor, Datta Gupta, and Sen Sajib. "BRAIN. Broad Research in Artificial Intelligence and Neuroscience-A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 9, no. 2 (2018): 34–44. https://doi.org/10.5281/zenodo.1245885.

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Small scaled image lost some important bits of information which cannot be recovered when scaled back. Using multi-objective genetic algorithm, we can recover these lost bits. In this paper, we described a genetic algorithm approach to recover lost bits while image resized to the smaller version using the original image data bit counts which are stored while the image is scaled. This method is very scalable to apply in a distributed system. Also, the same method can be applied to recover error bits in any types of data blocks. In this paper, we showed proof of concept by providing the implementation and results.
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Merino, Timothy, Roman Negri, Dipika Rajesh, M. Charity, and Julian Togelius. "The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddings." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 19, no. 1 (2023): 107–15. http://dx.doi.org/10.1609/aiide.v19i1.27506.

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The five-dollar model is a lightweight text-to-image generative architecture that generates low dimensional images or tile maps from an encoded text prompt. This model can successfully generate accurate and aesthetically pleasing content in low dimensional domains, with limited amounts of training data. Despite the small size of both the model and datasets, the generated images or maps are still able to maintain the encoded semantic meaning of the textual prompt. We apply this model to three small datasets: pixel art video game maps, video game sprite images, and down-scaled emoji images and apply novel augmentation strategies to improve the performance of our model on these limited datasets. We evaluate our models' performance using cosine similarity score between text-image pairs generated by the CLIP VIT-B/32 model to demonstrate quality generation.
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Sapna Shinde, Priti Chakurkar, and Rashmi Rane. "Deep Learning-Based Small Face Detection from Hard Image." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 03 (2024): 579–88. http://dx.doi.org/10.47392/irjaeh.2024.0084.

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Facial detection usually comes first in face recognition and face analysis systems. Previously, techniques such as directed gradient histograms and cascades relied on manually-engineered features from particular photos. Nevertheless, the precision with which these techniques could identify faces in uncontrolled environments was restricted. Numerous deep learning-based face recognition frameworks have recently been developed, many of which have significantly increased accuracy, as a result of the rapid progress of deep learning in computer vision. Despite these advancements, detecting small, scaled, positioned, occluded, blurred, and faces that are partially occluded in uncontrolled conditions remains a challenge in face identification. This problem has been studied for many years but has not been completely resolved.
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Lu, Tao, Jiaming Wang, Yanduo Zhang, Zhongyuan Wang, and Junjun Jiang. "Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network." Remote Sensing 11, no. 13 (2019): 1588. http://dx.doi.org/10.3390/rs11131588.

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Recently, the application of satellite remote sensing images is becoming increasingly popular, but the observed images from satellite sensors are frequently in low-resolution (LR). Thus, they cannot fully meet the requirements of object identification and analysis. To utilize the multi-scale characteristics of objects fully in remote sensing images, this paper presents a multi-scale residual neural network (MRNN). MRNN adopts the multi-scale nature of satellite images to reconstruct high-frequency information accurately for super-resolution (SR) satellite imagery. Different sizes of patches from LR satellite images are initially extracted to fit different scale of objects. Large-, middle-, and small-scale deep residual neural networks are designed to simulate differently sized receptive fields for acquiring relative global, contextual, and local information for prior representation. Then, a fusion network is used to refine different scales of information. MRNN fuses the complementary high-frequency information from differently scaled networks to reconstruct the desired high-resolution satellite object image, which is in line with human visual experience (“look in multi-scale to see better”). Experimental results on the SpaceNet satellite image and NWPU-RESISC45 databases show that the proposed approach outperformed several state-of-the-art SR algorithms in terms of objective and subjective image qualities.
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Günen, Mehmet Akif, Umit Haluk Atasever, and Erkan Beşdok. "Analyzing the Contribution of Training Algorithms on Deep Neural Networks for Hyperspectral Image Classification." Photogrammetric Engineering & Remote Sensing 86, no. 9 (2020): 581–88. http://dx.doi.org/10.14358/pers.86.9.581.

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Autoencoder (<small>AE</small>)-based deep neural networks learn complex problems by generating feature-space conjugates of input data. The learning success of an AE is too sensitive for a training algorithm. The problem of hyperspectral image (<small>HSI</small>) classification by using spectral features of pixels is a highly complex problem due to its multi-dimensional and excessive data nature. In this paper, the contribution of three gradient-based training algorithms (i.e., scaled conjugate gradient (<small>SCG</small>), gradient descent (<small>GD</small>), and resilient backpropagation algorithms (<small>RP</small>)) on the solution of the HSI classification problem by using AE was analyzed. Also, it was investigated how neighborhood component analysis affects classification performance for training algorithms on HSIs. Two hyperspectral image classification benchmark data sets were used in the experimental analysis. Wilcoxon signed-rank test indicates that RB is statistically better than SCG and GD in solving the related image classification problem.
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Wei, Linhai, Chen Zheng, and Yijun Hu. "Oriented Object Detection in Aerial Images Based on the Scaled Smooth L1 Loss Function." Remote Sensing 15, no. 5 (2023): 1350. http://dx.doi.org/10.3390/rs15051350.

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Although many state-of-the-art object detectors have been developed, detecting small and densely packed objects with complicated orientations in remote sensing aerial images remains challenging. For object detection in remote sensing aerial images, different scales, sizes, appearances, and orientations of objects from different categories could most likely enlarge the variance in the detection error. Undoubtedly, the variance in the detection error should have a non-negligible impact on the detection performance. Motivated by the above consideration, in this paper, we tackled this issue, so that we could improve the detection performance and reduce the impact of this variance on the detection performance as much as possible. By proposing a scaled smooth L1 loss function, we developed a new two-stage object detector for remote sensing aerial images, named Faster R-CNN-NeXt with RoI-Transformer. The proposed scaled smooth L1 loss function is used for bounding box regression and makes regression invariant to scale. This property ensures that the bounding box regression is more reliable in detecting small and densely packed objects with complicated orientations and backgrounds, leading to improved detection performance. To learn rotated bounding boxes and produce more accurate object locations, a RoI-Transformer module is employed. This is necessary because horizontal bounding boxes are inadequate for aerial image detection. The ResNeXt backbone is also adopted for the proposed object detector. Experimental results on two popular datasets, DOTA and HRSC2016, show that the variance in the detection error significantly affects detection performance. The proposed object detector is effective and robust, with the optimal scale factor for the scaled smooth L1 loss function being around 2.0. Compared to other promising two-stage oriented methods, our method achieves a mAP of 70.82 on DOTA, with an improvement of at least 1.26 and up to 16.49. On HRSC2016, our method achieves an mAP of 87.1, with an improvement of at least 0.9 and up to 1.4.
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Ni, Peishuang, Yanyang Liu, Hao Pei, Haoze Du, Haolin Li, and Gang Xu. "CLISAR-Net: A Deformation-Robust ISAR Image Classification Network Using Contrastive Learning." Remote Sensing 15, no. 1 (2022): 33. http://dx.doi.org/10.3390/rs15010033.

