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Found 30 papers, 6 papers with code
Data-Centric Machine Learning for Geospatial Remote Sensing Data
These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches.
Data-Centric Digital Agriculture: A Perspective
In response to the increasing global demand for food, feed, fiber, and fuel, digital agriculture is rapidly evolving to meet these demands while reducing environmental impact.
Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks
A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment.
Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery
Our proposed framework produces more precise attribution maps pinpointing the designating patterns forming the natural authenticity of protected areas.
Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness
This framework combines explainable machine learning and uncertainty quantification to assess and explain naturalness.
Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower
Cauliflower is a hand-harvested crop that must fulfill high-quality standards in sales making the timing of harvest important.
Location-aware Adaptive Normalization: A Deep Learning Approach For Wildfire Danger Forecasting
Climate change is expected to intensify and increase extreme events in the weather cycle.
MapInWild: A Remote Sensing Dataset to Address the Question What Makes Nature Wild
Antrophonegic pressure (i. e. human influence) on the environment is one of the largest causes of the loss of biological diversity.
Exploring Self-Attention for Crop-type Classification Explainability
Using explainable machine learning to reveal the inner workings of these models is an important step towards improving stakeholders' trust and efficient agriculture monitoring.
GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER
The dataset contains collected phenotypic traits of 740 plants, including the developmental stage as well as plant and cauliflower size.
Exploring Wilderness Characteristics Using Explainable Machine Learning in Satellite Imagery
Occluding certain activations in an interpretable artificial neural network we complete a comprehensive sensitivity analysis regarding wild and anthropogenic characteristics.
Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision
Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.
A Survey of Uncertainty in Deep Neural Networks
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks
In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest.
Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks
Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting.
Towards a Collective Agenda on AI for Earth Science Data Analysis
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer.
Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection
One of the most prominent applications is the detection of vehicles, for which deep learning approaches are increasingly used.
Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks
The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems.
Explainable Machine Learning for Scientific Insights and Discoveries
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data.
Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks
We count single berries in images to avoid the error-prone detection of grapevine clusters.
Ocean Eddy Identification and Tracking using Neural Networks
We show the detection and tracking results on sea level anomalies (SLA) data from the area of Australia and the East Australia current, and compare our two eddy detection and tracking approaches to identify the most robust and objective method.
Archetypal Analysis for Sparse Representation-based Hyperspectral Sub-pixel Quantification
In our experiments, the estimation of the automatically derived elementary spectra is compared to the estimation obtained by a manually designed spectral library by means of reconstruction error, mean absolute error of the fraction estimates, sum of fractions, $R^2$, and the number of used elementary spectra.
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields
In the present study an automated image analyzing framework was developed in order to estimate the size of grapevine berries from images in a high-throughput manner.
Deep Self-taught Learning for Remote Sensing Image Classification
This paper addresses the land cover classification task for remote sensing images by deep self-taught learning.
Sea Level Anomaly Prediction using Recurrent Neural Networks
We develop a combination of a convolutional neural network (CNN) and a recurrent neural network (RNN) to ana-lyse both the spatial and the temporal evolution of sea level and to suggest an independent, accurate method to predict interannual sea level anomalies (SLA).
Tropical Land Use Land Cover Mapping in Pará (Brazil) using Discriminative Markov Random Fields and Multi-temporal TerraSAR-X Data
Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information.
STAR: Spatio-Temporal Altimeter Waveform Retracking using Sparse Representation and Conditional Random Fields
We show that STAR enables the derivation of sea surface heights over the open ocean as well as over coastal regions of at least the same quality as compared to existing retracking methods, but for a larger number of cycles and thus retaining more useful data.
Incremental Import Vector Machines for Classifying Hyperspectral Data
The performance of the IVM in comparison to support vector machines (SVM) is evaluated in terms of accuracy and experiments are conducted to assess the potential of the probabilistic outputs of the IVM.
Shapelet-based Sparse Representation for Landcover Classification of Hyperspectral Images
A combination of shapelets and spectral information are represented in an undercomplete spatial-spectral dictionary for each individual patch, where the elements of the dictionary are linearly combined to a sparse representation of the patch.
Classification Of Hyperspectral Images
General Classification
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Statistical Inference, Learning and Models in Big Data
The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context.
