Deep learning has shown promising performance in submeter-level mapping tasks; however, the annotation cost of submeter-level imagery remains a challenge, especially when applied on a large scale. In this paper, we present the first submeter-level land cover mapping of Japan with eight classes, at a relatively low annotation cost. We introduce a human-in-the-loop deep learning framework leveraging OpenEarthMap, a recently introduced benchmark dataset for global submeter-level land cover mapping, with a U-Net model that achieves national-scale mapping with a small amount of additional labeled data. By adding a small amount of labeled data of areas or regions where a U-Net model trained on OpenEarthMap clearly failed and retraining the model, an overall accuracy of 80\% was achieved, which is a nearly 16 percentage point improvement after retraining. Using aerial imagery provided by the Geospatial Information Authority of Japan, we create land cover classification maps of eight classes for the entire country of Japan. Our framework, with its low annotation cost and high-accuracy mapping results, demonstrates the potential to contribute to the automatic updating of national-scale land cover mapping using submeter-level optical remote sensing data. The mapping results will be made publicly available.

Text classification is the task of assigning a sentence or document an appropriate category. The categories depend on the selected dataset and can cover arbitrary subjects. Therefore, text classifiers can be used to organize, structure, and categorize any kind of text. Common approaches use supervised learning to classify texts. Especially BERT-based language models achieved very good text classification results in recent years.

There are several important steps involved in setting up an AI workflow. The first step is to define the input data into the system. For example, one might specify that the input images to be classified are rectangular sub-images of a certain size derived from an overlapping grid applied to a whole slide image (WSI). Second, input data should be divided into training data (used to train the classifier) and test data (used to evaluate the classifier.) Third, both training data and test data should be annotated by an expert, to establish a ground truth classification category for each input image.

Abstract-- In this paper we presented a novel constructive approach for training deep neural networks using geometric approaches. We show that a topological covering can be used to define a class of distributed linear matrix inequalities, which in turn directly specify the shape and depth of a neural network architecture. The key insight is a fundamental relationship between linear matrix inequalities and their ability to bound the shape of data, and the rectified linear unit (ReLU) activation function employed in modern neural networks. We show that unit cover geometry and cover porosity are two design variables in cover-constructive learning that play a critical role in defining the complexity of the model and generalizability of the resulting neural network classifier. In the context of cover-constructive learning, these findings underscore the age old tradeoff between modelcomplexity and overfitting (as quantified by the number of elements in the data cover) and generalizability on test data. Finally, we benchmark on algorithm on the Iris, MNIST, and Wine dataset and show that the constructive algorithm is able to train a deep neural network classifier in one shot, achieving equal or superior levels of training and test classification accuracy with reduced training time. I. INTRODUCTION Artificial neural networks have proven themselves to be useful, highly flexible tools for addressing many complex problems where first-principles solutions are infeasible, impractical, orundesirable.

There are several important steps involved in setting up an AI workflow. The first step is to define the input data into the system. For example, one might specify that the input images to be classified are rectangular sub-images of a certain size derived from an overlapping grid applied to a whole slide image (WSI). Second, input data should be divided into training data (used to train the classifier) and test data (used to evaluate the classifier.) Third, both training data and test data should be annotated by an expert, to establish a ground truth classification category for each input image.