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The inherent unknown deformations of inverse synthetic aperture radar (ISAR) images, such as translation, scaling, and rotation, pose great challenges to space target classification. To achieve high-precision classification for ISAR images, a deformation-robust ISAR image classification network using contrastive learning (CL), i.e., CLISAR-Net, is proposed for deformation ISAR image classification. Unlike traditional supervised learning methods, CLISAR-Net develops a new unsupervised pretraining phase, which means that the method uses a two-phase training strategy to achieve classification. In the unsupervised pretraining phase, combined with data augmentation, positive and negative sample pairs are constructed using unlabeled ISAR images, and then the encoder is trained to learn discriminative deep representations of deformation ISAR images by means of CL. In the fine-tuning phase, based on the deep representations obtained from pretraining, a classifier is fine-tuned using a small number of labeled ISAR images, and finally, the deformation ISAR image classification is realized. In the experimental analysis, CLISAR-Net achieves higher classification accuracy than supervised learning methods for unknown scaled, rotated, and combined deformations. It implies that CLISAR-Net learned more robust deep features of deformation ISAR images through CL, which ensures the performance of the subsequent classification.
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Jenkins, David R. "Differential contrast enhancement of digital image information by PC-based massively parallel processing." Proceedings, annual meeting, Electron Microscopy Society of America 52 (1994): 516–17. http://dx.doi.org/10.1017/s0424820100170311.

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Digital images represent two-dimensional intensity maps which characterize the spacial x/y distribution of contrast information. Often, small detail contrasts are visaully unrecognizable due to the prevalence of large contrast components which, inversely proportional to their extent, compress the intensity range of the detail contrasts. A new “pixel-accurate intensity processing” (PAIP) technology provides unrestricted access to and display of all detail information contained in digital images independent of content, size and depth (8-16 bit). It offers an exciting new way for objective and exhaustive digital image evaluation in near-real-time.We developed a PC-based massively parallel processing workstation (PiXision) for fast, artifact free differential contrast extraction utilizing the PAIP technology. Information extraction and enhancement requires only a few digital processing steps (Fig. 1): 1. The original image is smoothed with pixelaccurate precision within a significant intensity range (IRS) using the PAIP technology; 2. The smoothed image may additionally be subtracted from the original generating a detail image; 3. Finally, the smoothed or detail images are linearly scaled to 8-bit providing full visual intensity range. The basic software routine “smoolhens” an image to any significant intensity range without spacial artifacts, using a variable automatically adjusting local mask for pixel averaging.
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Hasan, Sumaya Falih, Muntadher Aidi Shareef, and Hussein Sabah Jaber. "Influence of Noise Equivalent Beta Naught estimation on backscattering image classification of TerraSAR-X." Geodesy and cartography 50, no. 2 (2024): 104–12. http://dx.doi.org/10.3846/gac.2024.18264.

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Noise Equivalent Beta Naught is the different noise influence that beneficence to the radar signal. This type of noise is available in TerraSAR-X satellite images and expressed in forms of scaled polynomial described the noise power. On the other hand, Sigma naught or backscattering coefficient represents the average reflectivity of a horizontal material samples which used to reflect the nature of the land use and land cover in radar images. In this paper, radar satellite images in dual VV and HH polarization were used to study the influence of the noise on backscattering image classification. The result demonstrated that the visual interpretation of sigma naught which is result from the comparison between existence case and absence case (in the other word: with and without noise) of the noise illustrated that there is no different between them. In the other hand, for more details and more precise, an example of small images are used to show the values of obtained backscattering. The result demonstrated that the NEBN plays the main roles in decreasing the values of backscattering coefficient in TSX image. The influence of this noise had usually high in water body surface, because this surface is generally having small backscattering coefficients compared with land cover.
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JIN, JIANGANG. "AN ADAPTIVE IMAGE SCALING ALGORITHM BASED ON CONTINUOUS FRACTION INTERPOLATION AND MULTI-RESOLUTION HIERARCHY PROCESSING." Fractals 28, no. 08 (2020): 2040016. http://dx.doi.org/10.1142/s0218348x20400162.

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Traditional interpolation algorithms often blur the edges of the target image due to low-pass filtering effects, making it difficult to obtain satisfactory visual effects. Especially when the reduction ratio becomes small, the phenomenon of jagged edges and partial information loss will occur. In order to obtain better image scaling quality, an adaptive image scaling algorithm based on continuous fraction interpolation and multi-resolution hierarchical processing is proposed. In order to overcome the noise problem of the original image, this paper first performs wavelet decomposition on the original image to obtain multiple images with different resolutions. Secondly, in order to eliminate the influence of local area variance on the overall image, weighted average is performed on images of different resolutions. Then, in order to overcome the blurring effect of the weighted average image, by calculating the variance of the three groups of pixels around the target pixel, selecting a group of pixels with the smallest variance and using the Salzer continuous fraction interpolation equation to obtain the gray value of the target pixel. Finally, the multiple corrected images are stitched together into a scaled image. The algorithm in this paper achieves a high-order smooth transition between pixels in the same feature area, and can also adaptively modify the pixels of the image. The experimental results show that the edge of the target image obtained by the algorithm in this paper is clear, and the algorithm complexity is low, which is convenient for hardware implementation and can realize real-time image scaling.
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Dissertations / Theses on the topic "Small scaled image"

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Chang, Ran. "Effective Graph-Based Content--Based Image Retrieval Systems for Large-Scale and Small-Scale Image Databases." DigitalCommons@USU, 2013. https://digitalcommons.usu.edu/etd/2123.

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This dissertation proposes two novel manifold graph-based ranking systems for Content-Based Image Retrieval (CBIR). The two proposed systems exploit the synergism between relevance feedback-based transductive short-term learning and semantic feature-based long-term learning to improve retrieval performance. Proposed systems first apply the active learning mechanism to construct users' relevance feedback log and extract high-level semantic features for each image. These systems then create manifold graphs by incorporating both the low-level visual similarity and the high-level semantic similarity to achieve more meaningful structures for the image space. Finally, asymmetric relevance vectors are created to propagate relevance scores of labeled images to unlabeled images via manifold graphs. The extensive experimental results demonstrate two proposed systems outperform the other state-of-the-art CBIR systems in the context of both correct and erroneous users' feedback.
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Hohenegger, Christel. "Small Scale Stochastic Dynamics For Particle Image Velocimetry Applications." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10464.

