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23 results for “deep convolutional neural network”
Semantic Segmentation of Time Series Imagery Using Deep Convolutional Neural Networks: A Case Study of Sandbars in Grand Canyon
<p>This dataset contains imagery used to train and test Deep Convolutional Neural Networks for the purpose of binary semantic segmentation of a time series of oblique imagery capturing sandbar monitoring sites in The Grand Canyon. In addition the scripts needed for removing image distortion, registering, rectifying, and labeling imagery is present. </p>
Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
<p>This is a Fossil Image Dataset, which contains >415000 images. A total of 50 clades were labeled, with a final 90% accuracy. We used the web crawler to download fossil images from the Internet. We declare that all the collected images are used for academic purposes only. If anyone wants to use this dataset, please agree on the Terms of access for the Fossil Image Dataset (FID). We uploaded two datasets: FID (contains 0.415 million images) and reduced-FID (60 thousand images, 1200 for each clade). Requirements of necessary preinstalled Python libraries, algorithms for analysis, and the model weights are available at <a href="https://github.com/XiaokangLiuCUG/Fossil_Image_Dataset">https://github.com/XiaokangLiuCUG/Fossil_Image_Dataset</a>.</p>
A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification (dataset)
<p>This is a dataset which has been designed to be used for flood time series classification. Each time series is annotated as flood or no-flood and represents a pixel-wise time series derived from stack of Sentinel-1 IW GRD images that have been pre-processed according to <a href="http://doi.org/10.5281/zenodo.6510223">https://doi.org/10.5281/zenodo.6510223</a>.</p>
Figure 1. CNN architecture (adopted from Krizhevsky et al. '12)-Measuring Customer Behavior with Deep Convolutional Neural Networks
<p>The architecture of a CNN can be described as following. A small pixel region goes to input neurons and then connects to a first convolution hidden layer (Figure1). There we can see a set of learnable filters, which are activated during the presentation some particular type of feature in pixel region in the input. On this phase, CNN does shift invariance, which is carried by feature map. Subsampling layer goes next. There we have two processes: local averaging and sampling. As a result, we get declining resolution of feature map. To correspond this task CNN needs supervised learning. Before starting the experiment, we gave a set of labeled videos with different emotional experience. The system analyses images and finds similar features. Then the system creates a map, where it arranges videos in accordance with similar features. Thereby, images with similar emotions form certain class. To test the system, we add other videos and correct the system when it refers them improperly. The proposed model consists of four convolutional layers, followed by max-pooling layers, and three fully-connected layers with a final classificatory presented with MLP (with six basic outputs, corresponding to basic emotions for emotion classification and two outputs for motion classification for typical and non-typical behavior). The input data was presented as infrared camera output.</p>
Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network
<p>Short Description:</p> <p>In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone.</p> <p>Code and More Details: https://github.com/lepotatoguy/aqi</p>
Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations - data
<p>Data from the paper:<em> Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.</em></p> <p>Preprint: https://www.biorxiv.org/content/10.1101/840256v1</p> <p>Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> September 2020</p> <p> </p>
Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs
<p class="MsoNormal"><strong>1. </strong>Tropical forests are subject to diverse deforestation pressures while their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy.</p> <p class="MsoNormal"><strong>2. </strong>Here, we demonstrate the ability of deep convolutional neural networks (CNNs) to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of annual forest change (2001-2020) and the ALOS PALSAR digital surface model, to forecast deforestation (2021). We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short-Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km<sup>2</sup> tropical forest regions in the Southern Peruvian Amazon.</p> <p class="MsoNormal"><strong>3.</strong><strong> </strong><span>The networks could predict the location of future forest loss to a high degree of accuracy (F</span><sub>1 </sub><span>= 0.58-0.71). Our best performing model (3D CNN) had the highest pixel-wise accuracy (F</span><sub>1 </sub><span>= 0.71) when validated on 2020 forest loss (2014-2019 training). Visual interpretation of the mapped forecasts indicated that the network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides).</span></p> <p class="MsoNormal"><strong>4.</strong><strong> </strong>CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in harnessing the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.</p>
Deep convolutional neural network for owl vocal identification