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Fluid velocities and Brownian effects at nanoscales in the near-wall region of microchannels can be experimentally measured in an image plane parallel to the wall using, for example, evanescent wave illumination technique combined with particle image velocimetry [R. Sadr extit{et al.}, J. Fluid. Mech. 506, 357-367 (2004)]. The depth of field of this technique being difficult to modify, reconstruction of the out-of-plane dependence of the in-plane velocity profile remains extremely challenging. Tracer particles are not only carried by the flow, but they undergo random fluctuation imposed by the proximity of the wall. We study such a system under a particle based stochastic approach (Langevin) and a probabilistic approach (Fokker-Planck). The Langevin description leads to a coupled system of stochastic differential equations. Because the simulated data will be used to test a statistical hypothesis, we pay particular attention to the strong order of convergence of the scheme developing an appropriate Milstein scheme of strong order of convergence 1. Based on the probability density function of mean in-plane displacements, a statistical solution to the problem of the reconstruction of the out-of-plane dependence of the velocity profile is proposed. We developed a maximum likelihood algorithm which determines the most likely values for the velocity profile based on simulated perfect particle position, simulated perfect mean displacements and simulated observed mean displacements. Effects of Brownian motion on the approximation of the mean displacements are briefly discussed. A matched particle is a particle that starts and ends in the same image window after a measurement time. AS soon as the computation and observation domain are not the same, the distribution of the out-of-plane distances sampled by matched particles during the measurement time is not uniform. The combination of a forward and a backward solution of the one dimensional Fokker-Planck equation is used to determine this probability density function. The non-uniformity of the resulting distribution is believed to induce a bias in the determination of slip length and is quantified for relevant experimental parameters.
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Rumsey, Charles B. "A water tunnel investigation of a small scale rotor operating in the vortex ring state." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FRumsey.pdf.

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Sperandei, Bryan. "The Application of Particle Image Velocimetry in a Small Scale Wind Tunnel." Thesis, University of Waterloo, 2002. http://hdl.handle.net/10012/794.

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This study investigated the applicability of Particle Image Velocimetry (PIV) as a velocity measurement technique for use in wind tunnel flows. To carry out the investigation, a small scale wind tunnel was designed and built to be used specifically with PIV. The tunnel employed a novel contraction geometry which was compared to six other contraction designs using a computational fluid dynamics (CFD) software package. The wind tunnel configuration allowed for full optical access in the test section to allow for PIV measurements in three dimensions. The calibration and characterization of the flow quality within the wind tunnel were performed using PIV. Velocity measurements were obtained in the empty test section to assess the degree of uniformity, alignment, and turbulence at various test speeds. The longitudinal velocities were found to deviate by an average of 1. 8% along any given velocity profile. The flow was found to be well aligned with the test section walls, deviating by no more than &plusmn;0. 20° in most cases. As well, the turbulence levels in the test section were found to be low, with average intensities of 2. 0% and 0. 5% in the longitudinal and transverse directions, respectively. Following the characterization of the flow in the empty wind tunnel, a square cylinder was placed in the test section and PIV measurements were performed at a Reynolds number of 21,400. Mean velocities and turbulence intensities measured around the square cylinder were found to compare well with previous works conducted at similar Reynolds numbers in water flows. As a final validation of the wind tunnel/PIV system, measurements were made of the flow over a 1:18 scale Formula One racecar model at a free stream velocity of 40 <i>m/s</i>. The PIV system collected a large quantity of velocity information around the model, providing insight into the aerodynamic aspects of racecars such as downforce devices and vehicle draughting. The experiments performed in this study led to the conclusion that PIV is indeed a measurement technique with high potential for use in small wind tunnels, providing more spatially resolved velocity data than any other known measurement technique. The advancement of digital camera technology will make PIV a more practical measurement technique for use in larger wind tunnels as well.
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Campbell, Jacob L. "The Development of a Small Scale Radio Astronomy Image Synthesis Array for Research in Radio Frequency Interference Mitigation." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1014.pdf.

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Crane, Kristy Susanne. "Pressurized Hot Water: An Alternative Method of Nutrient Extraction and Subsequent Analysis for Use in Small-Scale Agriculture." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd495.pdf.

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Rammali, Hasin Mohammed. "Cartographic design aspects of medium- and small-scale space image maps : with specific reference to Libya." Thesis, University of Glasgow, 2000. http://theses.gla.ac.uk/1977/.

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Emtage, Nicholas F. "An investigation of the social and economic factors affecting the development of small-scale forestry by rural households in Leyte Province, Philippines /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18311.pdf.

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Rajendar, Ashok. "Internal flow effects on performance of combustion powered actuators." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42901.

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Earlier investigations of Combustion Powered Actuation (COMPACT) have demonstrated its utility for high-speed aerodynamic flow control. In this actuation approach, momentary (pulsed) actuation jets are produced by the ignition of a mixture of gaseous fuel and oxidizer within a cubic-centimeter scale chamber. The combustion process yields a high pressure burst and the ejection of a high-speed exhaust jet. The present thesis focuses on characterization of the effects of the internal flow (which is altered through the fuel and oxidizer inlet streams) on mixing and flame propagation within the actuator's combustion chamber, and thereby on actuator operation and performance. A test chamber with a grid of interchangeable air and fuel inlets was used for parametric investigations of the effects of inlet size and location. Actuator performance is characterized using dynamic pressure measurements and phase-locked Particle Image Velocimetry (PIV) of the combustor's internal flow field in the presence and absence of the active combustion process. Over the range tested, increased momentum of the air inlet jet for a given flow rate improves the actuator performance by increasing bulk velocities and small-scale motions within the chamber, thus yielding net higher flame propagation speed and subsequently faster pressure rise and higher pressure peak. Variation in inlet location that results in swirling flow within the chamber yields higher internal pressures while air flow over the spark ignition site yields lower internal pressures and erratic combustion. Improved refill and combustion processes will lead to enhanced performance combustor designs.
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Hugget, Quentin J. "Small-scale sedimentary features in the deep ocean and their effect on side-scan sonar images." Thesis, University of Reading, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.278238.

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Books on the topic "Small scaled image"

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Shiel, Arthur. Selecting small scale DIP systems: Checklist & guide. Cimtech Ltd, Hatfield Polytechnic, 1991.

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McAllister, Frances. A PC based image computing environment: The production of small scale texture using magnified bit map and colourpalette. The author], 1993.

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Casimir, Jean, and Walter D. Mignolo. The Haitians. Translated by Laurent Dubois. University of North Carolina Press, 2020. http://dx.doi.org/10.5149/northcarolina/9781469660486.001.0001.

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In this sweeping history, leading Haitian intellectual Jean Casimir argues that the story of Haiti should not begin with the usual image of Saint-Domingue as the richest colony of the eighteenth century. Rather, it begins with a reconstruction of how individuals from Africa, in the midst of the golden age of imperialism, created a sovereign society based on political imagination and a radical rejection of the colonial order, persisting even through the U.S. occupation in 1915. The Haitians also critically retheorizes the very nature of slavery, colonialism, and sovereignty. Here, Casimir centers the perspectives of Haiti's moun andeyo—the largely African-descended rural peasantry. Asking how these systematically marginalized and silenced people survived in the face of almost complete political disenfranchisement, Casimir identifies what he calls a counter-plantation system. Derived from Caribbean political and cultural practices, the counter-plantation encompassed consistent reliance on small-scale landholding. Casimir shows how lakou, small plots of land often inhabited by generations of the same family, were and continue to be sites of resistance even in the face of structural disadvantages originating in colonial times, some of which continue to be maintained by the Haitian government with support from outside powers.
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Duplouy, Alain, and Roger W. Brock, eds. Defining Citizenship in Archaic Greece. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198817192.001.0001.

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Citizenship is a major feature of contemporary national and international politics. It is also a legacy of ancient Greece. The concept of membership of a community appeared in Greece some three millennia ago as a participation in the social and political life of small-scale communities, but only towards the end of the fourth century BC did Aristotle offer the first explicit statement about it. Though long accepted, the Aristotelian definition remains deeply rooted in the philosophical and political thought of the classical period, but it probably fails to account accurately for the previous centuries or the dynamics of the emergent cities. Focusing on archaic Greece, this collective enquiry, bringing together renowned international scholars, aims at exploring new routes to archaic citizenship, exemplifying the living diversity of approaches to archaic Greece and to the Greek city. If the Aristotelian model has long been applied to all Greek cities regardless of chronological issues, historians are now challenging Aristotle’s theoretical definition and are looking for other ways of conceiving citizenship and community, setting the stage for a new image of archaic cities, which are no longer to be considered as primitive or incomplete classical poleis. Driven by this same objective, the essays collected here have not, however, been tailored to endorse any specific view. Each contributor brings his or her own national background and approaches to archaic citizenship through specific fields of enquiry (law, descent, cults, military obligations, associations, civic subdivisions, athletics, commensality, behaviours, etc.), often venturing off the beaten track.
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Gelernter, David. Mirror Worlds. Oxford University Press, 1991. http://dx.doi.org/10.1093/oso/9780195068122.001.0001.

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Technology doesn't flow smoothly; it's the big surprises that matter, and Yale computer expert David Gelernter sees one such giant leap right on the horizon. Today's small scale software programs are about to be joined by vast public software works that will revolutionize computing and transform society as a whole. One such vast program is the "Mirror world." Imagine looking at your computer screen and seeing reality--an image of your city, for instance, complete with moving traffic patterns, or a picture that sketches the state of an entire far-flung corporation at this second. These representations are called Mirror worlds, and according to Gelernter they will soon be available to everyone. Mirror worlds are high-tech voodoo dolls: by interacting with the images, you interact with reality. Indeed, Mirror worlds will revolutionize the use of computers, transforming them from (mere) handy tools to crystal balls which will allow us to see the world more vividly and see into it more deeply. Reality will be replaced gradually, piece-by-piece, by a software imitation; we will live inside the imitation; and the surprising thing is--this will be a great humanistic advance. we gain control over our world, plus a huge new measure of insight and vision. In this fascinating book--part speculation, part explanation--Gelernter takes us on a tour of the computer technology of the near future. Mirror worlds, he contends, will allow us to explore the world in unprecedented depth and detail without ever changing out of our pajamas. A hospital administrator might wander through an entire medical complex via a desktop computer. Any citizen might explore the performance of the local schools, chat electronically with teachers and other Mirror world visitors, plant software agents to report back on interesting topics; decide to run for the local school board, hire a campaign manager, and conduct the better part of the campaign itself--all by interacting with the Mirror world. Gelernter doesn't just speculate about how this amazing new software will be used--he shows us how it will be made, explaining carefully and in detail how to build a Mirror world using technology already available. we learn about "disembodied machines," "trellises," "ensembles," and other computer components which sound obscure, but which Gelernter explains using familiar metaphors and terms. (He tells us that a Mirror world is a microcosm just like a Japanese garden or a Gothic cathedral, and that a computer program is translated by the computer in the same way a symphony is translated by a violinist into music.) Mirror worlds offers a lucid and humanistic account of the coming software revolution, told by a computer scientist at the cutting edge of his field.
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Book chapters on the topic "Small scaled image"

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Shao, Chen, Qiang Wang, and Shi Li. "Small-Scale Image Inpainting on Mobile Platform." In Advances in Graphic Communication, Printing and Packaging. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3663-8_33.

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Salberg, Arnt-Børre, Øivind Due Trier, and Michael Kampffmeyer. "Large-Scale Mapping of Small Roads in Lidar Images Using Deep Convolutional Neural Networks." In Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59129-2_17.

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Platzer, Paul, and Bertrand Chapron. "The Effects of Unresolved Scales on Analogue Forecasting Ensembles." In Mathematics of Planet Earth. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70660-8_10.

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AbstractUsing similar states in a database called “catalogue” such as a reanalysis, analogues provide simple yet efficient ensemble forecasts in atmospheric and ocean sciences. Typically performed on low-resolution images of large-scale atmospheric or ocean circulation, analogue forecasting encounters uncertainties due to unresolved small spatial scales, as the latter contribute to the time-evolution of the circulation but not to the similarity criterion used to search for analogues. Another source of uncertainty are the finite distances between the analogues and the initial target large-scale variables. We disentangle these two sources of uncertainty using a modified version of the Lorenz system, where stochastic terms account for unresolved small spatial scales. For large enough catalogue size and forecast horizon, we show that the analogue forecasting ensemble spread is dominated by the effect of stochastic terms, with only little influence of the initial analogue-to-target distances. Conversely, for short-term forecast and small catalogue size, the analogue ensemble is mostly influenced by initial analogue-to-target distances and not by the effects of unresolved scales. This result calls for adjustments of the classical analogue method for small forecast horizons.
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Avola, Danilo, Gian Luca Foresti, Niki Martinel, Christian Micheloni, Daniele Pannone, and Claudio Piciarelli. "Real-Time Incremental and Geo-Referenced Mosaicking by Small-Scale UAVs." In Image Analysis and Processing - ICIAP 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68560-1_62.