<p>This repository contains all the code and data necessary to replicate the results presented in Ruff et al. 2019, "Automated identification of avian vocalizations with deep convolutional neural networks", and is published in support of that manuscript. The folder includes several Python scripts, our trained convolutional neural network (CNN), and a set of 164,210 spectrogram images that were reviewed to generate CNN performance metrics. We include the CNN's predicted class scores for the test images as well as the set of labels assigned to the same images by experienced human technicians. The published article can be found here: <a href="https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.125">https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.125</a></p> <p>As presented, the CNN is designed to accept grayscale PNG images at 500x129 resolution and will generate a set of seven class scores for each image. Class scores are the softmax activation from the final (seven unit) fully-connected layer of the CNN. Scores are bounded between 0 and 1 and sum to 1 for each image. This means target classes are implicitly treated as mutually exclusive (i.e., each image belongs to exactly one class), although in reality some images contain calls from >1 target species.</p> <p>The different scripts and their functions are as follows:<br> - Code used to construct and train the CNN is in Owl_CNN_train_model.py<br> - Code to generate spectrograms with randomized parameters based on tagged calls in audio files is in Owl_CNN_generate_training_data.py<br> - Code to generate random spectrograms from a set of audio files (used to generate training data for the Noise class) can be generated with Owl_CNN_make_noise_data.py<br> - Code used to process raw audio files, including segmenting them into 12 s clips, generating spectrograms, and generating class scores using a pre-trained CNN is in Owl_CNN_process_audio.py<br> - Code to generate class scores for an existing set of spectrogram images using a pre-trained CNN are in Owl_CNN_process_images.py</p> <p>Our seven target classes are as follows:<br> AEAC - Northern saw-whet owl, Aegolius acadicus.<br> BUVI - Great horned owl, Bubo virginianus.<br> GLGN - Northern pygmy-owl, Glaucidium gnoma.<br> MEKE - Western screech-owl, Megascops kennicottii.<br> STOC - (Northern) spotted owl, Strix occidentalis caurina.<br> STVA - Barred owl, Strix varia.<br> Noise - Catch-all for any clip that did not contain vocalizations of at least one of the six owl species listed above.</p> <p>The CNN was trained for 100 epochs and saved only after epochs in which validation loss improved. Loss was measured as categorical cross-entropy. The CNN was last saved at epoch 97 with reported metrics:<br> Training loss = 0.218<br> Training accuracy = 0.972<br> Validation loss = 0.165<br> Validation accuracy = 0.987</p> <p>Although this code has been tested and works on our system, we make no guarantee that it will work for others without modification. Created using Python version 2.7.14, TensorFlow version 1.2.1, Keras version 2.2, and SoX version 14.4. Code was developed by Bharath Padmaraju, Zack Ruff, and Chris Sullivan. Questions and comments may be directed to zjruff at gmail dot com.</p> <p>Zack Ruff<br> 15 July 2019</p>
Deep learning based on convolutional neural networks to classify nanobiomechanical data
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Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs
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multi class dataset_Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
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Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
<p>The rapid and accurate taxonomic identification of fossils is of great significance in paleontology, biostratigraphy, and other fields. However, taxonomic identification is often labor-intensive and tedious, and the requisition of extensive prior knowledge about a taxonomic group also requires long-term training. Moreover, identification results are often inconsistent across researchers and communities. Accordingly, in this study, we used deep learning to support taxonomic identification. We used web crawlers to collect the Fossil Image Dataset (FID) via the Internet, obtaining 415,339 images belonging to 50 fossil clades. Then we trained three powerful convolutional neural networks on a high-performance workstation. The Inception ResNet v2 architecture achieved an average accuracy of 0.90 in the test dataset when transfer learning was applied. The clades of microfossils and vertebrate fossils exhibited the highest identification accuracies of 0.95 and 0.90, respectively. In contrast, clades of sponges, bryozoans, and trace fossils with various morphologies or with few samples in the dataset exhibited a performance below 0.80. Visual explanation methods further highlighted the discrepancies among different fossil clades and suggested similarities between the identifications made by machine classifiers and taxonomists. Collecting large paleontological datasets from various sources, such as the literature, digitization of dark data, citizen-science data, and public data from the Internet may further enhance deep learning methods and their adoption. Such developments will also possibly lead to image-based systematic taxonomy to be replaced by machine-aided classification in the future. Pioneering studies can include microfossils and some invertebrate fossils. To contribute to this development, we deployed our model on a server for public access at www.ai-fossil.com.</p>
Data for: Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network [Version 2]
<p>This package provides material that can be openly published for the paper "Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network". It consists in the code used to generate results and figures as well as the weights of the deep convolutional neural networks trained to segment water in the surveillance camera images.</p>
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics [Dataset]
<p>Supplementary data for 'Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics'. </p>
Prediction of Endotracheal Tube Depth by Using Deep Convolutional Neural Networks
ClinicalTrials.gov study NCT05085743. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
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Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
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Joint identification of groundwater contamination source and heterogeneous hydrogeological parameters in LNAPL contaminated site based on deep convolutional encoder-decoder neural networks
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Automated detection, segmentation and classification of pericardial effusions on chest CT using a deep convolutional neural network
<p>Trainingsdata for chest CT pericard effusion and the finish trained nnU-Net model. </p>
Dataset used in "Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network" by Kamrava et al. in Geophysical Research Letters.
<p>Morphology of Heterogeneous Porous Media</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.