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Kawai, Masakata, Noriaki Ota, and Shinsuke Yamaoka. "Large-Scale Pretraining on Pathological Images for Fine-Tuning of Small Pathological Benchmarks." In Medical Image Learning with Limited and Noisy Data. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44917-8_25.

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Uddin, Abdul Hasib, Sharder Shams Mahamud, and Abu Shamim Mohammad Arif. "A Novel Leaf Fragment Dataset and ResNet for Small-Scale Image Analysis." In Intelligent Sustainable Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2422-3_3.

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Nurhikmah, F., M. H. Zawawi, N. H. Hassan, and N. M. Zahari. "Water Flow Velocity Analysis on Small-Scale Spillway Using Particle Image Velocimetry (PIV)." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6026-2_43.

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Resseguier, Valentin. "Maximum Likelihood Estimation of Subgrid Flows from Tracer Image Sequences." In Mathematics of Planet Earth. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70660-8_12.

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AbstractFrom a sequence of tracer satellite images, several methods (e.g. optical flow) exist to successfully estimate the main advecting current. Yet, this estimate is limited in resolution. To go beyond, we propose a new parametric estimation method to estimate second-order statistics of the residual small-scale velocity. We first express stochastic transport in a discrete setting to apply standard MLE techniques. Then we propose an efficient method to solve the MLE optimization problem through a fast log-likelihood gradient evaluation algorithm.
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Brüning, Markus, Paul Wunderlich, and Helene Dörksen. "Advanced Feature Extraction Workflow for Few Shot Object Recognition." In Bildverarbeitung in der Automation. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66769-9_4.

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AbstractObject recognition is well known to have a high importance in various fields. Example applications are anomaly detection and object sorting. Common methods for object recognition in images divide into neural and non-neural approaches: Neural-based concepts, e.g. using deep learning techniques, require a lot of training data and involve a resource intensive learning process. Additionally, when working with a small number of images, the development effort increases. Common non-neural feature detection approaches, such as SIFT, SURF or AKAZE, do not require these steps for preparation. They are computationally less expensive and often more efficient than the neural-based concepts. On the downside, these algorithms usually require grey-scale images as an input. Thus, information about the color of the reference image cannot be considered as a determinant for recognition. Our objective is to achieve an object recognition approach by eliminating the “color blindness” of key point extraction methods by using a combination of SIFT, color histograms and contour detection algorithms. This approach is evaluated in context of object recognition on a conveyor belt. In this scenario, objects can only be recorded while passing the camera’s field of vision. The approach is divided into three stages: In the first step, Otsu’s method is applied among other computer vision algorithms to perform automatic edge detection for object localization. Within the subsequent second stage, SIFT extracts key points out of the previously identified region of interest. In the last step, color histograms of the specified region are created to distinguish between objects that feature a high similarity in the extracted key points. Only one image is sufficient to serve as a template. We are able to show that developing and applying a concept with a combination of SIFT, histograms and edge detection algorithms successfully compensates the color blindness of the SIFT algorithm. Promising results in the conducted proof of concept are achieved without the need for implementing complex and time consuming methods.
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Li, Yan, Shi-Yuan Han, Tao Xu, Xiao-hui Yang, Qiu Lu, and Yuan Shen. "Cascade Detector Based on Multi-scale Features for Small Objects in Various Backgrounds of Remote Sensing." In Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0923-0_31.

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Conference papers on the topic "Small scaled image"

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Wieghaus, Markus F., Warda Saeed, and Muhammad Yasir. "Artificial Enhanced Image Inpainting for Broadband SMM." In 2024 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS). IEEE, 2024. http://dx.doi.org/10.1109/marss61851.2024.10612754.

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Shao, Chao, Shaochen Jiang, and Yongming Li. "PSA-Swin Transformer: Image Classification on Small-Scale Datasets*." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831183.

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A, Miao, and Jie Yang. "Research on lightweight small target detection algorithms based on scale awareness." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2025), edited by Haiquan Zhao and Xinhua Tang. SPIE, 2025. https://doi.org/10.1117/12.3070631.

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Lin, Jiatai, Guoqiang Han, Jingxuan Zhou, Zhenwei Shi, Zaiyi Liu, and Chu Han. "SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation for Histopathology Image." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821867.

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Cheng, Kangda, Zhilu Wu, Haiyan Jin, and Xiaobao Li. "Remote Sensing Image Captioning With Multi-Scale Feature and Small Target Attention." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642778.

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Zhong, Hao, Zixuan Zhou, Mingyuan Yang, Shanfeng Liu, Youmin Hu, and Haifeng Wang. "End-to-end industrial small-scale defect recognition based on real-time detection transformer." In 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), edited by Kelin Du and Azlan bin Mohd Zain. SPIE, 2024. http://dx.doi.org/10.1117/12.3038528.

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Kitching, John. "Chip-Scale Atomic Devices: From Clocks to Brain Imaging and Beyond." In 3D Image Acquisition and Display: Technology, Perception and Applications. Optica Publishing Group, 2024. https://doi.org/10.1364/3d.2024.jw2a.2.

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Chip-scale atomic clocks and sensors combine elements of precision atomic spectroscopy, silicon micromachining and photonics technology to achieve good performance with small size and low power consumption. Recent advances will be discussed including compact optical clocks, micromachined atomic beam clocks. Full-text article not available; see video presentation
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Meziere, Jason, Anastasios Pateral, Ross Harder, and Richard L. Sandberg. "Extending Bragg Coherent Diffraction Imaging to the Atomic Scale with Physics-Based Optimization." In 3D Image Acquisition and Display: Technology, Perception and Applications. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/3d.2024.rth4g.1.

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As upgrades to synchrotrons increase coherent flux, atomic resolution in Bragg Coherent Diffraction Imaging has become a distinct possibility. We present a new method that shows the capability to achieve atomic resolution for small nanoparticles.
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Lee, Kee Woong, Hyun-Chul Kim, Bumseung Kim, and Woo-Kyung Lee. "Design of SAR image feature detector for small-scaled coherent change detection." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7729605.

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Xie, Dongsheng, Wangyu Xue, Jiabin He, Yangyu Luo, Xinyuan Wang, and Zuoqin Yan. "A Small-Scaled Intraoperative 3D Visualization Navigation System for Femoral Head Repair Surgery." In 2021 6th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2021. http://dx.doi.org/10.1109/icivc52351.2021.9526993.

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Reports on the topic "Small scaled image"

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Alevizos, E., T. Schoenning, K. Köser, M. Snellen, and J. Greinert. Merging AUV-based multibeam and image data to map the small-scale heterogeneity of Mn-nodule distribution. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305404.

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Hammouti, A., S. Larmagnat, C. Rivard, and D. Pham Van Bang. Use of CT-scan images to build geomaterial 3D pore network representation in preparation for numerical simulations of fluid flow and heat transfer, Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331502.

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Non-intrusive techniques such as medical CT-Scan or micro-CT allow the definition of 3D connected pore networks in porous materials, such as sedimentary rocks or concrete. The definition of these networks is a key step towards the evaluation of fluid flow and heat transfer in energy resource (e.g., hydrocarbon and geothermal reservoirs) and CO2 sequestration research projects. As material heterogeneities play a role at all scales (from micro- to project-scale), numerical models represent a powerful tool for bridging the gap between small-scale measurements provided by X-ray imaging techniques and larger-scale transport properties. This study uses pre-existing medical CT-scan datasets of reference material, namely glass beads and conventional reservoir rocks (Berea sandstone, Boise sandstone, Indiana limestone) to extract the 3D geometry of connected pores using an open-source software (Spam). Pore networks from rock samples were generated from dry and then saturated samples. Binarized datasets were produced for these materials (generated by a thresholding technique) to obtain pore size distribution and tortuosity, as well as preferential paths for fluid flow. Average porosities were also calculated for comparison with those obtained by conventional commercial laboratory techniques. The results obtained show that this approach works well for medium and coarse-grained materials that do not contain a large percentage of fine particles. However, this approach does not allow representative networks to be obtained for fine-grained rocks, due to the fact that small pores (or pore throats) cannot be taken into account in the datasets obtained from the medical CT-Scan. A next step, using datasets produced from a micro- CT scan, is planned in order to be able to generate representative networks in this type of material as well.
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Weeks, Timothy "Dash." DTPH56-13-X-000013 Modern High-Toughness Steels for Fracture Propagation and Arrest Assessment-Phase II. Pipeline Research Council International, Inc. (PRCI), 2018. http://dx.doi.org/10.55274/r0012037.

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NIST work developed processes to identify the stress/strain/crack velocity conditions for unstable high-rate ductile crack propagation found in a full-scale pipeline burst test and duplicate those conditions in a medium-scale test. With modeling to validate conditions and assumptions used in reducing the scale of the tests. A medium-scale test to elucidate material property data necessary to qualify high-strength high-toughness steels based on the correlation to large-scale tests. Parametric determination of the material properties governing fracture propagation or arrest-ability was developed. This will assist researchers to determine a relevant and effective small-scale test (or tests) that provides enough information for material selection, design, reliability, as well as integrity and risk assessment. Pipe evaluated includes API5L X70 and X80 pipe. The strain was measured by a three-dimensional digital image correlation system. This project takes a phased approach with complementary research in successive phases beginning with a road map to systematically fill gaps in knowledge and understanding of the problem of unstable high-rate ductile running failures in pipelines. This report is structured to highlight the problem statement with respect to the current state of the art understanding, define knowledge gaps and present the plan, and progress toward meeting the objective. The following sections specifically cover the effort to develop and inform a constitutive material model necessary for the structural model of the medium-scale test. The material testing required to inform the constitutive material model is presented. Conclusions of this phase of the project are also presented in addition to the proposed work in Phase III of the project.
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Aleksandrov, Pavlo. NEWS GAMES IN THE UKRAINIAN MEDIA SPACE DURING THE FULL-SCALE RUSSIAN INVASION. Ivan Franko National University of Lviv, 2024. http://dx.doi.org/10.30970/vjo.2024.54-55.12140.

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The phenomenon of news games on the topic of the Russian-Ukrainian war of 2022-2023 has been explored in the article. During the research, a number of gaming projects from this period were analyzed, their genre and thematic specificity were determined, examples of gaming products were provided, and our own concept of a news game on the topical subject of wartime was presented. In 2022-2023, many game projects on the theme of the war in Ukraine appeared, which can be fully or partially classified as news games, conditionally dividing them into “civilian” ones, where the game character is a volunteer, an immigrant, a peaceful resident of the occupied territory, etc. and “combat”, in which the character is a Ukrainian soldier or combat unit. These games are primarily developed by gaming studios or individual game developers, rather than journalistic editorial teams, and they target an international audience (almost all the analyzed games have an English version). We categorize these news games as “entertainment” (those primarily oriented towards humor, boosting morale, and using current information or media images) and “serious” (those attempting to explain, reveal the essence, and show the war through the eyes of witnesses). According to the level of technical implementation, these games can be divided into “simple” ones (browser-based, requiring no download or payment) and “complex” ones that offer extended gameplay and are available only through subscription. Almost all gaming projects encourage donations to the Armed Forces of Ukraine and charitable funds, and the authors of paid games always emphasize that a portion of the proceeds will go towards supporting Ukrainian military personnel. Despite their significant potential, news games currently occupy a small niche in the Ukrainian media landscape. At the same time, in our opinion, the creative possibilities offered by the gaming mechanism of this interactive narrative are quite promising for explaining and revealing various socially important topics related to the Russian-Ukrainian war. Keywords: gamification; news games; game format; game research.
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Rahman, Shahedur, Rodrigo Salgado, Monica Prezzi, and Peter J. Becker. Improvement of Stiffness and Strength of Backfill Soils Through Optimization of Compaction Procedures and Specifications. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317134.

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Vibration compaction is the most effective way of compacting coarse-grained materials. The effects of vibration frequency and amplitude on the compaction density of different backfill materials commonly used by INDOT (No. 4 natural sand, No. 24 stone sand, and No. 5, No. 8, No. 43 aggregates) were studied in this research. The test materials were characterized based on the particle sizes and morphology parameters using digital image analysis technique. Small-scale laboratory compaction tests were carried out with variable frequency and amplitude of vibrations using vibratory hammer and vibratory table. The results show an increase in density with the increase in amplitude and frequency of vibration. However, the increase in density with the increase in amplitude of vibration is more pronounced for the coarse aggregates than for the sands. A comparison of the maximum dry densities of different test materials shows that the dry densities obtained after compaction using the vibratory hammer are greater than those obtained after compaction using the vibratory table when both tools were used at the highest amplitude and frequency of vibration available. Large-scale vibratory roller compaction tests were performed in the field for No. 30 backfill soil to observe the effect of vibration frequency and number of passes on the compaction density. Accelerometer sensors were attached to the roller drum (Caterpillar, model CS56B) to measure the frequency of vibration for the two different vibration settings available to the roller. For this roller and soil tested, the results show that the higher vibration setting is more effective. Direct shear tests and direct interface shear tests were performed to study the impact of particle characteristics of the coarse-grained backfill materials on interface shear resistance. The more angular the particles, the greater the shear resistance measured in the direct shear tests. A unique relationship was found between the normalized surface roughness and the ratio of critical-state interface friction angle between sand-gravel mixture with steel to the internal critical-state friction angle of the sand-gravel mixture.
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King, E. L., A. Normandeau, T. Carson, et al. Pockmarks, a paleo fluid efflux event, glacial meltwater channels, sponge colonies, and trawling impacts in Emerald Basin, Scotian Shelf: autonomous underwater vehicle surveys, William Kennedy 2022011 cruise report. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331174.

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A short but productive cruise aboard RV William Kennedy tested various new field equipment near Halifax (port of departure and return) but also in areas that could also benefit science understanding. The GSC-A Gavia Autonomous Underwater Vehicle equipped with bathymetric, sidescan and sub-bottom profiler was successfully deployed for the first time on Scotian Shelf science targets. It surveyed three small areas: two across known benthic sponge, Vazella (Russian Hat) within a DFO-directed trawling closure area on the SE flank of Sambro Bank, bordering Emerald Basin, and one across known pockmarks, eroded cone-shaped depression in soft mud due to fluid efflux. The sponge study sites (~ 150 170 m water depth) were known to lie in an area of till (subglacial diamict) exposure at the seabed. The AUV data identified gravel and cobble-rich seabed, registering individual clasts at 35 cm gridded resolution. A subtle variation in seabed texture is recognized in sidescan images, from cobble-rich on ridge crests and flanks, to limited mud-rich sediment in intervening troughs. Correlation between seabed topography and texture with the (previously collected) Vazella distribution along two transects is not straightforward. However there may be a preference for the sponge in the depressions, some of which have a thin but possibly ephemeral sediment cover. Both sponge study sites depict a hereto unknown morphology, carved in glacial deposits, consisting of a series of discontinuous ridges interpreted to be generated by erosion in multiple, continuous, meandering and cross-cutting channels. The morphology is identical to glacial Nye, or mp;lt;"N-mp;lt;"channels, cut by sub-glacial meltwater. However their scale (10 to 100 times mp;lt;"typicalmp;gt;" N-channels) and the unique eroded medium, (till rather than bedrock), presents a rare or unknown size and medium and suggests a continuum in sub-glacial meltwater channels between much larger tunnel valleys, common to the eastward, and the bedrock forms. A comparison is made with coastal Nova Scotia forms in bedrock. The Emerald Basin AUV site, targeting pockmarks was in ~260 to 270 m water depth and imaged eight large and one small pockmark. The main aim was to investigate possible recent or continuous fluid flux activity in light of ocean acidification or greenhouse gas contribution; most accounts to date suggested inactivity. While a lack of common attributes marking activity is confirmed, creep or rotational flank failure is recognized, as is a depletion of buried diffuse methane immediately below the seabed features. Discovery of a second, buried, pockmark horizon, with smaller but more numerous erosive cones and no spatial correlation to the buried diffuse gas or the seabed pockmarks, indicates a paleo-event of fluid or gas efflux; general timing and possible mechanisms are suggested. The basinal survey also registered numerous otter board trawl marks cutting the surficial mud from past fishing activity. The AUV data present a unique dataset for follow-up quantification of the disturbance. Recent realization that this may play a significant role in ocean acidification on a global scale can benefit from such disturbance quantification. The new pole-mounted sub-bottom profiler collected high quality data, enabling correlation of recently recognized till ridges exposed at the seabed as they become buried across the flank and base of the basin. These, along with the Nye channels, will help reconstruct glacial behavior and flow patterns which to date are only vaguely documented. Several cores provide the potential for stratigraphic dating of key horizons and will augment Holocene environmental history investigations by a Dalhousie University student. In summary, several unique features have been identified, providing sufficient field data for further compilation, analysis and follow-up publications.
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Cotten, David, Brandon Adams, Nancy O'Hare, et al. Vegetation mapping at Horseshoe Bend National Military Park: Photointerpretation key and final vegetation map. National Park Service, 2019. https://doi.org/10.36967/2267065.

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The University of Georgia Department of Geography’s Center for Geospatial Research (CGR), with the support of the National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program, described and mapped vegetation at Horseshoe Bend National Military Park (HOBE). This mapping effort was accomplished through collaboration with the NPS Southeast Coast Network (SECN), the North Carolina office of NatureServe (/Durham, N.C.), and Atkins North America, Inc. A final map of vegetation communities was created for Horseshoe Bend National Military Park (NMP) to the association level of the National Vegetation Classification System and in a 500-meter (1,640-foot [ft]) buffer zone around the park using the more general Anderson Level II classification. This map represents the vegetation found in the park during 2011, the year the images were acquired. We were provided with the vegetation communities occurring in the park, as determined by NatureServe from ground plots. We overlaid the location of the NatureServe plots on leaf-on color-infrared aerial photographs to determine the image signature of vegetation communities in terms of color, tone, texture, and topographic position. We also conducted our own field surveys to refine and verify photointerpretation. The park encompasses 829 hectares (2,049 acres [ac]) while the buffer alone covers 712 hectares (1,759 ac). Within the park boundary, there were 23 vegetation communities, with pine and hardwood forest communities dominating (88%). While forested, most of the forests (66% of total area) are mid-successional forests 30 to 75 years of age, reflecting past anthropogenic influences. The most common vegetation class is Early-to Mid-Successional Loblolly Pine Forest (24%). Areas impacted by exotic invasives or pine bark beetles were minimal (less than 1%). The buffer area (712 hectares [1,759 ac]) was 81% forested. There is a small component of rangeland (12%), which depending upon location and actual use, may influence water quality. Less than 3% of the buffer zone is high intensity anthropogenic land-uses. Because of the extensive past land use history, using the National Vegetation Classification System (NVCS) to the association level was challenging. Determining CEGL codes was particularly difficult because the National Vegetation Classification System was designed for relatively undisturbed vegetation communities. Using 16 NatureServe vegetation plots, color-infrared aerial photos, and data collected in the field, vegetation communities were delineated and assigned attributes. Using dominant vegetation classes and secondary vegetation classes, as well as modifiers to describe the diversity of species, detailed vegetation maps were created for Horseshoe Bend NMP. Polygons and attributes within the 500-meter (1,640-ft) buffer were created on a broader scale using a system based on the Anderson Level II classification scheme which includes anthropogenic and land use/land cover (LULC) classes. Within the park, the more detailed vegetation classes were used. The most common class found in Horseshoe Bend NMP is Early- to Mid-Successional Loblolly Pine Forest (6011) covering 24% of the park. This class combined with the second and third most common classes, Successional Sweetgum Floodplain Forest and Mid- to Late-Successional Loblolly Pine - Sweetgum Forest respectively, covers 57% of the park's 829 hectares (2,049 ac). The smallest class in the park that is larger than one half hectare is Highland Rim Pond (Woolgrass Bulrush—Threeway Sedge Type; 4719) which covers roughly one hectare. A rigorous accuracy assessment was conducted on the 23 map classes within the boundary of Horseshoe Bend NHP representing floristic types within the National Vegetation Classification System. Results showed 69% map accuracy and a kappa rating of 66% using 167 accuracy assessment points. The products generated from this project include vegetation maps, a photointerpretation key,..........
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Thompson, Anna, Michael Loso, Sydney Mooneyham, Brandon Tober, Christopher Larsen, and John Holt. Surficial geology and proglacial lake change at S?t? Tlein (Malaspina Glacier), Wrangell-St. Elias National Park and Preserve, Alaska. National Park Service, 2024. http://dx.doi.org/10.36967/2301689.

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S?t? Tlein (Tlingit for ?Big Glacier?) is the traditional name for what has recently been called Malaspina Glacier, the largest glacier in Alaska. The piedmont terminal lobe of S?t? Tlein is protected from the adjacent Pacific Ocean by a narrow, vegetated foreland dotted with proglacial lakes. Ice of the piedmont lobe is largely covered with debris and vegetation. These lakes and sedimentary deposits impact rates of melt and calving and therefore impact ongoing evolution of the glacier itself. To document these features, we present 1) a new surficial geology map for the foreland and piedmont lobe of S?t? Tlein (an area of 3477 km2) at a scale of 1:24,000, and 2) a detailed time-series of proglacial lake extents. The surficial geology is referenced to a 2012 IFSAR Digital Terrain Model with 5-m resolution, supplemented with additional satellite images, maps, and digital elevation models. We visited the foreland in 2021 to ground-truth portions of the mapped area. Lake outlines were digitized from Landsat imagery, focusing on lakes adjacent to the central ?Seward Lobe? of S?t? Tlein. A majority of the mapping area is occupied by glacier ice, a sizable fraction of which is covered by supraglacial debris of varying thicknesses. Off glacier, in the foreland, glacial outwash is the most common mapping unit, followed by moraines of varying ages and finally by marine beaches, bars, and lagoons. Perhaps surprisingly, given significant changes in the glacier itself over the last half-century, these deposits have not changed dramatically since a similar map was produced by Plafker and Miller in 1958. The most significant changes we found are related to lake development. Other than Malaspina Lake, the largest and most persistent lake in the foreland, proglacial lakes were uncommon in the foreland in 1958. Our mapping shows that lake numbers on the Seward Lobe increased from 5 to more than 200 between 1972 and 2020. Most of the new thermokarst lakes are small, compared to Malaspina Lake, but may be having strong impacts on the future evolution of S?t? Tlein. One of these new lakes, Sitkagi Lagoon, is ice-walled and receives input from the Pacific Ocean, portending the possible initiation of catastrophic tidewater glacier retreat.
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Ampatzidis, Yiannis, Mahendra Bhandari, Andres Ferreyra, et al. AI in Agriculture: Opportunities, Challenges, and Recommendations. Chair Alex Thomasson. Council for Agricultural Science and Technology, 2025. https://doi.org/10.62300/iaag042514.

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Artificial Intelligence (AI) is rapidly being integrated into people’s lives, reshaping industries, and enabling previously unimagined innovation, even in agriculture. Generative AI focuses on creating content like text and pictures based on vast quantities of data. ExtensionBot is a generative AI platform that supports agricultural extension by providing farmers with accurate scientific information and specific recommendations. It has been shown to deliver more accurate responses to agricultural questions than broader generative AI models. Other forms of AI have been used to analyze data to provide support for management decisions, such as in livestock monitoring, food traceability, genetic studies, and predicting weather and crop yield. Furthermore, AI is particularly adept at image analysis and can identify insects, weeds, and diseases. It has also been used to detect the quality of produce and allow machines to perceive the precise location of fruits for robotic picking. Many successful AI examples exist in agriculture, but numerous challenges prevent rapid development. These include the common incompatibility of agricultural data, the wide variability in agriculture that restricts the broad applicability of AI models, the common lack of connectivity in rural and agricultural areas, concerns about the privacy of agricultural data, the resistance to change in the agricultural industry, the lack of an AI-skilled workforce, and high adoption costs for AI technologies. Furthermore, there is fear about how AI will affect the agricultural community’s ability to maintain human knowledge and skill in agriculture. Cybersecurity is another concern, particularly as autonomous machines begin to emerge, facilitated by AI. If robots perform agricultural tasks, what happens when they are hacked or fail, and a human is not available to solve the immediate problem? Additionally, the advancement of AI in agriculture affects humans in multiple ways. First, it affects the work that agricultural workers perform and how that work is done. Ideally, workers will have input into the design of AI tools to ensure these tools improve their efficiency and safety in daily tasks as well as their overall work experience. Consumers of agricultural products also have a stake in AI for agriculture, as it can improve food safety, nutrition, and health. There are also particular ethical concerns about the advancement of AI in agriculture. For example, the data aggregation of numerous farms can have disadvantages for small and low-income farms. More research is needed to develop AI for agriculture in ways that are mindful of the many challenges. If resources for research on AI in agriculture are unavailable, innovation will be reduced, and collaboration will be hindered in its development. Researchers may end up competing for tightly limited funds rather than sharing knowledge. For the U.S. to lead the world in developing AI for agriculture, it must promote innovation, industry competition, interdisciplinary collaboration, and appropriate standards to ensure big data and AI are used responsibly and contribute to efficient and resilient agriculture and food systems. We recommend that policymakers focus on AI in agriculture to create an enabling environment for its development, to ensure adequate resources are available for research, to facilitate opportunities for workforce development, to enable guidelines leading to its adoption, to foster a regulatory framework that protects agricultural data, to ensure wide-ranging benefits to various scales and income levels of farms, to provide for cybersecurity, and to promote the development of standards to ensure AI systems in agriculture are safe, efficient, reliable, and ethical. AI has immense potential to enable the next step change in agriculture, and initiatives should be formed to position the U.S. as the global leader in agricultural AI, driving economic growth, ensuring food security and food safety, and promoting ethical practices that lead to environmental stewardship.
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